Sklansky, J.,
Recognition of Convex Blobs,
PR(2), No. 1, January 1970, pp. 3-10.
Elsevier DOI
BibRef
7001
Earlier:
TRUCI TR-69-3, July 1969.
Blob Extraction.
See also Parallel Detection of Concavities in Cellular Blobs.
See also Minimal Rectangular Partitions of Digitized Blobs.
BibRef
Minor, L.G.,
Sklansky, J.,
The Detection and Segmentation of Blobs in Infrared Images,
SMC(11), 1981, pp. 194-201.
BibRef
8100
Earlier:
PRIP81(464-469).
Segmentation, Blobs.
See also Recognition of Convex Blobs.
BibRef
Rosenberg, B.,
The Analysis of Convex Blobs,
CGIP(1), No. 2, August 1972, pp. 183-192.
Elsevier DOI
BibRef
7208
Cooper, D.B.,
Maximum Likelihood Estimation of Markov-Process Blob Boundaries
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PAMI(1), No. 4, October 1979, 372-384.
Blob Extraction.
Segmentation, Blobs.
BibRef
7910
Danker, A.J.,
Rosenfeld, A.,
Blob Detection by Relaxation,
PAMI(3), No. 1, January 1981, pp. 79-92.
BibRef
8101
And:
A2, A1 plus A3:
Dyer, C.R.[Charles R.],
Blob Extraction by Relaxation,
DARPA79(61-65).
BibRef
Hong, T.H.,
Rosenfeld, A.,
Compact Region Extraction Using Weighted Pixel Linking in a Pyramid,
PAMI(6), No. 2, March 1984, pp. 222-229.
BibRef
8403
Earlier:
Unforced Image Partitioning by Weighted Pyramid Linking,
DARPA82(72-78).
See also Segmentation and Estimation of Image Region Properties Through Cooperative Hierarchical Computation.
BibRef
Hong, T.H.,
Shneier, M.O.,
Extracting Compact Objects Using Linked Pyramids,
PAMI(6), No. 2, March 1984, pp. 229-236.
BibRef
8403
Earlier:
DARPA82(58-71).
See also Segmentation and Estimation of Image Region Properties Through Cooperative Hierarchical Computation.
BibRef
Shneier, M.O.,
Using Pyramids to Define Local Thresholds for Blob Detection,
PAMI(5), No. 3, May 1983, pp. 345-349.
BibRef
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Earlier:
DARPAN79(31-35).
Blob Extraction.
Segmentation, Blobs.
BibRef
Sher, C.A.[C. Allen],
Rosenfeld, A.[Azriel],
Detecting and Extracting Compact Textured Regions Using Pyramids,
IVC(7), No. 2, May 1989, pp. 129-134.
Elsevier DOI
Blob Extraction.
Segmentation, Blobs.
BibRef
8905
Rosenfeld, A.[Azriel],
Sher, C.A.[C. Allen],
Detection and Delineation of Compact Objects Using Intensity Pyramids,
PR(21), No. 2, 1988, pp. 147-151.
Elsevier DOI
BibRef
8800
Rewo, L.[Ludomir],
Enhancement and Detection of Convex Objects Using Regression Models,
CVGIP(25), No. 2, February 1984, pp. 257-269.
Elsevier DOI
Blob Extraction. Blob detection.
BibRef
8402
Blostein, D.[Dorothea],
Ahuja, N.[Narendra],
A Multi-scale Region Detector,
CVGIP(45), No. 1, January 1989, pp. 22-41.
Elsevier DOI
Blob Extraction.
Segmentation, Blobs.
Textures, Structural.
This is not really texture segmentation, but segmentation of
texture elements. The standard Laplacian of Gaussian is applied and
homogeneous regions are found which are composed of areas most
easily represented as disks. Some analysis of the LoG is done to
derive a means to find the disks. Different sizes are used to get
different size disks.
BibRef
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Kerekes, J.P.,
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Spectral imaging system analytical model for subpixel object detection,
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0501
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0905
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Stefanou, M.S.,
Kerekes, J.P.,
Image-Derived Prediction of Spectral Image Utility for Target Detection
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IEEE DOI
1003
BibRef
Kerekes, J.P.[John P.],
Hyperspectral remote sensing subpixel object detection performance,
AIPR11(1-4).
IEEE DOI
1204
BibRef
Tzafestas, C.S.[Costas S.],
Maragos, P.[Petros],
Shape Connectivity:
Multiscale Analysis and Application to Generalized Granulometries,
JMIV(17), No. 2, September 2002, pp. 109-129.
DOI Link
0211
BibRef
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Granulometric Size Density for Segmented Random-Disk Models,
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DOI Link
0211
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Caselles, V.[Vicent],
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Grain Filters,
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DOI Link
0211
BibRef
Lee, K.M.[Kyoung-Mi],
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0304
BibRef
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WACV00(64-70).
IEEE DOI
0010
Finding blobs.
BibRef
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IEEE Abstract.
0402
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Radial segmentation,
PRL(25), No. 12, September 2004, pp. 1337-1350.
Elsevier DOI
0409
To partition circular regions.
See also model-based approach to junction detection using radial energy, A.
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Hou, Z.X.[Zheng-Xin],
Phase based feature detector consistent with human visual system
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PRL(25), No. 10, 16 July 2004, pp. 1115-1121.
Elsevier DOI
0407
BibRef
Jiang, J.M.[Jian-Min],
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Dominant colour extraction in DCT domain,
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Elsevier DOI
0610
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0609
Damerval, C.[Christophe],
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Blob Detection With Wavelet Maxima Lines,
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IEEE DOI
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Springer DOI
1003
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Highlight on a Feature Extracted at Fine Scales:
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SSVM09(782-794).
Springer DOI
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BibRef
Matei, B.,
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Nonlinear and Nonseparable Bidimensional Multiscale Representation
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IP(24), No. 11, November 2015, pp. 4570-4580.
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1509
Approximation methods
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Wilkinson, M.H.F.[Michael H.F.],
Connected Shape-Size Pattern Spectra for Rotation and Scale-Invariant
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PAMI(29), No. 2, February 2007, pp. 272-285.
IEEE DOI
0701
BibRef
Earlier:
Connected rotation-invariant size-shape granulometries,
ICPR04(I: 688-691).
IEEE DOI
0409
BibRef
Urbach, E.R.[Erik R.],
Intelligent Object Detection Using Trees,
ISMM15(289-300).
Springer DOI
1506
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A Comparison of Spatial Pattern Spectra,
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Springer DOI
0908
BibRef
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IEEE DOI
0211
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IEEE DOI
0711
In hyperspectral imagery analysis. Model background using physics
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Compare to AMSD and ACE.
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DOI Link
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Genetic programming to generate code applied in windows across the image
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Gutierrez, J.A.[José A.],
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Gao, D.S.[Da-Shan],
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IEEE DOI
0904
BibRef
Earlier: A1, A3, Only:
Bottom-up saliency is a discriminant process,
ICCV07(1-6).
IEEE DOI
0710
BibRef
Earlier: A1, A3, Only:
Discriminant Interest Points are Stable,
CVPR07(1-6).
IEEE DOI
0706
Related to infomax, inference by detection of suspicious coincidences,
classification with minimal uncertainty, and classification with
minimum probability of error.
Apply to localize objects in clutter.
BibRef
Han, S.H.[Sun-Hyoung],
Vasconcelos, N.M.[Nuno M.],
Complex discriminant features for object classification,
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IEEE DOI
0810
BibRef
Rosin, P.L.[Paul L.],
A simple method for detecting salient regions,
PR(42), No. 11, November 2009, pp. 2363-2371.
Elsevier DOI
0907
Salience map; Importance map; Focus of attention; Distance transform
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Gopalakrishnan, V.[Viswanath],
Hu, Y.Q.[Yi-Qun],
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Salient Region Detection by Modeling Distributions of Color and
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MultMed(11), No. 5, 2009, pp. 892-905.
IEEE DOI
0907
BibRef
Gopalakrishnan, V.[Viswanath],
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Rajan, D.[Deepu],
Random Walks on Graphs for Salient Object Detection in Images,
IP(19), No. 12, December 2010, pp. 3232-3242.
IEEE DOI
1011
BibRef
Earlier:
Random walks on graphs to model saliency in images,
CVPR09(1698-1705).
IEEE DOI
0906
BibRef
Gopalakrishnan, V.[Viswanath],
Rajan, D.[Deepu],
Hu, Y.Q.[Yi-Qun],
A Linear Dynamical System Framework for Salient Motion Detection,
CirSysVideo(22), No. 5, May 2012, pp. 683-692.
IEEE DOI
1202
BibRef
Earlier: A1, A3, A2:
Sustained Observability for Salient Motion Detection,
ACCV10(III: 732-743).
Springer DOI
1011
BibRef
And: A1, A3, A2:
Unsupervised Feature Selection for Salient Object Detection,
ACCV10(II: 15-26).
Springer DOI
1011
BibRef
Tuytelaars, T.[Tinne],
Lampert, C.H.[Christoph H.],
Blaschko, M.B.[Matthew B.],
Buntine, W.[Wray],
Unsupervised Object Discovery: A Comparison,
IJCV(88), No. 2, June 2010, pp. xx-yy.
Springer DOI
1003
BibRef
Sharmanska, V.[Viktoriia],
Quadrianto, N.[Novi],
Lampert, C.H.[Christoph H.],
Augmented Attribute Representations,
ECCV12(V: 242-255).
Springer DOI
1210
BibRef
Ozdemir, B.[Bahadir],
Aksoy, S.[Selim],
Eckert, S.[Sandra],
Pesaresi, M.[Martino],
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Performance measures for object detection evaluation,
PRL(31), No. 10, 15 July 2010, pp. 1128-1137.
Elsevier DOI
1008
Performance evaluation; Object detection; Object matching; Shape
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BibRef
Chen, J.[Jie],
Shan, S.G.[Shi-Guang],
He, C.[Chu],
Zhao, G.Y.[Guo-Ying],
Pietikainen, M.,
Chen, X.L.[Xi-Lin],
Gao, W.[Wen],
WLD: A Robust Local Image Descriptor,
PAMI(32), No. 9, September 2010, pp. 1705-1720.
IEEE DOI
1008
Weber Local Descriptor (human perception depends not only on the
change, but the initial level).
WLD: differential excitation and orientation.
Apply to variety of feature detections.
BibRef
Matsumoto, M.[Mitsuharu],
Self-Quotient epsilon-Filter for Feature Extraction from
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IEICE(E93-D), No. 11, November 2010, pp. 3066-3075.
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1011
BibRef
Gu, Y.F.[Yan-Feng],
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Zhang, Y.[Ye],
Kernel-based regularized-angle spectral matching for target detection
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PRL(32), No. 2, 15 January 2011, pp. 114-119.
Elsevier DOI
1101
Hyperspectral imagery; Target detection; Spectral matched filter;
Spectral angle mapper; Kernel methods
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Khachaturov, G.[Georgii],
A scalable, high-precision, and low-noise detector of shift-invariant
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PRL(32), No. 2, 15 January 2011, pp. 145-152.
Elsevier DOI
1101
Feature detection; Shift invariance; Multi-scale processing;
Image-to-data structures processing
BibRef
Lemaitre, C.,
Perdoch, M.,
Rahmoune, A.,
Matas, J.G.,
Miteran, J.,
Detection and matching of curvilinear structures,
PR(44), No. 7, July 2011, pp. 1514-1527.
Elsevier DOI
1103
Curvilinear structures; Wiry objects; Descriptor; Detector;
Segmentation; Matching
BibRef
Lemaitre, C.[Cédric],
Miteran, J.[Johel],
Matas, J.G.[Jiri G.],
Definition of a Model-Based Detector of Curvilinear Regions,
CAIP07(686-693).
Springer DOI
0708
BibRef
Murray, P.,
Marshall, S.,
A New Design Tool for Feature Extraction in Noisy Images Based on
Grayscale Hit-or-Miss Transforms,
IP(20), No. 7, July 2011, pp. 1938-1948.
IEEE DOI
1107
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Vedaldi, A.[Andrea],
Zisserman, A.[Andrew],
Efficient Additive Kernels via Explicit Feature Maps,
PAMI(34), No. 3, March 2012, pp. 480-492.
IEEE DOI
1201
BibRef
Earlier:
CVPR10(3539-3546).
IEEE DOI
1006
BibRef
Vempati, S.[Sreekanth],
Vedaldi, A.[Andrea],
Zisserman, A.[Andrew],
Jawahar, C.V.,
Generalized Rbf feature maps for Efficient Detection,
BMVC10(xx-yy).
HTML Version.
1009
BibRef
Vedaldi, A.[Andrea],
Zisserman, A.[Andrew],
Sparse kernel approximations for efficient classification and detection,
CVPR12(2320-2327).
IEEE DOI
1208
BibRef
Vedaldi, A.[Andrea],
Gulshan, V.[Varun],
Varma, M.[Manik],
Zisserman, A.[Andrew],
Multiple kernels for object detection,
ICCV09(606-613).
IEEE DOI
0909
See also Learning The Discriminative Power-Invariance Trade-Off.
BibRef
Chatfield, K.[Ken],
Simonyan, K.[Karen],
Vedaldi, A.[Andrea],
Zisserman, A.[Andrew],
Return of the Devil in the Details:
Delving Deep into Convolutional Nets,
BMVC14(xx-yy).
HTML Version.
1410
BibRef
Chatfield, K.[Ken],
Lempitsky, V.[Victor],
Vedaldi, A.[Andrea],
Zisserman, A.[Andrew],
The devil is in the details:
An evaluation of recent feature encoding methods,
BMVC11(xx-yy).
HTML Version.
1110
Award, BMVC, HM Poster.
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A sparse curvature-based detector of affine invariant blobs,
CVIU(116), No. 4, April 2012, pp. 524-537.
Elsevier DOI
1202
BibRef
Earlier:
A Scale Invariant Interest Point Detector for Discriminative Blob
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IbPRIA09(233-240).
Springer DOI
0906
Interest points; Scale invariant detector; Affine invariant detector;
Gaussian curvature; Gaussian fitting; Blob evolution
BibRef
Kompella, V.R.[Varun Raj],
Sturm, P.F.[Peter F.],
Collective-reward based approach for detection of semi-transparent
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CVIU(116), No. 4, April 2012, pp. 484-499.
Elsevier DOI
1202
Collective-reward; Object detection; Semi-transparency; Transparency; Glass.
Both transmission and reflection.
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Liu, S.W.[Shang-Wang],
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An Improved Hybrid Model for Automatic Salient Region Detection,
SPLetters(19), No. 4, April 2012, pp. 207-210.
IEEE DOI
1203
BibRef
Shi, R.,
Liu, Z.,
Du, H.,
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Region Diversity Maximization for Salient Object Detection,
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IEEE DOI
1203
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Kobayashi, T.[Takumi],
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Motion Recognition Using Local Auto-Correlation of Space-Time Gradients,
PRL(33), No. 9, 1 July 2012, pp. 1188-1195.
Elsevier DOI
1202
BibRef
Earlier:
Image Feature Extraction Using Gradient Local Auto-Correlations,
ECCV08(I: 346-358).
Springer DOI
0810
Motion recognition; Motion feature extraction; Space-time gradient;
Auto-correlation; Bag-of-features
See also Face Recognition System Using Local Autocorrelations and Multiscale Integration.
See also Gesture Recognition Using Auto-Regressive Coefficients of Higher-Order Local Auto-Correlation Features.
BibRef
Lakemond, R.[Ruan],
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WWW Link.
1206
BibRef
Earlier: A1, A3, A2:
Affine Adaptation of Local Image Features Using the Hessian Matrix,
AVSBS09(496-501).
IEEE DOI
0909
Blob detectors and corner features.
See also Two Stream LSTM: A Deep Fusion Framework for Human Action Recognition.
BibRef
Lakemond, R.[Ruan],
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Sridharan, S.[Sridha],
Negative Determinant of Hessian Features,
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IEEE DOI
1205
BibRef
Umakanthan, S.,
Denman, S.,
Sridharan, S.,
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Wark, T.,
Spatio Temporal Feature Evaluation for Action Recognition,
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IEEE DOI
1303
BibRef
Vidas, S.,
Lakemond, R.[Ruan],
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Fookes, C.[Clinton],
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An Exploration of Feature Detector Performance in the Thermal-Infrared
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DICTA11(217-224).
IEEE DOI
1205
BibRef
Yoo, J.C.,
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Image matching using peak signal-to-noise ratio-based occlusion
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IET-IPR(6), No. 5, 2012, pp. 483-495.
DOI Link
1210
locate objects with partial occlusions.
Compare to correlation based methods.
BibRef
Zheng, Z.[Zhong],
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Hamalainen, J.,
Tirkkonen, O.,
A Blind Time-Reversal Detector in the Presence of Channel Correlation,
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IEEE DOI
1304
BibRef
Kong, Y.[Yan],
Dong, W.M.[Wei-Ming],
Mei, X.[Xing],
Zhang, X.P.[Xiao-Peng],
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Object class detection: A survey,
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DOI Link
1311
Survey, Object Class. Object class detection, also known as category-level object detection,
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recent technical achievements.
BibRef
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Detecting parametric objects in large scenes by Monte Carlo sampling,
IJCV(106), No. 1, January 2014, pp. 57-75.
WWW Link.
1402
BibRef
Earlier:
Efficient Monte Carlo Sampler for Detecting Parametric Objects in Large
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ECCV12(III: 539-552).
Springer DOI
1210
Sampling rather than all points.
BibRef
Niitsu, Y.S.[Yasu-Shi],
Iizuka, T.[Takaaki],
Improving light marker accuracy on camera images,
SPIE(Newsroom), February 18, 2014
DOI Link
1402
A novel method determines precise boundaries of the light markers used
to find the center of a target in image processing applications.
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Yang, H.G.[Hui-Guang],
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Automatic segmentation of granular objects in images:
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PR(47), No. 6, 2014, pp. 2266-2279.
Elsevier DOI
1403
Image segmentation
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Zimmermann, K.[Karel],
Hurych, D.[David],
Svoboda, T.[Tomáš],
Non-Rigid Object Detection with Local Interleaved Sequential Alignment
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IEEE DOI
1404
BibRef
Earlier:
Exploiting Features:
Locally Interleaved Sequential Alignment for Object Detection,
ACCV12(I:446-459).
Springer DOI
1304
Computational modeling
BibRef
Cheng, G.[Gong],
Han, J.W.[Jun-Wei],
Zhou, P.C.[Pei-Cheng],
Guo, L.[Lei],
Multi-class geospatial object detection and geographic image
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Elsevier DOI
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Geospatial object detection. Find specific objects or spatial pattern.
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Cheng, G.[Gong],
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Guo, L.[Lei],
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Learning coarse-to-fine sparselets for efficient object detection and
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CVPR15(1173-1181)
IEEE DOI
1510
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Peng, X.M.[Xiao-Ming],
Combine color and shape in real-time detection of texture-less
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Real-time texture-less object detection
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Chong, N.S.[Nguan Soon],
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Visual detection in omnidirectional view sensors,
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Springer DOI
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Diebold, J.[Julia],
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The Role of Diffusion in Figure Hunt Games,
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1505
Finding waldo.
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Han, X.H.[Xian-Hua],
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High-Order Statistics of Weber Local Descriptors for Image
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Adaptation models
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Han, X.H.[Xian-Hua],
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HEp-2 Staining Pattern Recognition Using Stacked Fisher Network for
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PR(63), No. 1, 2017, pp. 542-550.
Elsevier DOI
1612
HEp-2 image representation
BibRef
Earlier:
Add A3:
Xu, G.[Gang],
MLMI15(85-93).
Springer DOI
1511
BibRef
Gao, L.[Lianru],
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PR(48), No. 5, 2015, pp. 1844-1853.
Elsevier DOI
1502
BibRef
Earlier: A1, A2, A3, Only:
CVPR11(1353-1360).
IEEE DOI
1106
Object recognition
BibRef
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Pedersoli, M.[Marco],
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CVIU(138), No. 1, 2015, pp. 92-101.
Elsevier DOI
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Object recognition
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Bagdanov, A.D.[Andrew D.],
Villanueva, J.J.[Juan J.],
Recursive Coarse-to-Fine Localization for Fast Object Detection,
ECCV10(VI: 280-293).
Springer DOI
1009
BibRef
Santosh, K.C.,
Wendling, L.[Laurent],
Antani, S.K.[Sameer K.],
Thoma, G.R.[George R.],
Overlaid Arrow Detection for Labeling Regions of Interest in
Biomedical Images,
IEEE_Int_Sys(31), No. 3, May 2016, pp. 66-75.
IEEE DOI
1606
BibRef
Earlier:
Scalable Arrow Detection in Biomedical Images,
ICPR14(3257-3262)
IEEE DOI
1412
Biomedical imaging
BibRef
Hong, J.K.[Jong-Kwang],
Hong, Y.W.[Yong-Won],
Uh, Y.J.[Young-Jung],
Byun, H.R.[Hye-Ran],
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detection in indoor scenes,
PRL(86), No. 1, 2017, pp. 56-61.
Elsevier DOI
1702
Object detection
BibRef
Wang, S.P.[Shi-Ping],
Huang, A.P.[Ai-Ping],
Salient object detection with low-rank approximation and l2,1-norm
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IVC(57), No. 1, 2017, pp. 67-77.
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Background: low rank; objects: sparse.
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An Invariant Sliding Window Detection Process,
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1706
Adaptation models, Clutter, Detectors, Radar signal processing,
Random variables, Shape, Surveillance,
Constant false alarm rate (CFAR), invariance, radar detection,
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Prakash, T.[Tanmay],
Kak, A.C.[Avinash C.],
Active learning for designing detectors for infrequently occurring
objects in wide-area satellite imagery,
CVIU(170), 2018, pp. 92-108.
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1806
Object detection, Satellite imagery, Active learning,
Distributed computing, Feature selection, Pattern recognition
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Bailey, D.G.[Donald G.],
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IET-IPR(12), No. 9, September 2018, pp. 1673-1682.
DOI Link
1809
Generalised LoG (GLoG) detector for non-circular blobs.
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Wang, R.[Rui],
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Object instance detection with pruned Alexnet and extended training
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Elsevier DOI
1812
Object instance detection, Pruned Alexnet,
Binarized normed gradient, Data extension
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Li, W.[Wei],
Tao, R.[Ran],
MsRi-CCF: Multi-Scale and Rotation-Insensitive Convolutional Channel
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RS(10), No. 12, 2018, pp. xx-yy.
DOI Link
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Li, Y.[Yan],
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Mixed Supervised Object Detection with Robust Objectness Transfer,
PAMI(41), No. 3, March 2019, pp. 639-653.
IEEE DOI
1902
Detectors, Cats, Robustness, Object detection, Semantics, Training, Face,
Weakly supervised detection, mixed supervised detection,
robust objectness transfer
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Wu, X.[Xing],
Zhang, X.[Xia],
Wang, N.[Nan],
Cen, Y.[Yi],
Joint Sparse and Low-Rank Multi-Task Learning with Extended
Multi-Attribute Profile for Hyperspectral Target Detection,
RS(11), No. 2, 2019, pp. xx-yy.
DOI Link
1902
BibRef
Qiu, H.Q.[He-Qian],
Li, H.L.[Hong-Liang],
Wu, Q.B.[Qing-Bo],
Meng, F.[Fanman],
Ngan, K.N.[King Ngi],
Shi, H.C.[Heng-Can],
A2RMNet: Adaptively Aspect Ratio Multi-Scale Network for Object
Detection in Remote Sensing Images,
RS(11), No. 13, 2019, pp. xx-yy.
DOI Link
1907
BibRef
Pang, J.M.[Jiang-Miao],
Li, C.[Cong],
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RR^2-CNN:
Fast Tiny Object Detection in Large-Scale Remote Sensing Images,
GeoRS(57), No. 8, August 2019, pp. 5512-5524.
IEEE DOI
1908
Remote-sensing Region-based CNN.
convolutional neural nets, feature extraction,
geophysical image processing, image classification,
remote sensing region-based convolutional neural network (R²-CNN)
BibRef
Zhang, Y.L.[Yuan-Lin],
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Feng, Y.C.[Ya-Chuang],
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High-Resolution Remote Sensing Object Detection,
GeoRS(57), No. 8, August 2019, pp. 5535-5548.
IEEE DOI
1908
convolutional neural nets, feature extraction,
learning (artificial intelligence), object detection,
rotation and scaling robust enhancement (RSRE)
See also TGRS-HRRSD-Dataset: High Resolution Remote Sensing Detection (HRRSD).
BibRef
Wan, F.[Fang],
Wei, P.X.[Peng-Xu],
Han, Z.J.[Zhen-Jun],
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Min-Entropy Latent Model for Weakly Supervised Object Detection,
PAMI(41), No. 10, October 2019, pp. 2395-2409.
IEEE DOI
1909
BibRef
Earlier: A1, A2, A4, A3, A5:
CVPR18(1297-1306)
IEEE DOI
1812
Proposals, Detectors, Object detection, Optimization, Entropy,
Redundancy, Task analysis, Weakly supervised learning,
recurrent learning.
Entropy, Training, Graphical models
BibRef
Liu, X.Y.[Xin-Yu],
Li, D.H.[Dong-Hui],
Dong, N.[Na],
Ip, W.H.[Wai Hung],
Yung, K.L.[Kai Leung],
Noncooperative Target Detection of Spacecraft Objects Based on
Artificial Bee Colony Algorithm,
IEEE_Int_Sys(34), No. 4, July 2019, pp. 3-15.
IEEE DOI
1909
Optimization, Artificial bee colony algorithm,
Intelligent systems, Heuristic algorithms, Object detection,
Mathematical model
BibRef
Li, L.[Lin],
Zhang, S.B.[Sheng-Bing],
Wu, J.[Juan],
Efficient Object Detection Framework and Hardware Architecture for
Remote Sensing Images,
RS(11), No. 20, 2019, pp. xx-yy.
DOI Link
1910
BibRef
Zhang, G.J.[Gong-Jie],
Lu, S.J.[Shi-Jian],
Zhang, W.[Wei],
CAD-Net: A Context-Aware Detection Network for Objects in Remote
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GeoRS(57), No. 12, December 2019, pp. 10015-10024.
IEEE DOI
1912
Remote sensing, Object detection, Optical sensors, Optical imaging,
Feature extraction, Detectors, Visualization,
optical remote sensing images
BibRef
Chen, C.,
Ling, Q.,
Adaptive Convolution for Object Detection,
MultMed(21), No. 12, December 2019, pp. 3205-3217.
IEEE DOI
1912
Feature extraction, Detectors, Convolution, Object detection,
Adaptive systems, Task analysis, Semantics, object detection,
deep learning
BibRef
Tong, W.Q.[Wei-Qing],
Li, H.S.[Hai-Sheng],
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Tan, Y.,
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Recent Advances in 3D Object Detection in the Era of Deep Neural
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IEEE DOI
2002
Survey, Objetc Detection. Object detection, Cameras, Sensors,
Laser radar, Task analysis,
deep learning
BibRef
Chen, Q.A.[Qi-Ang],
Wang, P.S.[Pei-Song],
Cheng, A.[Anda],
Wang, W.[Wanguo],
Zhang, Y.[Yifan],
Cheng, J.[Jian],
Robust one-stage object detection with location-aware classifiers,
PR(105), 2020, pp. 107334.
Elsevier DOI
2006
Object detetion, Classification, Localization,
Feature visualization, Receptive field
BibRef
Li, C.L.[Chuan-Long],
Sun, X.M.[Xing-Ming],
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Springer DOI
2006
BibRef
Wang, B.S.[Bi-Sheng],
Cao, G.[Guo],
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Zhang, Y.Q.[You-Qiang],
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CVIU(199), 2020, pp. 103030.
Elsevier DOI
2009
Separate localization and classification components.
Task differentiation, Feature fusion, Object detection, SSD
BibRef
Zhang, R.,
Huang, Y.,
Pu, M.,
Zhang, J.,
Guan, Q.,
Zou, Q.,
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Object Discovery From a Single Unlabeled Image by Mining Frequent
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IP(29), 2020, pp. 8606-8621.
IEEE DOI
2009
Feature extraction, Annotations, Saliency detection, Training,
Data mining, Task analysis, Semantics, Object discovery,
convolutional neural networks
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Hsu, C.C.[Cheng-Chun],
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Every Pixel Matters: Center-aware Feature Alignment for Domain Adaptive
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ECCV20(IX:733-748).
Springer DOI
2011
BibRef
Yan, J.Q.[Jiang-Qiao],
Zhao, L.J.[Liang-Jin],
Diao, W.H.[Wen-Hui],
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AF-EMS Detector: Improve the Multi-Scale Detection Performance of the
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RS(13), No. 2, 2021, pp. xx-yy.
DOI Link
2101
BibRef
García-Domínguez, M.[Manuel],
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UFOD: An AutoML framework for the construction, comparison, and
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PRL(145), 2021, pp. 135-140.
Elsevier DOI
2104
AutoML, Deep learning, Object detection, Transfer learning
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Chen, J.[Jin],
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Duan, L.X.[Li-Xin],
Chen, L.[Lin],
Sequential Instance Refinement for Cross-Domain Object Detection in
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IP(30), 2021, pp. 3970-3984.
IEEE DOI
2104
Object detection, Feature extraction, Detectors,
Reinforcement learning, Proposals, Task analysis.
BibRef
Wang, H.S.[Hong-Song],
Liao, S.C.[Sheng-Cai],
Shao, L.[Ling],
AFAN: Augmented Feature Alignment Network for Cross-Domain Object
Detection,
IP(30), 2021, pp. 4046-4056.
IEEE DOI
2104
Training, Object detection, Feature extraction, Detectors,
Generators, Semantics, Proposals, Object detection,
unsupervised domain adaptation
BibRef
Guo, Y.G.[Ya-Guang],
Zou, Q.[Qi],
Jin, L.[Lu],
A coarse to fine network for fast and accurate object detection in
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IET-CV(15), No. 4, 2021, pp. 274-282.
DOI Link
2106
BibRef
Fang, X.[Xian],
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Wang, H.P.[Hong-Peng],
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Elsevier DOI
2107
Object detection, Bounding box regression, One-stage detector,
Loss function, Non-maximum suppression
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Kalkan, S.[Sinan],
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Imbalance Problems in Object Detection: A Review,
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IEEE DOI
2109
Survey, Object Detection.
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Earlier: A1, A2, A4, A3:
Generating Positive Bounding Boxes for Balanced Training of Object
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WACV20(883-892)
IEEE DOI
2006
Object detection, Taxonomy, Feature extraction, Deep learning,
Pipelines, Neural networks, Pattern analysis, Object detection,
objective imbalance.
Generators, Detectors, Training, Object detection, Sampling methods,
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Nie, J.[Jing],
Pang, Y.W.[Yan-Wei],
Zhao, S.J.[Sheng-Jie],
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Efficient Selective Context Network for Accurate Object Detection,
CirSysVideo(31), No. 9, September 2021, pp. 3456-3468.
IEEE DOI
2109
Feature extraction, Detectors,
Object detection, Semantics, Data mining, Computer architecture,
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Kim, S.T.[Seong Tae],
Lee, H.J.[Hong Joo],
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CirSysVideo(31), No. 9, September 2021, pp. 3529-3543.
IEEE DOI
2109
Detectors, Uncertainty, Training, Object detection, Automobiles,
Feature extraction, Task analysis, Loss gradient modulation,
two-stage region-based object detection
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Wang, K.P.[Kun-Peng],
Cai, J.X.[Jing-Xiang],
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Lightweight Underwater Object Detection Based on YOLO v4 and
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DOI Link
2112
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Chen, Z.[Ze],
Fu, Z.H.[Zhi-Hang],
Huang, J.Q.[Jian-Qiang],
Tao, M.Y.[Ming-Yuan],
Jiang, R.X.[Rong-Xin],
Tian, X.[Xiang],
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IVC(116), 2021, pp. 104314.
Elsevier DOI
2112
BibRef
Earlier: A1, A2, A5, A7, A8, Only:
SLV: Spatial Likelihood Voting for Weakly Supervised Object Detection,
CVPR20(12992-13001)
IEEE DOI
2008
Object detection, Weak supervision, Spatial likelihood voting,
Self-knowledge distillation.
Proposals, Training, Detectors, Task analysis,
Feature extraction
BibRef
Wang, G.B.[Guan-Bo],
Ding, H.W.[Hong-Wei],
Li, B.[Bo],
Nie, R.C.[Ren-Can],
Zhao, Y.[Yifan],
Trident-YOLO: Improving the precision and speed of mobile device
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IET-IPR(16), No. 1, 2022, pp. 145-157.
DOI Link
2112
BibRef
Wang, X.D.[Xiao-Dong],
Zeng, X.X.[Xian-Xian],
Zhang, Y.[Yun],
Chen, K.[Kairui],
Li, D.[Dong],
Improved fine-grained object retrieval with Hard Global Softmin Loss
objective,
SP:IC(100), 2022, pp. 116515.
Elsevier DOI
2112
Fine-grained object retrieval, Hard Global Softmin Loss,
Convolutional neural network
BibRef
Huang, Z.C.[Zhan-Chao],
Li, W.[Wei],
Xia, X.G.[Xiang-Gen],
Wu, X.[Xin],
Cai, Z.Q.[Zhao-Quan],
Tao, R.[Ran],
A Novel Nonlocal-Aware Pyramid and Multiscale Multitask Refinement
Detector for Object Detection in Remote Sensing Images,
GeoRS(60), 2022, pp. 1-20.
IEEE DOI
2112
Feature extraction, Detectors, Task analysis, Head, Visualization,
Remote sensing, Neural networks, Attention, multiscale, multitask,
remote sensing (RS) images
BibRef
Chen, S.[Suting],
Cheng, Z.[Zehua],
Zhang, L.C.[Liang-Chen],
Zheng, Y.[Yujie],
SnipeDet: Attention-guided pyramidal prediction kernels for generic
object detection,
PRL(152), 2021, pp. 302-310.
Elsevier DOI
2112
Attention mechanism, Hard negative mining, Feature enhancement,
Object detection, Prediction module
BibRef
Ye, Y.X.[Yuan-Xin],
Ren, X.Y.[Xiao-Yue],
Zhu, B.[Bai],
Tang, T.F.[Teng-Feng],
Tan, X.[Xin],
Gui, Y.[Yang],
Yao, Q.[Qin],
An Adaptive Attention Fusion Mechanism Convolutional Network for
Object Detection in Remote Sensing Images,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link
2202
BibRef
Zhang, K.[Kaihua],
Shen, H.[Haikuo],
Multi-Stage Feature Enhancement Pyramid Network for Detecting Objects
in Optical Remote Sensing Images,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link
2202
BibRef
Zhang, C.[Cheng],
Pan, T.Y.[Tai-Yu],
Li, Y.D.[Yan-Dong],
Hu, H.[Hexiang],
Xuan, D.[Dong],
Changpinyo, S.[Soravit],
Gong, B.Q.[Bo-Qing],
Chao, W.L.[Wei-Lun],
MosaicOS: A Simple and Effective Use of Object-Centric Images for
Long-Tailed Object Detection,
ICCV21(407-417)
IEEE DOI
2203
Training, Image segmentation, Computational modeling,
Object detection, Detectors, Recognition and classification,
Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Chen, C.L.[Chun-Lin],
Yu, J.[Jun],
Ling, Q.[Qiang],
Sparse attention block:
Aggregating contextual information for object detection,
PR(124), 2022, pp. 108418.
Elsevier DOI
2203
Context around objects.
Object detection, Self-attention, Convolution neural network
BibRef
Zhang, T.[Tao],
Jin, B.[Bo],
Jia, W.J.[Wen-Jing],
An anchor-free object detector based on soften optimized
bi-directional FPN,
CVIU(218), 2022, pp. 103410.
Elsevier DOI
2205
Object detection, Anchor-free, Feature Pyramid Network, Soft-weighted
BibRef
Luo, J.[Junkun],
Hu, Y.M.[Yi-Min],
Li, J.[Jiadong],
Surround-Net:
A Multi-Branch Arbitrary-Oriented Detector for Remote Sensing,
RS(14), No. 7, 2022, pp. xx-yy.
DOI Link
2205
BibRef
Kalra, A.[Agastya],
Stoppi, G.[Guy],
Brown, B.[Bradley],
Agarwal, R.[Rishav],
Kadambi, A.[Achuta],
Towards Rotation Invariance in Object Detection,
ICCV21(3510-3520)
IEEE DOI
2203
Adaptation models, Uncertainty, Codes, Shape, Computational modeling,
Object detection, Detection and localization in 2D and 3D,
Datasets and evaluation
BibRef
Sun, Z.Q.[Zhi-Qing],
Cao, S.[Shengcao],
Yang, Y.M.[Yi-Ming],
Kitani, K.[Kris],
Rethinking Transformer-based Set Prediction for Object Detection,
ICCV21(3591-3600)
IEEE DOI
2203
Training, Codes, Computational modeling, Object detection,
Transformers, Optimization,
BibRef
Wang, K.[Keyang],
Zhang, L.[Lei],
Reconcile Prediction Consistency for Balanced Object Detection,
ICCV21(3611-3620)
IEEE DOI
2203
Location awareness, Training, Shape, Detectors, Object detection,
Benchmark testing, Detection and localization in 2D and 3D,
Recognition and classification
BibRef
Liu, F.F.[Fan-Fan],
Wei, H.R.[Hao-Ran],
Zhao, W.Z.[Wen-Zhe],
Li, G.Z.[Guo-Zhen],
Peng, J.Q.[Jing-Quan],
Li, Z.[Zihao],
WB-DETR: Transformer-Based Detector without Backbone,
ICCV21(2959-2967)
IEEE DOI
2203
Pipelines, Detectors, Object detection, Transformers,
Feature extraction, Decoding,
BibRef
Dai, X.Y.[Xi-Yang],
Chen, Y.P.[Yin-Peng],
Yang, J.W.[Jian-Wei],
Zhang, P.C.[Peng-Chuan],
Yuan, L.[Lu],
Zhang, L.[Lei],
Dynamic DETR: End-to-End Object Detection with Dynamic Attention,
ICCV21(2968-2977)
IEEE DOI
2203
Training, Visualization, Object detection, Color, Transformers,
Feature extraction, Encoding,
Vision applications and systems
BibRef
Zhao, X.Y.[Xiang-Yun],
Zou, X.[Xu],
Wu, Y.[Ying],
Morphable Detector for Object Detection on Demand,
ICCV21(4751-4760)
IEEE DOI
2203
Training, Visualization, Embedded systems, Semantics, Prototypes,
Object detection, Vision applications and systems,
Detection and localization in 2D and 3D
BibRef
Gao, Z.T.[Zi-Teng],
Wang, L.M.[Li-Min],
Wu, G.S.[Gang-Shan],
Mutual Supervision for Dense Object Detection,
ICCV21(3621-3630)
IEEE DOI
2203
Training, Location awareness, Pipelines, Detectors, Object detection,
Benchmark testing, Detection and localization in 2D and 3D,
BibRef
Khindkar, V.[Vaishnavi],
Arora, C.[Chetan],
Balasubramanian, V.N.[Vineeth N.],
Subramanian, A.[Anbumani],
Saluja, R.[Rohit],
Jawahar, C.V.,
To miss-attend is to misalign! Residual Self-Attentive Feature
Alignment for Adapting Object Detectors,
WACV22(376-386)
IEEE DOI
2202
Visualization, Pipelines, Object detection,
Detectors, Benchmark testing, Feature extraction, Transfer,
Vision Systems and Applications
BibRef
Yu, F.[Fuxun],
Wang, D.[Di],
Chen, Y.P.[Yin-Peng],
Karianakis, N.[Nikolaos],
Shen, T.[Tong],
Yu, P.[Pei],
Lymberopoulos, D.[Dimitrios],
Lu, S.[Sidi],
Shi, W.S.[Wei-Song],
Chen, X.[Xiang],
SC-UDA: Style and Content Gaps aware Unsupervised Domain Adaptation
for Object Detection,
WACV22(1061-1070)
IEEE DOI
2202
Costs, Training data, Object detection, Detectors,
Benchmark testing, Feature extraction, Transfer, Few-shot,
Semi- and Un- supervised Learning Scene Understanding
BibRef
Hnewa, M.[Mazin],
Radha, H.[Hayder],
Multiscale Domain Adaptive Yolo for Cross-Domain Object Detection,
ICIP21(3323-3327)
IEEE DOI
2201
Training, Instruments, Object detection, Detectors,
Feature extraction, Real-time systems, Object detection, Domain shift
BibRef
Liu, Y.Y.[Yuan-Yuan],
Liu, Z.Y.[Zi-Yang],
Fang, F.[Fang],
Fu, Z.H.[Zhang-Hua],
Chen, Z.L.[Zhan-Long],
Hierarchical Domain-Consistent Network for Cross-Domain Object
Detection,
ICIP21(474-478)
IEEE DOI
2201
Training, Visualization, Convolution, Prototypes, Object detection,
Feature extraction, Cross-domain object detection, adversarial learning
BibRef
Ye, X.H.[Xin-Hai],
Xiong, F.C.[Feng-Chao],
Lu, J.F.[Jian-Feng],
Zhao, H.F.[Hai-Feng],
Zhou, J.[Jun],
M2-Net: A Multi-scale Multi-level Feature Enhanced Network for Object
Detection in Optical Remote Sensing Images,
DICTA20(1-8)
IEEE DOI
2201
Semantics, Object detection, Detectors, Feature extraction,
Optical imaging, Task analysis, Remote sensing,
multi-scale analysis
BibRef
Seib, V.[Viktor],
Paulus, D.[Dietrich],
Object Detection in Cluttered Environments with Sparse Keypoint
Selection,
TradiCV21(2496-2505)
IEEE DOI
2112
Codes, Neural networks,
Robot vision systems, Object detection, Cameras
BibRef
Zareian, A.[Alireza],
Rosa, K.D.[Kevin Dela],
Hu, D.H.[Derek Hao],
Chang, S.F.[Shih-Fu],
Open-Vocabulary Object Detection Using Captions,
CVPR21(14388-14397)
IEEE DOI
2111
Location awareness, Training, Deep learning, Costs,
Annotations, Object detection
BibRef
Wang, Y.[Yu],
Zhang, R.[Rui],
Zhang, S.[Shuo],
Li, M.[Miao],
Xia, Y.Y.[Yang-Yang],
Zhang, X.S.[Xi-Shan],
Liu, S.L.[Shao-Li],
Domain-Specific Suppression for Adaptive Object Detection,
CVPR21(9598-9607)
IEEE DOI
2111
Degradation, Adaptation models, Convolution, Semantics,
Object detection, Feature extraction, Pattern recognition
BibRef
Joseph, K.J.,
Khan, S.[Salman],
Khan, F.S.[Fahad Shahbaz],
Balasubramanian, V.N.[Vineeth N.],
Towards Open World Object Detection,
CVPR21(5826-5836)
IEEE DOI
2111
Protocols, Computational modeling,
Object detection, Detectors, Benchmark testing, Pattern recognition
BibRef
VS, V.[Vibashan],
Gupta, V.[Vikram],
Oza, P.[Poojan],
Sindagi, V.A.[Vishwanath A.],
Patel, V.M.[Vishal M.],
MeGA-CDA: Memory Guided Attention for Category-Aware Unsupervised
Domain Adaptive Object Detection,
CVPR21(4514-4524)
IEEE DOI
2111
Training, Object detection, Benchmark testing,
Feature extraction, Routing, Pattern recognition
BibRef
Deng, J.[Jinhong],
Li, W.[Wen],
Chen, Y.H.[Yu-Hua],
Duan, L.X.[Li-Xin],
Unbiased Mean Teacher for Cross-domain Object Detection,
CVPR21(4089-4099)
IEEE DOI
2111
Training, Adaptation models,
Computational modeling, 3G mobile communication, Estimation, Object detection
BibRef
Wang, T.[Tong],
Zhu, Y.S.[You-Song],
Zhao, C.Y.[Chao-Yang],
Zeng, W.[Wei],
Wang, J.Q.[Jin-Qiao],
Tang, M.[Ming],
Adaptive Class Suppression Loss for Long-Tail Object Detection,
CVPR21(3102-3111)
IEEE DOI
2111
Training, Adaptation models, Vocabulary, Head, Object detection, Manuals
BibRef
Guo, J.Y.[Jian-Yuan],
Han, K.[Kai],
Wang, Y.H.[Yun-He],
Wu, H.[Han],
Chen, X.[Xinghao],
Xu, C.J.[Chun-Jing],
Xu, C.[Chang],
Distilling Object Detectors via Decoupled Features,
CVPR21(2154-2164)
IEEE DOI
2111
Knowledge engineering, Semantics, Detectors, Object detection,
Feature extraction, Pattern recognition, Neck
BibRef
Zhang, S.Y.[Song-Yang],
Li, Z.[Zeming],
Yan, S.P.[Shi-Peng],
He, X.M.[Xu-Ming],
Sun, J.[Jian],
Distribution Alignment:
A Unified Framework for Long-tail Visual Recognition,
CVPR21(2361-2370)
IEEE DOI
2111
Deep learning, Visualization, Image segmentation,
Semantics, Object detection, Pattern recognition
BibRef
Guo, J.Y.[Jian-Yuan],
Han, K.[Kai],
Wu, H.[Han],
Zhang, C.[Chao],
Chen, X.H.[Xing-Hao],
Xu, C.J.[Chun-Jing],
Xu, C.[Chang],
Wang, Y.H.[Yun-He],
Positive-Unlabeled Data Purification in the Wild for Object Detection,
CVPR21(2652-2661)
IEEE DOI
2111
Purification, Training data, Image annotation, Object detection,
Detectors, Benchmark testing, Semisupervised learning
BibRef
Meng, D.[Depu],
Chen, X.K.[Xiao-Kang],
Fan, Z.[Zejia],
Zeng, G.[Gang],
Li, H.Q.[Hou-Qiang],
Yuan, Y.[Yuhui],
Sun, L.[Lei],
Wang, J.D.[Jing-Dong],
Conditional DETR for Fast Training Convergence,
ICCV21(3631-3640)
IEEE DOI
2203
WWW Link. Encoder-decoder object detection.
Training, Codes, Pose estimation, Object detection, Transformers,
Decoding, Detection and localization in 2D and 3D,
Recognition and classification
BibRef
Gao, P.[Peng],
Zheng, M.H.[Ming-Hang],
Wang, X.G.[Xiao-Gang],
Dai, J.F.[Ji-Feng],
Li, H.S.[Hong-Sheng],
Fast Convergence of DETR with Spatially Modulated Co-Attention,
ICCV21(3601-3610)
IEEE DOI
2203
Training, Representation learning, Visualization, Schedules, Costs,
Object detection, Transformers, Vision applications and systems
BibRef
Dai, Z.G.[Zhi-Gang],
Cai, B.[Bolun],
Lin, Y.[Yugeng],
Chen, J.[Junying],
UP-DETR: Unsupervised Pre-training for Object Detection with
Transformers,
CVPR21(1601-1610)
IEEE DOI
2111
Location awareness, Training data, Object detection, Transformers,
Feature extraction, Natural language processing, Pattern recognition
BibRef
Tan, J.[Jingru],
Lu, X.[Xin],
Zhang, G.[Gang],
Yin, C.Q.[Chang-Qing],
Li, Q.Q.[Quan-Quan],
Equalization Loss v2:
A New Gradient Balance Approach for Long-tailed Object Detection,
CVPR21(1685-1694)
IEEE DOI
2111
Training, Codes, Object detection,
Benchmark testing, Boosting, Pattern recognition
BibRef
Ma, Y.C.[Yu-Chen],
Liu, S.T.[Song-Tao],
Li, Z.[Zeming],
Sun, J.[Jian],
IQDet:
Instance-wise Quality Distribution Sampling for Object Detection,
CVPR21(1717-1725)
IEEE DOI
2111
Training, Visualization, Semantics, Detectors, Object detection,
Mixture models, Feature extraction
BibRef
Ge, Z.[Zheng],
Liu, S.T.[Song-Tao],
Li, Z.[Zeming],
Yoshie, O.[Osamu],
Sun, J.[Jian],
OTA: Optimal Transport Assignment for Object Detection,
CVPR21(303-312)
IEEE DOI
2111
WWW Link.
Code, Object Detection. Training, Costs, Codes, Transportation, Estimation, Object detection
BibRef
Liu, J.[Ji],
Li, D.[Dong],
Zheng, R.Z.[Rong-Zhang],
Tian, L.[Lu],
Shan, Y.[Yi],
RankDetNet: Delving into Ranking Constraints for Object Detection,
CVPR21(264-273)
IEEE DOI
2111
Location awareness,
Costs, Object detection, Pattern recognition, Classification algorithms
BibRef
Plaut, E.[Elad],
Ben Yaacov, E.[Erez],
El Shlomo, B.[Bat],
3D Object Detection from a Single Fisheye Image Without a Single
Fisheye Training Image,
OmniCV21(3654-3662)
IEEE DOI
2109
Training, Solid modeling,
Training data, Object detection, Detectors, Network architecture
BibRef
Pardo, A.[Alejandro],
Xu, M.M.[Meng-Meng],
Thabet, A.[Ali],
Arbeláez, P.[Pablo],
Ghanem, B.[Bernard],
BAOD: Budget-Aware Object Detection,
LXCV21(1247-1256)
IEEE DOI
2109
Uncertainty, Annotations, Supervised learning,
Diversity reception, Optimization methods, Object detection
BibRef
Kim, S.[Songeun],
Park, S.Y.[Soon-Yong],
Expandable Spherical Projection and Feature Fusion Methods for Object
Detection from Fisheye Images,
MVA21(1-5)
DOI Link
2109
Image edge detection, Object detection,
Feature extraction, Cameras, Distortion, Real-time systems
BibRef
Jaiswal, A.[Ayush],
Wu, Y.[Yue],
Natarajan, P.[Pradeep],
Natarajan, P.[Premkumar],
Class-agnostic Object Detection,
WACV21(918-927)
IEEE DOI
2106
Training, Visualization, Protocols, Grounding, Object detection
BibRef
Fang, F.[Fen],
Xu, Q.[Qianli],
Li, L.Y.[Li-Yuan],
Gu, Y.[Ying],
Lim, J.H.[Joo-Hwee],
Detecting Objects with High Object Region Percentage,
ICPR21(7173-7180)
IEEE DOI
2105
Training, Location awareness, Shape, Costing, Object detection,
Detectors, Object-region-percentage, neural network
BibRef
Liu, L.Q.[Li-Qiang],
Wei, S.A.[Shi-An],
Jiang, L.[Long],
Wang, Y.T.[Ya-Tao],
Weighted Aggregating Feature Pyramid Network for Object Detection,
CVIDL20(347-353)
IEEE DOI
2102
feature extraction, image representation, object detection,
lightweight convolutional module, object detection methods,
Object detection
BibRef
Bai, Y.,
Meng, Z.,
Feature Maps Channel Augmentation for Object Detection,
CVIDL20(125-129)
IEEE DOI
2102
object detection, optimisation,
optimization solution, inter-channel relationship, Attention Mechanism
BibRef
Su, P.[Peng],
Wang, K.[Kun],
Zeng, X.Y.[Xing-Yu],
Tang, S.X.[Shi-Xiang],
Chen, D.P.[Da-Peng],
Qiu, D.[Di],
Wang, X.G.[Xiao-Gang],
Adapting Object Detectors with Conditional Domain Normalization,
ECCV20(XI:403-419).
Springer DOI
2011
BibRef
Kim, H.[Hanjae],
Joung, S.[Sunghun],
Kim, I.J.[Ig-Jae],
Sohn, K.H.[Kwang-Hoon],
Shape-Adaptive Kernel Network for Dense Object Detection,
ICIP20(2046-2050)
IEEE DOI
2011
Kernel, Shape, Object detection, Detectors, Convolution,
Feature extraction, Strain, Dense object detection,
object deformation
BibRef
Zhao, X.Y.[Xiang-Yun],
Schulter, S.[Samuel],
Sharma, G.[Gaurav],
Tsai, Y.H.[Yi-Hsuan],
Chandraker, M.[Manmohan],
Wu, Y.[Ying],
Object Detection with a Unified Label Space from Multiple Datasets,
ECCV20(XIV:178-193).
Springer DOI
2011
BibRef
Hou, Y.Z.[Yun-Zhong],
Zheng, L.[Liang],
Gould, S.[Stephen],
Multiview Detection with Feature Perspective Transformation,
ECCV20(VII:1-18).
Springer DOI
2011
Code, Object Detection.
WWW Link. MultiviewX Dataset.
BibRef
Qiu, H.[Han],
Ma, Y.C.[Yu-Chen],
Li, Z.M.[Ze-Ming],
Liu, S.T.[Song-Tao],
Sun, J.[Jian],
BorderDet: Border Feature for Dense Object Detection,
ECCV20(I:549-564).
Springer DOI
2011
A point-like feature to guide the border search, for dense collection
of objects.
BibRef
Carion, N.[Nicolas],
Massa, F.[Francisco],
Synnaeve, G.[Gabriel],
Usunier, N.[Nicolas],
Kirillov, A.[Alexander],
Zagoruyko, S.[Sergey],
End-to-end Object Detection with Transformers,
ECCV20(I:213-229).
Springer DOI
2011
BibRef
Li, J.D.[Jun-De],
Ghosh, S.[Swaroop],
Quantum-soft Qubo Suppression for Accurate Object Detection,
ECCV20(XXIX: 158-173).
Springer DOI
2010
BibRef
Cao, Y.,
Chen, K.,
Loy, C.C.,
Lin, D.,
Prime Sample Attention in Object Detection,
CVPR20(11580-11588)
IEEE DOI
2008
Detectors, Training, Object detection, Task analysis, Proposals,
Measurement, Focusing
BibRef
Jiang, C.,
Xu, H.,
Zhang, W.,
Liang, X.,
Li, Z.,
SP-NAS: Serial-to-Parallel Backbone Search for Object Detection,
CVPR20(11860-11869)
IEEE DOI
2008
Computer architecture, Feature extraction, Task analysis,
Object detection, Search problems, Neck, Spatial resolution
BibRef
Tan, J.,
Wang, C.,
Li, B.,
Li, Q.,
Ouyang, W.,
Yin, C.,
Yan, J.,
Equalization Loss for Long-Tailed Object Recognition,
CVPR20(11659-11668)
IEEE DOI
2008
Training, Task analysis, Proposals, Detectors, Object recognition,
Object detection
BibRef
Wu, Z.,
Tao, Q.,
Lin, G.,
Cai, J.,
Exploring Bottom-Up and Top-Down Cues With Attentive Learning for
Webly Supervised Object Detection,
CVPR20(12933-12942)
IEEE DOI
2008
Object detection, Detectors, Training, Labeling, Task analysis,
Feature extraction, Testing
BibRef
Wang, X.,
Zhang, S.,
Yu, Z.,
Feng, L.,
Zhang, W.,
Scale-Equalizing Pyramid Convolution for Object Detection,
CVPR20(13356-13365)
IEEE DOI
2008
Convolution, Feature extraction, Kernel, Detectors, Correlation,
Object detection, Head
BibRef
Zhang, C.,
Cao, Y.,
Wu, J.,
Rethinking the Route Towards Weakly Supervised Object Localization,
CVPR20(13457-13466)
IEEE DOI
2008
Task analysis, Computational modeling, Pipelines, Detectors,
Training, Noise measurement
BibRef
Küppers, F.,
Kronenberger, J.,
Shantia, A.,
Haselhoff, A.,
Multivariate Confidence Calibration for Object Detection,
SAIAD20(1322-1330)
IEEE DOI
2008
Calibration, Detectors, Object detection, Logistics, Uncertainty,
Task analysis, Standards
BibRef
Pato, L.V.,
Negrinho, R.,
Aguiar, P.M.Q.,
Seeing without Looking: Contextual Rescoring of Object Detections for
AP Maximization,
CVPR20(14598-14606)
IEEE DOI
2008
Detectors, Feature extraction, Visualization, Context modeling,
Object detection, Proposals
BibRef
Guo, J.,
Han, K.,
Wang, Y.,
Zhang, C.,
Yang, Z.,
Wu, H.,
Chen, X.,
Xu, C.,
Hit-Detector: Hierarchical Trinity Architecture Search for Object
Detection,
CVPR20(11402-11411)
IEEE DOI
2008
Detectors, Neck, Object detection, Feature extraction,
Computer architecture, Search problems, Task analysis
BibRef
Shen, Y.,
Ji, R.,
Chen, Z.,
Hong, X.,
Zheng, F.,
Liu, J.,
Xu, M.,
Tian, Q.,
Noise-Aware Fully Webly Supervised Object Detection,
CVPR20(11323-11332)
IEEE DOI
2008
Noise measurement, Detectors, Training, Object detection,
Task analysis, Data models, Proposals
BibRef
Tan, M.,
Pang, R.,
Le, Q.V.,
EfficientDet: Scalable and Efficient Object Detection,
CVPR20(10778-10787)
IEEE DOI
2008
Detectors, Feature extraction, Compounds, Object detection,
Image resolution, Network architecture, Optimization
BibRef
Ramanathan, V.,
Wang, R.,
Mahajan, D.,
DLWL:
Improving Detection for Lowshot Classes With Weakly Labelled Data,
CVPR20(9339-9349)
IEEE DOI
2008
Proposals, Training, Data models, Object detection, Standards,
Predictive models
BibRef
Zhu, P.,
Wang, H.,
Saligrama, V.,
Don't Even Look Once: Synthesizing Features for Zero-Shot Detection,
CVPR20(11690-11699)
IEEE DOI
2008
Detectors, Visualization, Feature extraction, Training, Semantics,
Object detection, Measurement
BibRef
Zhao, N.,
Chua, T.,
Lee, G.H.,
SESS: Self-Ensembling Semi-Supervised 3D Object Detection,
CVPR20(11076-11084)
IEEE DOI
2008
Object detection,
Perturbation methods, Proposals, Task analysis, Training
BibRef
Zheng, Y.,
Huang, D.,
Liu, S.,
Wang, Y.,
Cross-domain Object Detection through Coarse-to-Fine Feature
Adaptation,
CVPR20(13763-13772)
IEEE DOI
2008
Feature extraction, Subspace constraints, Object detection,
Detectors, Task analysis, Semantics, Prototypes
BibRef
Qiu, H.,
Li, H.,
Wu, Q.,
Shi, H.,
Offset Bin Classification Network for Accurate Object Detection,
CVPR20(13185-13194)
IEEE DOI
2008
Object detection, Feature extraction, Focusing, Detectors,
Explosions, Proposals, Entropy
BibRef
Chen, C.,
Liu, M.,
Meng, X.,
Xiao, W.,
Ju, Q.,
RefineDetLite: A Lightweight One-stage Object Detection Framework for
CPU-only Devices,
EDLCV20(2997-3007)
IEEE DOI
2008
Detectors, Training, Feature extraction, Object detection,
Convolution, Task analysis, Computational complexity
BibRef
Ren, Z.,
Yu, Z.,
Yang, X.,
Liu, M.,
Lee, Y.J.,
Schwing, A.G.,
Kautz, J.,
Instance-Aware, Context-Focused, and Memory-Efficient Weakly
Supervised Object Detection,
CVPR20(10595-10604)
IEEE DOI
2008
Proposals, Object detection, Training, Memory management, Detectors,
Task analysis, Face
BibRef
Li, H.,
Wu, Z.,
Zhu, C.,
Xiong, C.,
Socher, R.,
Davis, L.S.,
Learning From Noisy Anchors for One-Stage Object Detection,
CVPR20(10585-10594)
IEEE DOI
2008
Detectors, Training, Noise measurement, Proposals, Object detection,
Standards, Head
BibRef
Li, J.C.[Jia-Chen],
Cheng, B.[Bowen],
Feris, R.[Rogerio],
Xiong, J.[Jinjun],
Huang, T.S.[Thomas S.],
Hwu, W.M.[Wen-Mei],
Shi, H.[Humphrey],
Pseudo-IoU: Improving Label Assignment in Anchor-Free Object
Detection,
MAI21(2378-2387)
IEEE DOI
2109
Measurement, Training, Location awareness,
Computational modeling, Object detection
BibRef
Ramakrishnan, K.,
Panda, R.,
Fan, Q.,
Henning, J.,
Oliva, A.,
Feris, R.,
Relationship Matters: Relation Guided Knowledge Transfer for
Incremental Learning of Object Detectors,
CLVision20(1009-1018)
IEEE DOI
2008
Proposals, Detectors, Knowledge engineering, Object detection,
Training, Task analysis, Knowledge transfer
BibRef
Farhadi, M.,
Ghasemi, M.,
Vrudhula, S.,
Yang, Y.,
Enabling Incremental Knowledge Transfer for Object Detection at the
Edge,
LPCV20(1591-1599)
IEEE DOI
2008
Adaptation models, Object detection, Computational modeling,
Knowledge transfer, Feature extraction, Image edge detection, Performance evaluation
BibRef
Li, Y.,
Pang, Y.,
Shen, J.,
Cao, J.,
Shao, L.,
NETNet: Neighbor Erasing and Transferring Network for Better Single
Shot Object Detection,
CVPR20(13346-13355)
IEEE DOI
2008
Feature extraction, Detectors, Object detection,
Nanoelectromechanical systems, Logic gates, Semantics
BibRef
Chen, C.,
Zheng, Z.,
Ding, X.,
Huang, Y.,
Dou, Q.,
Harmonizing Transferability and Discriminability for Adapting Object
Detectors,
CVPR20(8866-8875)
IEEE DOI
2008
Feature extraction, Training, Object detection, Semantics,
Interpolation, Detectors, Task analysis
BibRef
Wang, Z.,
Wu, Z.,
Lu, J.,
Zhou, J.,
BiDet: An Efficient Binarized Object Detector,
CVPR20(2046-2055)
IEEE DOI
2008
Detectors, Object detection, Neural networks, Feature extraction,
Mutual information, Redundancy, Quantization (signal)
BibRef
Varadarajan, S.[Srikrishna],
Kant, S.[Sonaal],
Srivastava, M.M.[Muktabh Mayank],
Benchmark for Generic Product Detection:
A Low Data Baseline for Dense Object Detection,
ICIAR20(I:30-41).
Springer DOI
2007
Generic detection, to use across applications.
BibRef
Srivastava, M.M.[Muktabh Mayank],
Bag of Tricks for Retail Product Image Classification,
ICIAR20(I:71-82).
Springer DOI
2007
BibRef
Zhang, H.Y.[Hao-Yang],
Wang, Y.[Ying],
Dayoub, F.[Feras],
Sünderhauf, N.[Niko],
VarifocalNet: An IoU-aware Dense Object Detector,
CVPR21(8510-8519)
IEEE DOI
2111
Location awareness, Training, Codes, Detectors,
Computer architecture, Benchmark testing
BibRef
Hall, D.,
Dayoub, F.,
Skinner, J.,
Zhang, H.,
Miller, D.,
Corke, P.,
Carneiro, G.,
Angelova, A.,
Sünderhauf, N.,
Probabilistic Object Detection: Definition and Evaluation,
WACV20(1020-1029)
IEEE DOI
2006
Uncertainty, Object detection, Detectors, Probabilistic logic,
Task analysis, Semantics, Robots
BibRef
Huang, Z.,
Ke, W.,
Huang, D.,
Improving Object Detection with Inverted Attention,
WACV20(1294-1302)
IEEE DOI
2006
Training, Heating systems, Detectors, Feature extraction,
Tensile stress, Training data, Object detection
BibRef
Yang, Z.,
Liu, S.,
Hu, H.,
Wang, L.,
Lin, S.,
RepPoints: Point Set Representation for Object Detection,
ICCV19(9656-9665)
IEEE DOI
2004
Code, Object Detection.
WWW Link. object detection, object recognition, point set representation,
object detection, modern object detectors,
Training
BibRef
Li, X.Y.[Xiao-Yan],
Kan, M.[Meina],
Shan, S.G.[Shi-Guang],
Chen, X.L.[Xi-Lin],
Weakly Supervised Object Detection With Segmentation Collaboration,
ICCV19(9734-9743)
IEEE DOI
2004
image classification, image representation, image segmentation,
learning (artificial intelligence), object detection, Pascal,
Image segmentation
BibRef
Zhao, Y.,
Price, B.,
Cohen, S.,
Gurari, D.,
Unconstrained Foreground Object Search,
ICCV19(2030-2039)
IEEE DOI
2004
image classification, image retrieval,
learning (artificial intelligence), object detection,
Image color analysis
BibRef
Jiang, P.,
Hou, Q.,
Cao, Y.,
Cheng, M.,
Wei, Y.,
Xiong, H.,
Integral Object Mining via Online Attention Accumulation,
ICCV19(2070-2079)
IEEE DOI
2004
Code, Object Detection.
WWW Link. image classification, image segmentation, object detection,
object recognition, integral object mining, Benchmark testing
BibRef
Li, F.,
Mo, Z.,
Wang, P.,
Liu, Z.,
Zhang, J.,
Li, G.,
Hu, Q.,
He, X.,
Leng, C.,
Zhang, Y.,
Cheng, J.,
A System-Level Solution for Low-Power Object Detection,
LPCV19(2461-2468)
IEEE DOI
2004
embedded systems, learning (artificial intelligence),
object detection, video surveillance, video surveillance,
Neural networks
BibRef
Shao, S.,
Li, Z.,
Zhang, T.,
Peng, C.,
Yu, G.,
Zhang, X.,
Li, J.,
Sun, J.,
Objects365: A Large-Scale, High-Quality Dataset for Object Detection,
ICCV19(8429-8438)
IEEE DOI
2004
Dataset, Object Detection. feature extraction, image annotation, image classification,
image segmentation, learning (artificial intelligence), Clocks
BibRef
Wu, Z.,
Suresh, K.,
Narayanan, P.,
Xu, H.,
Kwon, H.,
Wang, Z.,
Delving Into Robust Object Detection From Unmanned Aerial Vehicles:
A Deep Nuisance Disentanglement Approach,
ICCV19(1201-1210)
IEEE DOI
2004
Code, Object Detection.
WWW Link. autonomous aerial vehicles, learning (artificial intelligence),
object detection, transforms, free meta-data, UAV images, Detectors
BibRef
Wang, T.[Tao],
Yuan, L.[Li],
Zhang, X.P.[Xiao-Peng],
Feng, J.S.[Jia-Shi],
Distilling Object Detectors With Fine-Grained Feature Imitation,
CVPR19(4928-4937).
IEEE DOI
2002
BibRef
Cai, L.[Lile],
Zhao, B.[Bin],
Wang, Z.[Zhe],
Lin, J.[Jie],
Foo, C.S.[Chuan Sheng],
Aly, M.S.[Mohamed Sabry],
Chandrasekhar, V.[Vijay],
MaxpoolNMS: Getting Rid of NMS Bottlenecks in Two-Stage Object
Detectors,
CVPR19(9348-9356).
IEEE DOI
2002
BibRef
Arun, A.[Aditya],
Jawahar, C.V.,
Kumar, M.P.[M. Pawan],
Dissimilarity Coefficient Based Weakly Supervised Object Detection,
CVPR19(9424-9433).
IEEE DOI
2002
BibRef
Xu, H.[Hang],
Jiang, C.[Chenhan],
Liang, X.D.[Xiao-Dan],
Li, Z.G.[Zhen-Guo],
Spatial-Aware Graph Relation Network for Large-Scale Object Detection,
CVPR19(9290-9299).
IEEE DOI
2002
BibRef
Lin, D.[Di],
Shen, D.G.[Ding-Guo],
Shen, S.T.[Si-Ting],
Ji, Y.F.[Yuan-Feng],
Lischinski, D.[Dani],
Cohen-Or, D.[Daniel],
Huang, H.[Hui],
ZigZagNet: Fusing Top-Down and Bottom-Up Context for Object
Segmentation,
CVPR19(7482-7491).
IEEE DOI
2002
BibRef
Niitani, Y.[Yusuke],
Akiba, T.[Takuya],
Kerola, T.[Tommi],
Ogawa, T.[Toru],
Sano, S.[Shotaro],
Suzuki, S.[Shuji],
Sampling Techniques for Large-Scale Object Detection From Sparsely
Annotated Objects,
CVPR19(6503-6511).
IEEE DOI
2002
BibRef
Saito, K.[Kuniaki],
Ushiku, Y.[Yoshitaka],
Harada, T.[Tatsuya],
Saenko, K.[Kate],
Strong-Weak Distribution Alignment for Adaptive Object Detection,
CVPR19(6949-6958).
IEEE DOI
2002
BibRef
Barnea, E.[Ehud],
Ben-Shahar, O.[Ohad],
Exploring the Bounds of the Utility of Context for Object Detection,
CVPR19(7404-7412).
IEEE DOI
2002
BibRef
Sawatzky, J.[Johann],
Souri, Y.[Yaser],
Grund, C.[Christian],
Gall, J.[Jurgen],
What Object Should I Use? - Task Driven Object Detection,
CVPR19(7597-7606).
IEEE DOI
2002
BibRef
RoyChowdhury, A.[Aruni],
Chakrabarty, P.[Prithvijit],
Singh, A.[Ashish],
Jin, S.[SouYoung],
Jiang, H.[Huaizu],
Cao, L.L.[Liang-Liang],
Learned-Miller, E.[Erik],
Automatic Adaptation of Object Detectors to New Domains Using
Self-Training,
CVPR19(780-790).
IEEE DOI
2002
BibRef
Zhu, X.G.[Xin-Ge],
Pang, J.M.[Jiang-Miao],
Yang, C.[Ceyuan],
Shi, J.P.[Jian-Ping],
Lin, D.[Dahua],
Adapting Object Detectors via Selective Cross-Domain Alignment,
CVPR19(687-696).
IEEE DOI
2002
BibRef
Zhou, X.Y.[Xing-Yi],
Zhuo, J.C.[Jia-Cheng],
Krahenbuhl, P.[Philipp],
Bottom-Up Object Detection by Grouping Extreme and Center Points,
CVPR19(850-859).
IEEE DOI
2002
BibRef
Du, P.,
Zhang, H.,
Ma, H.,
Classifier Refinement for Weakly Supervised Object Detection with
Class-Specific Activation Map,
ICIP19(3367-3371)
IEEE DOI
1910
Weakly supervised learning, object detection,
image-level annotations, class-specific activation map
BibRef
Antioquia, A.M.C.,
Tan, D.S.[D. Stanley],
Azcarraga, A.,
Hua, K.,
Single-Fusion Detector: Towards Faster Multi-Scale Object Detection,
ICIP19(76-80)
IEEE DOI
1910
Object Detection, Feature Fusion, Object Recognition,
Convolutional Neural Networks, Deep Learning
BibRef
Son, J.[Jeany],
Kim, D.[Daniel],
Lee, S.[Solae],
Kwak, S.[Suha],
Cho, M.[Minsu],
Han, B.H.[Bo-Hyung],
Forget and Diversify:
Regularized Refinement for Weakly Supervised Object Detection,
ACCV18(IV:632-648).
Springer DOI
1906
BibRef
Wever, R.[Rijnder],
Runia, T.F.H.[Tom F. H.],
Subitizing with Variational Autoencoders,
BrainDriven18(III:617-627).
Springer DOI
1905
Count number of objects in a small set.
BibRef
Mehta, R.[Rakesh],
Ozturk, C.[Cemalettin],
Object Detection at 200 Frames per Second,
AutoNUE18(V:659-675).
Springer DOI
1905
BibRef
Joseph, K.J.,
Patel, R.C.[Rajiv Chunilal],
Srivastava, A.[Amit],
Gupta, U.[Uma],
Balasubramanian, V.N.[Vineeth N.],
MASON: A Model AgnoStic ObjectNess Framework,
AutoNUE18(V:642-658).
Springer DOI
1905
BibRef
Zhang, K.J.[Kai-Jun],
Guo, C.H.[Cheng-Hao],
Niu, Z.H.[Zhong-Han],
Liu, L.F.[Lu-Fei],
Yang, Y.B.[Yu-Bin],
SCOD: Dynamical Spatial Constraints for Object Detection,
MMMod19(I:17-28).
Springer DOI
1901
BibRef
Zhang, Y.Q.[Yong-Qiang],
Bai, Y.C.[Yan-Cheng],
Ding, M.L.[Ming-Li],
Li, Y.Q.[Yong-Qiang],
Ghanem, B.[Bernard],
W2F: A Weakly-Supervised to Fully-Supervised Framework for Object
Detection,
CVPR18(928-936)
IEEE DOI
1812
Detectors, Object detection, Training, Proposals,
Electronics packaging, Streaming media, Cats
BibRef
Kim, Y.H.[Yong-Hyun],
Kang, B.N.[Bong-Nam],
Kim, D.J.[Dai-Jin],
Detector with focus: Normalizing gradient in image pyramid,
ICIP17(420-424)
IEEE DOI
1803
Data models, Deformable models, Detectors, Interpolation,
Object detection, Pose estimation, Training, detection, gradient, normalization
BibRef
Tychsen-Smith, L.,
Petersson, L.,
DeNet: Scalable Real-Time Object Detection with Directed Sparse
Sampling,
ICCV17(428-436)
IEEE DOI
1802
convolution, deconvolution, neural nets, object detection,
sampling methods, statistical distributions,
BibRef
Redmon, J.[Joseph],
Farhadi, A.[Ali],
YOLO9000: Better, Faster, Stronger,
CVPR17(6517-6525)
IEEE DOI
1711
Award, CVPR, HM. Detectors, Feature extraction, Image resolution, Object detection,
Real-time systems, Training
Real time, 9000 object categories.
BibRef
Chan, J.[Jacob],
Lee, J.A.[Jimmy Addison],
Kemao, Q.[Qian],
BIND: Binary Integrated Net Descriptors for Texture-Less Object
Recognition,
CVPR17(3020-3028)
IEEE DOI
1711
Clutter, Detectors, Encoding, Image edge detection,
Object recognition, Resistance, Robustness
Compare to BORDER, BOLD, LINE2D
BibRef
Chen, K.[Kai],
Song, H.[Hang],
Loy, C.C.[Chen Change],
Lin, D.[Dahua],
Discover and Learn New Objects from Documentaries,
CVPR17(1111-1120)
IEEE DOI
1711
Detectors, Optimization, Pragmatics, Proposals, Training, Visualization
BibRef
Hoffman, J.[Judy],
Gupta, S.[Saurabh],
Darrell, T.J.[Trevor J.],
Learning with Side Information through Modality Hallucination,
CVPR16(826-834)
IEEE DOI
1612
RGB recognition, trained with depth information.
BibRef
Shrivastava, A.,
Gupta, A.,
Girshick, R.[Ross],
Training Region-Based Object Detectors with Online Hard Example
Mining,
CVPR16(761-769)
IEEE DOI
1612
BibRef
Redmon, J.,
Divvala, S.,
Girshick, R.,
Farhadi, A.,
You Only Look Once: Unified, Real-Time Object Detection,
CVPR16(779-788)
IEEE DOI
1612
BibRef
Arrais, R.[Rafael],
Oliveira, M.[Miguel],
Toscano, C.[César],
Veiga, G.[Germano],
A Hybrid Top-Down Bottom-Up Approach for the Detection of Cuboid Shaped
Objects,
ICIAR16(512-520).
Springer DOI
1608
BibRef
Duan, K.[Kun],
Wang, W.[Wei],
Yu, T.[Ting],
Procrustean decomposition for orthogonal cascade detection,
WACV16(1-9)
IEEE DOI
1606
speed up a standard sliding window detector.
Detectors
BibRef
Newtson, K.,
Creusere, C.D.,
Histogram Oriented Gradients and Map Seeking Circuits pattern
recognition with compressed imagery,
Southwest16(113-116)
IEEE DOI
1605
Feature extraction
Finding the edges and correlate the patterns with the object of interest.
BibRef
Lu, Y.,
Lu, C.[Cewu],
Tang, C.K.[Chi-Keung],
Online Video Object Detection Using Association LSTM,
ICCV17(2363-2371)
IEEE DOI
1802
object detection, video signal processing,
Long Short-Term Memory, association LSTM,
Tools
BibRef
Lee, M.H.[Man Hee],
Park, I.K.[In Kyu],
Performance Evaluation of Local Descriptors for Affine Invariant Region
Detector,
RoLoD14(630-643).
Springer DOI
1504
BibRef
Valmadre, J.[Jack],
Sridharan, S.[Sridha],
Lucey, S.[Simon],
Learning Detectors Quickly with Stationary Statistics,
ACCV14(I: 99-114).
Springer DOI
1504
Object detectors.
BibRef
Fang, W.H.[Wen-Hua],
Chen, J.[Jun],
Liang, C.[Chao],
Wang, X.[Xiao],
Nan, Y.Y.[Yuan-Yuan],
Hu, R.M.[Rui-Min],
Object Detection in Low-Resolution Image via Sparse Representation,
MMMod15(I: 234-245).
Springer DOI
1501
reconstruct higher resolution image for detection.
BibRef
Frintrop, S.[Simone],
Garcia, G.M.[German Martin],
Cremers, A.B.[Armin B.],
A Cognitive Approach for Object Discovery,
ICPR14(2329-2334)
IEEE DOI
1412
Databases
BibRef
Ma, K.[Kai],
Ben-Arie, J.[Jezekiel],
Compound Exemplar Based Object Detection by Incremental Random Forest,
ICPR14(2407-2412)
IEEE DOI
1412
Dynamic programming
BibRef
Riabchenko, E.[Ekaterina],
Chen, K.[Ke],
Kämäräinen, J.K.[Joni-Kristian],
Progressive Visual Object Detection with Positive Training Examples
Only,
SCIA15(388-399).
Springer DOI
1506
BibRef
Earlier: A1, A3, A2:
Density-Aware Part-Based Object Detection with Positive Examples,
ICPR14(2814-2819)
IEEE DOI
1412
Detectors
BibRef
Peng, X.C.[Xing-Chao],
Saenko, K.[Kate],
Combining Texture and Shape Cues for Object Recognition with Minimal
Supervision,
ACCV16(IV: 256-272).
Springer DOI
1704
BibRef
Peng, X.C.[Xing-Chao],
Sun, B.C.[Bao-Chen],
Ali, K.[Karim],
Saenko, K.[Kate],
Learning Deep Object Detectors from 3D Models,
ICCV15(1278-1286)
IEEE DOI
1602
Data models. Use crowdsource 3D CAD models for training. But include low-level
cues.
BibRef
Sun, B.C.[Bao-Chen],
Saenko, K.[Kate],
Deep CORAL: Correlation Alignment for Deep Domain Adaptation,
TASKCV16(III: 443-450).
Springer DOI
1611
BibRef
Earlier:
Subspace Distribution Alignment for Unsupervised Domain Adaptation,
BMVC15(xx-yy).
DOI Link
1601
BibRef
Earlier:
From Virtual to Reality:
Fast Adaptation of Virtual Object Detectors to Real Domains,
BMVC14(xx-yy).
HTML Version.
1410
BibRef
Russakovsky, O.[Olga],
Deng, J.[Jia],
Huang, Z.H.[Zhi-Heng],
Berg, A.C.[Alexander C.],
Fei-Fei, L.[Li],
Detecting Avocados to Zucchinis:
What Have We Done, and Where Are We Going?,
ICCV13(2064-2071)
IEEE DOI
1403
categorical object detection.
BibRef
Ehlers, A.[Arne],
Scheuermann, B.[Björn],
Baumann, F.[Florian],
Rosenhahn, B.[Bodo],
Cleaning Up Multiple Detections Caused by Sliding Window Based Object
Detectors,
CIARP13(I:456-463).
Springer DOI
1311
BibRef
Tan, T.N.[Tie-Niu],
Huang, Y.Z.[Yong-Zhen],
Zhang, J.G.[Jun-Ge],
Recent Progress on Object Classification and Detection,
CIARP13(II:1-8).
Springer DOI
1311
BibRef
Nalpantidis, L.[Lazaros],
Großmann, B.[Bjarne],
Krüger, V.[Volker],
Fast and Accurate Unknown Object Segmentation for Robotic Systems,
ISVC13(II:318-327).
Springer DOI
1311
BibRef
Ren, X.F.[Xiao-Feng],
Ramanan, D.[Deva],
Histograms of Sparse Codes for Object Detection,
CVPR13(3246-3253)
IEEE DOI
1309
Feature Learning; Object Detection; Sparse Coding; Supervised Training
multiple features, beyond HoGradients.
BibRef
Guo, X.[Xin],
Liu, D.[Dong],
Jou, B.[Brendan],
Zhu, M.J.[Mo-Jun],
Cai, A.N.[An-Ni],
Chang, S.F.[Shih-Fu],
Robust Object Co-detection,
CVPR13(3206-3213)
IEEE DOI
1309
Objects of same category from a pool of similar objects.
BibRef
Scharfenberger, C.[Christian],
Waslander, S.L.[Steven L.],
Zelek, J.S.[John S.],
Clausi, D.A.[David A.],
Existence Detection of Objects in Images for Robot Vision Using
Saliency Histogram Features,
CRV13(75-82)
IEEE DOI
1308
Feature extraction
BibRef
Li, Y.[Yali],
He, F.[Fei],
Lu, W.H.[Wen-Hao],
Wang, S.J.[Sheng-Jin],
Combining Fast Extracted Edge Descriptors and Feature Sharing for Rapid
Object Detection,
DTCE12(II:478-490).
Springer DOI
1304
BibRef
Bria, A.[Alessandro],
Marrocco, C.[Claudio],
Molinara, M.[Mario],
Tortorella, F.[Francesco],
A ranking-based cascade approach for unbalanced data,
ICPR12(3439-3442).
WWW Link.
1302
Use ranking rather than simply error.
BibRef
Martelli, S.[Samuele],
Cristani, M.[Marco],
Bazzani, L.[Loris],
Tosato, D.[Diego],
Murino, V.[Vittorio],
Joining feature-based and similarity-based pattern description
paradigms for object detection,
ICPR12(2702-2705).
WWW Link.
1302
BibRef
Dai, J.F.[Ji-Feng],
Feng, J.J.[Jian-Jiang],
Zhou, J.[Jie],
Mining sub-categories for object detection,
ICPR12(3260-3263).
WWW Link.
1302
BibRef
Zhang, J.G.[Jun-Ge],
Zhao, X.[Xin],
Huang, Y.Z.[Yong-Zhen],
Huang, K.Q.[Kai-Qi],
Tan, T.N.[Tie-Niu],
Semantic windows mining in sliding window based object detection,
ICPR12(3264-3267).
WWW Link.
1302
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Kusuma, G.P.[Gede Putra],
Szabo, A.[Attila],
Li, Y.Q.[Yi-Qun],
Lee, J.A.[Jimmy Addison],
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subsequence,
ICPR12(3668-3671).
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1302
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Hartl, A.[Andreas],
Reitmayr, G.[Gerhard],
Rectangular target extraction for mobile augmented reality applications,
ICPR12(81-84).
WWW Link.
1302
BibRef
Singh, S.[Saurabh],
Gupta, A.[Abhinav],
Efros, A.A.[Alexei A.],
Unsupervised Discovery of Mid-Level Discriminative Patches,
ECCV12(II: 73-86).
Springer DOI
1210
patches, like parts of objects.
BibRef
Russakovsky, O.[Olga],
Lin, Y.Q.[Yuan-Qing],
Yu, K.[Kai],
Fei-Fei, L.[Li],
Object-Centric Spatial Pooling for Image Classification,
ECCV12(II: 1-15).
Springer DOI
1210
Object centered spatial. Infer location, use that to get properties of
object and background
BibRef
Russakovsky, O.[Olga],
Ng, A.Y.[Andrew Y.],
A Steiner tree approach to efficient object detection,
CVPR10(1070-1077).
IEEE DOI
1006
BibRef
Dubout, C.[Charles],
Fleuret, F.[François],
Accelerated Training of Linear Object Detectors,
SPTLI13(572-577)
IEEE DOI
1309
BibRef
Earlier:
Exact Acceleration of Linear Object Detectors,
ECCV12(III: 301-311).
Springer DOI
1210
BibRef
Hoiem, D.[Derek],
Chodpathumwan, Y.[Yodsawalai],
Dai, Q.[Qieyun],
Diagnosing Error in Object Detectors,
ECCV12(III: 340-353).
Springer DOI
1210
BibRef
Doulamis, N.D.[Nikolaos D.],
Doulamis, A.D.[Anastasios D.],
Fast and Adaptive Deep Fusion Learning for Detecting Visual Objects,
Concept12(III: 345-354).
Springer DOI
1210
BibRef
Cao, L.[Lu],
Kobayashi, Y.[Yoshinori],
Kuno, Y.[Yoshinori],
A Spatial-based Approach for Groups of Objects,
ISVC12(II: 597-608).
Springer DOI
1209
locating several identical objects grouped together.
BibRef
Nasse, F.[Fabian],
Fink, G.A.[Gernot A.],
A Bottom-up Approach for Learning Visual Object Detection Models from
Unreliable Sources,
DAGM12(488-497).
Springer DOI
1209
BibRef
Verschae, R.[Rodrigo],
Ruiz-del-Solar, J.[Javier],
TCAS: A Multiclass Object Detector for Robot and Computer Vision
Applications,
ISVC12(I: 632-641).
Springer DOI
1209
BibRef
Prest, A.[Alessandro],
Leistner, C.[Christian],
Civera, J.[Javier],
Schmid, C.[Cordelia],
Ferrari, V.[Vittorio],
Learning object class detectors from weakly annotated video,
CVPR12(3282-3289).
IEEE DOI
1208
BibRef
Liu, K.[Kun],
Wang, Q.[Qing],
Driever, W.[Wolfgang],
Ronneberger, O.[Olaf],
2D/3D rotation-invariant detection using equivariant filters and kernel
weighted mapping,
CVPR12(917-924).
IEEE DOI
1208
BibRef
Wang, X.Y.[Xiao-Yu],
Hua, G.[Gang],
Han, T.X.[Tony X.],
Detection by detections: Non-parametric detector adaptation for a video,
CVPR12(350-357).
IEEE DOI
1208
Trained object detector.
BibRef
Neugebauer, C.,
Cameron-Jones, M.,
Horton, M.,
Learnt combination in object detector ensembles,
IVCNZ10(1-8).
IEEE DOI
1203
BibRef
Quast, K.[Katharina],
Seeger, C.[Christoph],
Trivedi, M.M.[Mohan M.],
Kaup, A.[Andre],
Boosting based object detection using a geometric model,
ICIP11(3569-3572).
IEEE DOI
1201
BibRef
Zhao, X.Y.[Xin-Yue],
Satoh, Y.,
Takauji, H.,
Kaneko, S.,
Iwata, K.,
Ozaki, R.,
Robust adapted object detection under complex environment,
AVSBS11(261-266).
IEEE DOI
1111
BibRef
Porikli, F.M.,
Ozkan, H.,
Data driven frequency mapping for computationally scalable object
detection,
AVSBS11(30-35).
IEEE DOI
1111
BibRef
Smirnov, P.[Pavel],
Semenov, P.[Piotr],
Redkin, A.[Alexander],
Chun, A.[Anthony],
Toward Accurate Feature Detectors Performance Evaluation,
CVS11(51-60).
Springer DOI
1109
BibRef
Zhang, G.X.[Gao-Xiang],
Jiang, F.[Feng],
Zhao, D.B.[De-Bin],
Sun, X.S.[Xiao-Shuai],
Liu, S.H.[Shao-Hui],
Saliency Detection: A Self-Adaption Sparse Representation Approach,
ICIG11(461-465).
IEEE DOI
1109
BibRef
Chen, G.[Guang],
Han, T.X.[Tony X.],
Lao, S.H.[Shi-Hong],
Adapting an object detector by considering the worst case:
A conservative approach,
CVPR11(1369-1376).
IEEE DOI
1106
BibRef
Kim, H.C.[Hyun-Cheol],
Kim, W.Y.[Whoi-Yul],
Salient Region Detection Using Discriminative Feature Selection,
ACIVS11(305-315).
Springer DOI
1108
BibRef
Xiong, J.[Jian],
Nguyen, T.M.[Thanh Minh],
Wu, Q.M.J.[Q.M. Jonathan],
FPGA Implementation of Blob Recognition,
CRV11(125-131).
IEEE DOI
1105
BibRef
Zhang, Z.M.[Zi-Ming],
Huang, J.W.[Jia-Wei],
Li, Z.N.[Ze-Nian],
Learning Sparse Features On-Line for Image Classification,
ICIAR11(I: 122-131).
Springer DOI
1106
BibRef
Chiusano, G.[Gabriele],
Staglianò, A.[Alessandra],
Basso, C.[Curzio],
Verri, A.[Alessandro],
DCE-MRI Analysis Using Sparse Adaptive Representations,
MLMI11(67-74).
Springer DOI
1109
BibRef
Staglianò, A.[Alessandra],
Chiusano, G.[Gabriele],
Basso, C.[Curzio],
Santoro, M.[Matteo],
Learning Adaptive and Sparse Representations of Medical Images,
MCV10(130-140).
Springer DOI
1009
Sparse coding by learning dictionaries of features.
BibRef
Semenovich, D.[Dimitri],
Sowmya, A.[Arcot],
Geometry Aware Local Kernels for Object Recognition,
ACCV10(I: 490-503).
Springer DOI
1011
BibRef
Li, H.Y.[Hong-Yu],
Chen, L.[Lei],
Removal of false positive in object detection with contour-based
classifiers,
ICIP10(3941-3944).
IEEE DOI
1009
after Haar-based detection.
BibRef
Schindler, A.[Andreas],
Maier, G.[Georg],
Object detection in gray scale images based on invariant polynomial
features,
ICIP10(4633-4636).
IEEE DOI
1009
BibRef
Petit, F.[Frederic],
Capelle-Laize, A.S.[Anne-Sophie],
Carre, P.[Philippe],
Hue-based quaternionic criterion for focused-color extraction,
ICIP10(1617-1620).
IEEE DOI
1009
Extract specific colored region.
BibRef
Liu, J.[Jiamin],
White, J.M.[Jacob M.],
Summers, R.M.[Ronald M.],
Automated detection of blob structures by Hessian analysis and object
scale,
ICIP10(841-844).
IEEE DOI
1009
BibRef
Ming, A.[Anlong],
Ma, H.[Huadong],
A blob detector in color images,
CIVR07(364-370).
DOI Link
0707
BibRef
Gao, K.[Ke],
Zhang, Y.D.[Yong-Dong],
Zhang, W.[Wei],
Lin, S.X.[Shou-Xun],
Affine Stable Characteristic based sample expansion for object
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CIVR10(422-429).
DOI Link
1007
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Kobayashi, J.[Junya],
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Detection of Abnormal Objects in a Scene Based on Local Features,
MVA09(34-).
PDF File.
0905
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Su, J.Y.[Jing-Yong],
Zhu, Z.Q.[Zhi-Qiang],
Srivastava, A.[Anuj],
Huffer, F.[Fred],
Detection of Shapes in 2D Point Clouds Generated from Images,
ICPR10(2640-2643).
IEEE DOI
1008
BibRef
Cho, M.[Minsu],
Shin, Y.M.[Young Min],
Lee, K.M.[Kyoung Mu],
Unsupervised detection and segmentation of identical objects,
CVPR10(1617-1624).
IEEE DOI Video of talk:
WWW Link.
1006
Grow from local feature matches.
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Liu, H.R.[Hai-Rong],
Yan, S.C.[Shui-Cheng],
Efficient structure detection via random consensus graph,
CVPR12(574-581).
IEEE DOI
1208
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And:
Common visual pattern discovery via spatially coherent correspondences,
CVPR10(1609-1616).
IEEE DOI Video of talk:
WWW Link.
1006
local features and spatial arrangements
Not simple blobs, but more complex structures.
BibRef
Pham, M.T.[Minh-Tri],
Gao, Y.[Yang],
Hoang, V.D.D.[Viet-Dung D.],
Cham, T.J.[Tat-Jen],
Fast polygonal integration and its application in extending Haar-like
features to improve object detection,
CVPR10(942-949).
IEEE DOI
1006
Fast technique for arbitrary polygon, not just rectangular window.
BibRef
Lehmann, A.[Alain],
Leibe, B.[Bastian],
Van Gool, L.J.[Luc J.],
Feature-centric Efficient Subwindow Search,
ICCV09(940-947).
IEEE DOI
0909
Searching in object detection.
See also Efficient Subwindow Search: A Branch and Bound Framework for Object Localization.
BibRef
Nie, Q.[Qing],
Li, W.M.[Wei-Ming],
Zhan, S.Y.[Shou-Yi],
Classification Based on SPACT and Visual Saliency,
CISP09(1-5).
IEEE DOI
0910
Modified spatial PACT as local feature descriptor.
BibRef
Gao, J.M.[Jing-Min],
Sun, Y.[Yan],
The Jag-Wave Feature Detection in 2D Images,
CISP09(1-5).
IEEE DOI
0910
BibRef
Nguyen, T.B.[Thanh Binh],
Chung, S.T.[Sun Tae],
An Improved Real-Time Blob Detection for Visual Surveillance,
CISP09(1-5).
IEEE DOI
0910
BibRef
Wang, A.L.[Ai-Li],
Liu, P.G.[Pi-Gang],
Chen, Y.S.[Yu-Shi],
Multiwavelet-Based Region of Interest Image Coding,
CISP09(1-4).
IEEE DOI
0910
BibRef
Kumar, P.[Praveen],
Palaniappan, K.[Kannappan],
Mittal, A.[Ankush],
Seetharaman, G.[Guna],
Parallel Blob Extraction Using the Multi-core Cell Processor,
ACIVS09(320-332).
Springer DOI
0909
BibRef
Vacura, M.[Miroslav],
Svatek, V.[Vojtech],
Saathoff, C.[Carsten],
Franz, T.[Thomas],
Troncy, R.[Raphael],
Describing low-level image features using the COMM ontology,
ICIP08(49-52).
IEEE DOI
0810
Extract low level features with COMM rather than MPEG-7 standard.
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Li, Z.D.[Zhi-Dong],
Chen, J.[Jing],
On Semantic Object Detection with Salient Feature,
ISVC08(II: 782-791).
Springer DOI
0812
BibRef
Emaminejad, A.,
Brookes, M.,
FEUDOR: Feature Extraction Using Distinctive Octagonal Regions,
BMVC08(xx-yy).
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0809
BibRef
Mahmood, A.[Arif],
Structure-less object detection using AdaBoost algorithm,
ICMV07(85-90).
IEEE DOI
0712
BibRef
Chin, B.[Barret],
Zhang, M.J.[Meng-Jie],
Object Detection Using Neural Networks and Genetic Programming,
EvoIASP08(xx-yy).
Springer DOI
0804
BibRef
Baró, X.[Xavier],
Vitrià, J.[Jordi],
Weighted Dissociated Dipoles: An Extended Visual Feature Set,
CVS08(xx-yy).
Springer DOI
0805
representation based on Haar-like filters for use in classification.
BibRef
Baró, X.[Xavier],
Vitrià, J.[Jordi],
Evolutionary Object Detection by Means of Naïve Bayes
Models Estimation,
EvoIASP08(xx-yy).
Springer DOI
0804
BibRef
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Chapter on 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture continues in
Spot Detection, Bright Spots .