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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
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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
Blanford, R.P.[Ronald P.],
Tanimoto, S.L.[Steven L.],
Bright-Spot Detection in Pyramids,
CVGIP(43), No. 2, August 1988, pp. 133-149.
Elsevier DOI
BibRef
8808
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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van der Heijden, F.,
Apperloo, W.,
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Numerical Optimization in Spot Detector Design,
PRL(18), No. 11-13, November 1997, pp. 1091-1097.
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Smeulders, A.W.M.,
Detection and Characterization of Isolated and Overlapping Spots,
CVIU(70), No. 1, April 1998, pp. 23-35.
DOI Link
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Chianese, A.[Angelo],
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Multiresolution spot detection by means of entropy thresholding,
JOSA-A(17), No. 7, July 2000, pp. 1160-1171.
0008
BibRef
Olivo-Marin, J.C.[Jean-Christophe],
Extraction of spots in biological images using multiscale products,
PR(35), No. 9, September 2002, pp. 1989-1996.
Elsevier DOI
0206
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Kerekes, J.P.,
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Spectral imaging system analytical model for subpixel object detection,
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Kerekes, J.P.,
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0905
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Stefanou, M.S.,
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Image-Derived Prediction of Spectral Image Utility for Target Detection
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1003
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Kerekes, J.P.[John P.],
Hyperspectral remote sensing subpixel object detection performance,
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IEEE DOI
1204
BibRef
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Shape Connectivity:
Multiscale Analysis and Application to Generalized Granulometries,
JMIV(17), No. 2, September 2002, pp. 109-129.
DOI Link
0211
BibRef
Dougherty, E.R.[Edward R.],
Granulometric Size Density for Segmented Random-Disk Models,
JMIV(17), No. 3, November 2002, pp. 271-281.
DOI Link
0211
BibRef
Caselles, V.[Vicent],
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DOI Link
0211
BibRef
Ancona, N.[Nicola],
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Stella, E.[Ettore],
Distante, A.,
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IVC(21), No. 8, August 2003, pp. 675-692.
Elsevier DOI
0307
BibRef
Earlier:
Object detection in images run-time complexity and parameter selection
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IEEE DOI
0211
BibRef
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0606
Supervised learning; Support vector machines; Generalization;
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IEEE DOI
0010
Finding blobs.
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0402
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Sinzinger, E.D.[Eric D.],
Radial segmentation,
PRL(25), No. 12, September 2004, pp. 1337-1350.
Elsevier DOI
0409
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See also model-based approach to junction detection using radial energy, A.
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Phase based feature detector consistent with human visual system
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Elsevier DOI
0407
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Jiang, J.M.[Jian-Min],
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0610
Dominant colour features; MPEG-7; Feature extraction in compressed domain
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0701
BibRef
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1003
BibRef
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Highlight on a Feature Extracted at Fine Scales:
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SSVM09(782-794).
Springer DOI
0906
BibRef
Matei, B.,
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Nonlinear and Nonseparable Bidimensional Multiscale Representation
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1509
Approximation methods
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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
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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
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IEEE DOI
0211
BibRef
Broadwater, J.[Joshua],
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Hybrid Detectors for Subpixel Targets,
PAMI(29), No. 11, November 2007, pp. 1891-1903.
IEEE DOI
0711
In hyperspectral imagery analysis. Model background using physics
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Compare to AMSD and ACE.
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Zhang, M.J.[Meng-Jie],
Bhowan, U.[Urvesh],
Ny, B.[Bunna],
Genetic Programming for Object Detection:
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ELCVIA(6), No. 1, 2007, pp. 27-43.
DOI Link
0709
Genetic programming to generate code applied in windows across the image
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Gutierrez, J.A.[José A.],
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Springer2008, ISBN: 978-1-84628-912-5.
WWW Link.
Code, Landmarks. Techniques to achieve optimal results.
Buy this book: Precision Landmark Location for Machine Vision and Photogrammetry: Finding and Achieving the Maximum Possible Accuracy
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Bur, A.,
Hugli, H.,
Visual Attention on the Sphere,
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IEEE DOI
0810
HVS Attention mechinism applied to spot detection.
See also Dynamic visual attention on the sphere.
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Grosjean, B.[Bénédicte],
Moisan, L.[Lionel],
A-contrario Detectability of Spots in Textured Backgrounds,
JMIV(33), No. 3, March 2009, pp. xx-yy.
Springer DOI
0903
Based on human visual system analysis.
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Gao, D.S.[Da-Shan],
Han, S.H.[Sun-Hyoung],
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Discriminant Saliency, the Detection of Suspicious Coincidences, and
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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,
ICIP08(1700-1703).
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
BibRef
Gopalakrishnan, V.[Viswanath],
Hu, Y.Q.[Yi-Qun],
Rajan, D.[Deepu],
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
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Quadrianto, N.[Novi],
Lampert, C.H.[Christoph H.],
Augmented Attribute Representations,
ECCV12(V: 242-255).
Springer DOI
1210
BibRef
Smal, I.,
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Meijering, E.H.W.,
Quantitative Comparison of Spot Detection Methods in Fluorescence
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IEEE DOI
1002
BibRef
Ozdemir, B.[Bahadir],
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Elsevier DOI
1008
Performance evaluation; Object detection; Object matching; Shape
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He, C.[Chu],
Zhao, G.Y.[Guo-Ying],
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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
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WLD: differential excitation and orientation.
Apply to variety of feature detections.
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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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PRL(32), No. 2, 15 January 2011, pp. 114-119.
Elsevier DOI
1101
Hyperspectral imagery; Target detection; Spectral matched filter;
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Khachaturov, G.[Georgii],
A scalable, high-precision, and low-noise detector of shift-invariant
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Elsevier DOI
1101
Feature detection; Shift invariance; Multi-scale processing;
Image-to-data structures processing
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Lemaitre, C.,
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Rahmoune, A.,
Matas, J.G.,
Miteran, J.,
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Elsevier DOI
1103
Curvilinear structures; Wiry objects; Descriptor; Detector;
Segmentation; Matching
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Lemaitre, C.[Cédric],
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Matas, J.G.[Jiri G.],
Definition of a Model-Based Detector of Curvilinear Regions,
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Springer DOI
0708
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Murray, P.,
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A New Design Tool for Feature Extraction in Noisy Images Based on
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IP(20), No. 7, July 2011, pp. 1938-1948.
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1107
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Vedaldi, A.[Andrea],
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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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Ferraz, L.[Luis],
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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
Detection,
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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Liang, X.H.[Xin-Hong],
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.,
Zhang, X.,
Shen, L.,
Region Diversity Maximization for Salient Object Detection,
SPLetters(19), No. 4, April 2012, pp. 215-218.
IEEE DOI
1203
BibRef
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],
Sridharan, S.[Sridha],
Fookes, C.[Clinton],
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JMIV(44), No. 2, October 2012, pp. 150-167.
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],
Fookes, C.[Clinton],
Sridharan, S.[Sridha],
Negative Determinant of Hessian Features,
DICTA11(530-535).
IEEE DOI
1205
BibRef
Umakanthan, S.,
Denman, S.,
Sridharan, S.,
Fookes, C.,
Wark, T.,
Spatio Temporal Feature Evaluation for Action Recognition,
DICTA12(1-8).
IEEE DOI
1303
BibRef
Vidas, S.,
Lakemond, R.[Ruan],
Denman, S.,
Fookes, C.[Clinton],
Sridharan, S.[Sridha],
Wark, T.J.,
An Exploration of Feature Detector Performance in the Thermal-Infrared
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DICTA11(217-224).
IEEE DOI
1205
BibRef
Pinheiro Marques, R.C.[Regis C.],
Medeiros, F.N.S.[Fátima N.S.],
Santos Nobre, J.[Juvencio],
SAR Image Segmentation Based on Level Set Approach and G_A^0 Model,
PAMI(34), No. 10, October 2012, pp. 2046-2057.
IEEE DOI
1208
Using SAR image properties.
BibRef
Araujo, R.T.S.,
Medeiros, F.N.S.,
Costa, R.C.S.,
Pinheiro Marques, R.C.[Regis C.],
Moreira, R.B.,
Silva, J.L.,
Spots segmentation in SAR images for remote sensing of environment,
Southwest04(95-99).
IEEE DOI
0411
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Yoo, J.C.,
Ahn, C.W.,
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],
Wei, L.[Lu],
Hamalainen, J.,
Tirkkonen, O.,
A Blind Time-Reversal Detector in the Presence of Channel Correlation,
SPLetters(20), No. 5, May 2013, pp. 459-462.
IEEE DOI
1304
BibRef
Kong, Y.[Yan],
Dong, W.M.[Wei-Ming],
Mei, X.[Xing],
Zhang, X.P.[Xiao-Peng],
Paul, J.C.[Jean-Claude],
SimLocator: robust locator of similar objects in images,
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1612
HEp-2 image representation
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1502
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Earlier: A1, A2, A3, Only:
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IEEE DOI
1106
Object recognition
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Object recognition
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Springer DOI
1009
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IEEE DOI
1606
BibRef
Earlier:
Scalable Arrow Detection in Biomedical Images,
ICPR14(3257-3262)
IEEE DOI
1412
Biomedical imaging
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Hong, J.K.[Jong-Kwang],
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1702
Object detection
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1702
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1706
Adaptation models, Clutter, Detectors, Radar signal processing,
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IP(27), No. 7, July 2018, pp. 3248-3263.
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1805
computer vision, image classification, image reconstruction,
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Active learning for designing detectors for infrequently occurring
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1806
Object detection, Satellite imagery, Active learning,
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Khan, M.A.U.[Mohammad A.U.],
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Generalised LoG (GLoG) detector for non-circular blobs.
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IEEE DOI
1811
Proposals, Computer architecture, Task analysis, Automobiles,
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Elsevier DOI
1812
Object instance detection, Pruned Alexnet,
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MsRi-CCF: Multi-Scale and Rotation-Insensitive Convolutional Channel
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RS(10), No. 12, 2018, pp. xx-yy.
DOI Link
1901
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Li, Y.[Yan],
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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],
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Joint Sparse and Low-Rank Multi-Task Learning with Extended
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RS(11), No. 2, 2019, pp. xx-yy.
DOI Link
1902
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Qiu, H.Q.[He-Qian],
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Shi, H.C.[Heng-Can],
A2RMNet: Adaptively Aspect Ratio Multi-Scale Network for Object
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RS(11), No. 13, 2019, pp. xx-yy.
DOI Link
1907
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Pang, J.M.[Jiang-Miao],
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RR^2-CNN:
Fast Tiny Object Detection in Large-Scale Remote Sensing Images,
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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)
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Zhang, Y.,
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Hierarchical and Robust Convolutional Neural Network for Very
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IEEE DOI
1908
convolutional neural nets, feature extraction,
learning (artificial intelligence), object detection,
rotation and scaling robust enhancement (RSRE)
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Wan, F.[Fang],
Wei, P.X.[Peng-Xu],
Han, Z.J.[Zhen-Jun],
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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,
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Entropy, Training, Graphical models
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Liu, X.Y.[Xin-Yu],
Li, D.H.[Dong-Hui],
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Noncooperative Target Detection of Spacecraft Objects Based on
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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
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Li, L.[Lin],
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Zhang, G.J.[Gong-Jie],
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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
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Chen, C.,
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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
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Tong, W.Q.[Wei-Qing],
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IEEE DOI
2002
Survey, Objetc Detection. Object detection, Cameras, Sensors,
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Elsevier DOI
2006
Object detetion, Classification, Localization,
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Li, C.L.[Chuan-Long],
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2009
Separate localization and classification components.
Task differentiation, Feature fusion, Object detection, SSD
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IEEE DOI
2009
Feature extraction, Annotations, Saliency detection, Training,
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Camouflaged Object Detection and Tracking: A Survey,
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AF-EMS Detector: Improve the Multi-Scale Detection Performance of the
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García-Domínguez, M.[Manuel],
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Mata, E.[Eloy],
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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.
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Wang, H.S.[Hong-Song],
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Shao, L.[Ling],
AFAN: Augmented Feature Alignment Network for Cross-Domain Object
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IP(30), 2021, pp. 4046-4056.
IEEE DOI
2104
Training, Object detection, Feature extraction, Detectors,
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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
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Wang, K.[Kun],
Zeng, X.Y.[Xing-Yu],
Tang, S.X.[Shi-Xiang],
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Wang, X.G.[Xiao-Gang],
Adapting Object Detectors with Conditional Domain Normalization,
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Springer DOI
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Joung, S.[Sunghun],
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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
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Zhao, X.Y.[Xiang-Yun],
Schulter, S.[Samuel],
Sharma, G.[Gaurav],
Tsai, Y.H.[Yi-Hsuan],
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Wu, Y.[Ying],
Object Detection with a Unified Label Space from Multiple Datasets,
ECCV20(XIV:178-193).
Springer DOI
2011
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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.
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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
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Li, J.D.[Jun-De],
Ghosh, S.[Swaroop],
Quantum-soft Qubo Suppression for Accurate Object Detection,
ECCV20(XXIX: 158-173).
Springer DOI
2010
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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,
Computer vision, 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
Chen, Z.,
Fu, Z.,
Jiang, R.,
Chen, Y.,
Hua, X.,
SLV: Spatial Likelihood Voting for Weakly Supervised Object Detection,
CVPR20(12992-13001)
IEEE DOI
2008
Proposals, Training, Detectors, Object detection, Task analysis,
Feature extraction, Computer vision
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, Computer vision, 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
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
Fan, D.,
Ji, G.,
Sun, G.,
Cheng, M.,
Shen, J.,
Shao, L.,
Camouflaged Object Detection,
CVPR20(2774-2784)
IEEE DOI
2008
Task analysis, Object detection, Image segmentation, Measurement,
Cats, Computer vision
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
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
Chen, K.[Kean],
Li, J.G.[Jian-Guo],
Lin, W.Y.[Wei-Yao],
See, J.[John],
Wang, J.[Ji],
Duan, L.Y.[Ling-Yu],
Chen, Z.B.[Zhi-Bo],
He, C.W.[Chang-Wei],
Zou, J.[Junni],
Towards Accurate One-Stage Object Detection With AP-Loss,
CVPR19(5114-5122).
IEEE DOI
2002
BibRef
Chen, Z.M.[Zhi-Ming],
Chen, K.[Kean],
Lin, W.Y.[Wei-Yao],
See, J.[John],
Yu, H.[Hui],
Ke, Y.[Yan],
Yang, C.[Cong],
Piou Loss: Towards Accurate Oriented Object Detection in Complex
Environments,
ECCV20(V:195-211).
Springer DOI
2011
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
Zhao, M.M.[Mu-Ming],
Zhang, J.[Jian],
Zhang, C.Y.[Chong-Yang],
Zhang, W.J.[Wen-Jun],
Towards Locally Consistent Object Counting with Constrained Multi-stage
Convolutional Neural Networks,
ACCV18(VI:247-261).
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.,
Bai, Y.,
Ding, M.,
Li, Y.,
Ghanem, B.,
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
Chan, J.[Jacoob],
Lee, J.A.[Jimmy Addison],
Qian, K.[Kemao],
F-SORT: An Alternative for Faster Geometric Verification,
ACCV16(I: 385-399).
Springer DOI
1704
BibRef
And:
BORDER: An Oriented Rectangles Approach to Texture-Less Object
Recognition,
CVPR16(2855-2863)
IEEE DOI
1612
Resort image features for different orientations.
Bounding Oriented-Rectangle Descriptors for Enclosed Regions.
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.[Mojun],
Cai, A.[Anni],
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
BibRef
Kusuma, G.P.[Gede Putra],
Szabo, A.[Attila],
Li, Y.Q.[Yi-Qun],
Lee, J.A.[Jimmy Addison],
Appearance-based object recognition using weighted longest increasing
subsequence,
ICPR12(3668-3671).
WWW Link.
1302
BibRef
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
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Chapter on 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture continues in
Object Localization .