7.1.7 Feature, Object, Blob Detection and Spot Detection Systems

Chapter Contents (Back)
Spots. Object Detction. Spot Detection. Blob Detection. Blob Segmentation. See also Depth Object Detection, 3D Object Detection. See also One-Shot Object Detection and Segmentation. See also Object Localization. See also Small Objects, Detect Small Objects. See also Fiducial Markers Design, Detection and Analysis. See also Instance of Particular Object, Specified Object. See also Learning Object Descriptions, Object Recognition.

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 in Noisy Images,
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 8305
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 8901

van der Heijden, F., Apperloo, W., Spreeuwers, L.J.,
Numerical Optimization in Spot Detector Design,
PRL(18), No. 11-13, November 1997, pp. 1091-1097. 9806
BibRef

Noordmans, H.J., Smeulders, A.W.M.,
Detection and Characterization of Isolated and Overlapping Spots,
CVIU(70), No. 1, April 1998, pp. 23-35.
DOI Link BibRef 9804

Boccignone, G.[Giuseppe], Chianese, A.[Angelo], Picariello, A.[Antonio],
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
BibRef

Kerekes, J.P., Baum, J.E.,
Spectral imaging system analytical model for subpixel object detection,
GeoRS(40), No. 5, May 2002, pp. 1088-1101.
IEEE Top Reference. 0206
BibRef

Kerekes, J.P., Baum, J.E.,
Full-Spectrum Spectral Imaging System Analytical Model,
GeoRS(43), No. 3, March 2005, pp. 571-580.
IEEE Abstract. 0501
BibRef

Stefanou, M.S., Kerekes, J.P.,
A Method for Assessing Spectral Image Utility,
GeoRS(47), No. 6, June 2009, pp. 1698-1706.
IEEE DOI 0905
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Stefanou, M.S., Kerekes, J.P.,
Image-Derived Prediction of Spectral Image Utility for Target Detection Applications,
GeoRS(48), No. 4, April 2010, pp. 1827-1833.
IEEE DOI 1003
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Kerekes, J.P.[John P.],
Hyperspectral remote sensing subpixel object detection performance,
AIPR11(1-4).
IEEE DOI 1204
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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

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
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Caselles, V.[Vicent], Monasse, P.[Pascal],
Grain Filters,
JMIV(17), No. 3, November 2002, pp. 249-270.
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Ancona, N.[Nicola], Cicirelli, G., Stella, E.[Ettore], Distante, A.,
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Earlier:
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Ancona, N.[Nicola], Maglietta, R.[Rosalia], Stella, E.[Ettore],
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Elsevier DOI 0606
Supervised learning; Support vector machines; Generalization; Leave-one-out error; Sparse and dense data representation BibRef

d'Orazio, T., Ancona, N., Cicirelli, G., Nitti, M.,
A ball detection algorithm for real soccer image sequences,
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Lee, K.M.[Kyoung-Mi], Street, W.N.[W. Nick],
Model-based detection, segmentation, and classification for image analysis using on-line shape learning,
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Lee, K.M.[Kyoung-Mi], Street, W.N.[W. Nick],
Automatic Image Segmentation and Classification Using On-line Shape Learning,
WACV00(64-70).
IEEE DOI 0010
Finding blobs. BibRef

Pang, G.K.H., Liu, H.H.S.,
LED location beacon system based on processing of digital images,
ITS(2), No. 3, September 2001, pp. 135-150.
IEEE Abstract. 0402
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Beraldin, J.A.[J. Angelo], Blais, F.[Francois], Rioux, M.[Marc], Domey, J.[Jacques],
Position sensitive light spot detector,
US_Patent6,297,488, Oct 2, 2001
WWW Link. BibRef 0110

Sinzinger, E.D.[Eric D.],
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. BibRef

Xiao, Z.T.[Zhi-Tao], Hou, Z.X.[Zheng-Xin],
Phase based feature detector consistent with human visual system characteristics,
PRL(25), No. 10, 16 July 2004, pp. 1115-1121.
Elsevier DOI 0407
BibRef

Jiang, J.M.[Jian-Min], Weng, Y.[Ying], Li, P.J.[Peng-Jie],
Dominant colour extraction in DCT domain,
IVC(24), No. 12, 1 December 2006, pp. 1269-1277.
Elsevier DOI 0610
Dominant colour features; MPEG-7; Feature extraction in compressed domain Without decompressing. BibRef

Gonzo, L.[Lorenzo], Simoni, A.[Andrea], Gottardi, M.[Massimo], Beraldin, J.A.[J. Angelo],
System and method of light spot position and color detection,
US_Patent7,022,966, Apr 4, 2006
WWW Link. BibRef 0604

Marks, R.L.[Richard L.],
Method for color transition detection,
US_Patent7,113,193, Sep 26, 2006
WWW Link. Detect object via color BibRef 0609

Damerval, C.[Christophe], Meignen, S.[Sylvain],
Blob Detection With Wavelet Maxima Lines,
SPLetters(14), No. 1, January 2007, pp. 39-42.
IEEE DOI 0701
BibRef

Damerval, C.[Christophe], Meignen, S.[Sylvain],
Study of a Robust Feature: The Pointwise Lipschitz Regularity,
IJCV(88), No. 3, July 2010, pp. xx-yy.
Springer DOI 1003
BibRef
Earlier:
Highlight on a Feature Extracted at Fine Scales: The Pointwise Lipschitz Regularity,
SSVM09(782-794).
Springer DOI 0906
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Matei, B., Meignen, S.,
Nonlinear and Nonseparable Bidimensional Multiscale Representation Based on Cell-Average Representation,
IP(24), No. 11, November 2015, pp. 4570-4580.
IEEE DOI 1509
Approximation methods BibRef

Urbach, E.R., Roerdink, J.B.T.M.[Jos B.T.M.], Wilkinson, M.H.F.[Michael H.F.],
Connected Shape-Size Pattern Spectra for Rotation and Scale-Invariant Classification of Gray-Scale Images,
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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Land, S.[Sander], Wilkinson, M.H.F.[Michael H.F.],
A Comparison of Spatial Pattern Spectra,
ISMM09(92-103).
Springer DOI 0908
BibRef

Wilkinson, M.H.F.,
Generalized pattern spectra sensitive to spatial information,
ICPR02(I: 21-24).
IEEE DOI 0211
BibRef

Broadwater, J.[Joshua], Chellappa, R.[Rama],
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 and statistics. Compare to AMSD and ACE. BibRef

Zhang, M.J.[Meng-Jie], Bhowan, U.[Urvesh], Ny, B.[Bunna],
Genetic Programming for Object Detection: A Two-Phase Approach with an Improved Fitness Function,
ELCVIA(6), No. 1, 2007, pp. 27-43.
DOI Link 0709
Genetic programming to generate code applied in windows across the image to extract objects. BibRef

Clarke, T.A.[Timothy Alan], Wang, X.C.[Xin-Chi],
Method for identifying measuring points in an optical measuring system,
US_Patent7,184,151, Feb 27, 2007
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Gutierrez, J.A.[José A.], Armstrong, B.S.R.[Brian S.R.],
Precision Landmark Location for Machine Vision and Photogrammetry: Finding and Achieving the Maximum Possible Accuracy,
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 BibRef 0800

Bogdanova, I., Bur, A., Hugli, H.,
Visual Attention on the Sphere,
IP(17), No. 11, November 2008, pp. 1-15.
IEEE DOI 0810
HVS Attention mechinism applied to spot detection. See also Dynamic visual attention on the sphere. BibRef

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. BibRef

Gao, D.S.[Da-Shan], Han, S.H.[Sun-Hyoung], Vasconcelos, N.M.[Nuno M.],
Discriminant Saliency, the Detection of Suspicious Coincidences, and Applications to Visual Recognition,
PAMI(31), No. 6, June 2009, pp. 989-1005.
IEEE DOI 0904
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Earlier: A1, A3, Only:
Bottom-up saliency is a discriminant process,
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Earlier: A1, A3, Only:
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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
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Rosin, P.L.[Paul L.],
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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],
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Gopalakrishnan, V.[Viswanath], Hu, Y.Q.[Yi-Qun], Rajan, D.[Deepu],
Random Walks on Graphs for Salient Object Detection in Images,
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Earlier:
Random walks on graphs to model saliency in images,
CVPR09(1698-1705).
IEEE DOI 0906
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Gopalakrishnan, V.[Viswanath], Rajan, D.[Deepu], Hu, Y.Q.[Yi-Qun],
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CirSysVideo(22), No. 5, May 2012, pp. 683-692.
IEEE DOI 1202
BibRef
Earlier: A1, A3, A2:
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ACCV10(III: 732-743).
Springer DOI 1011
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And: A1, A3, A2:
Unsupervised Feature Selection for Salient Object Detection,
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Springer DOI 1011
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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
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Sharmanska, V.[Viktoriia], Quadrianto, N.[Novi], Lampert, C.H.[Christoph H.],
Augmented Attribute Representations,
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Smal, I., Loog, M., Niessen, W.J., Meijering, E.H.W.,
Quantitative Comparison of Spot Detection Methods in Fluorescence Microscopy,
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IEEE DOI 1002
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Ozdemir, B.[Bahadir], Aksoy, S.[Selim], Eckert, S.[Sandra], Pesaresi, M.[Martino], Ehrlich, D.[Daniele],
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 modeling; Multi-criteria ranking 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 Noise Corrupted Image,
IEICE(E93-D), No. 11, November 2010, pp. 3066-3075.
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Gu, Y.F.[Yan-Feng], Wang, C.[Chen], Wang, S.Z.[Shi-Zhe], Zhang, Y.[Ye],
Kernel-based regularized-angle spectral matching for target detection in hyperspectral imagery,
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 BibRef

Khachaturov, G.[Georgii],
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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,
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Murray, P., Marshall, S.,
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Vedaldi, A.[Andrea], Zisserman, A.[Andrew],
Efficient Additive Kernels via Explicit Feature Maps,
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IEEE DOI 1201
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Earlier: CVPR10(3539-3546).
IEEE DOI 1006
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Vempati, S.[Sreekanth], Vedaldi, A.[Andrea], Zisserman, A.[Andrew], Jawahar, C.V.,
Generalized Rbf feature maps for Efficient Detection,
BMVC10(xx-yy).
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Vedaldi, A.[Andrea], Zisserman, A.[Andrew],
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CVPR12(2320-2327).
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Vedaldi, A.[Andrea], Gulshan, V.[Varun], Varma, M.[Manik], Zisserman, A.[Andrew],
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See also Learning The Discriminative Power-Invariance Trade-Off. BibRef

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Chatfield, K.[Ken], Lempitsky, V.[Victor], Vedaldi, A.[Andrea], Zisserman, A.[Andrew],
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Ferraz, L.[Luis], Binefa, X.[Xavier],
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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 objects in single images,
CVIU(116), No. 4, April 2012, pp. 484-499.
Elsevier DOI 1202
Collective-reward; Object detection; Semi-transparency; Transparency; Glass. Both transmission and reflection. BibRef

Liu, S.W.[Shang-Wang], He, D.J.[Dong-Jian], Liang, X.H.[Xin-Hong],
An Improved Hybrid Model for Automatic Salient Region Detection,
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IEEE DOI 1203
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Shi, R., Liu, Z., Du, H., Zhang, X., Shen, L.,
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Kobayashi, T.[Takumi], Otsu, N.[Nobuyuki],
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Elsevier DOI 1202
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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.
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AVSBS09(496-501).
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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],
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DICTA11(530-535).
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Umakanthan, S., Denman, S., Sridharan, S., Fookes, C., Wark, T.,
Spatio Temporal Feature Evaluation for Action Recognition,
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Vidas, S., Lakemond, R.[Ruan], Denman, S., Fookes, C.[Clinton], Sridharan, S.[Sridha], Wark, T.J.,
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Pinheiro Marques, R.C.[Regis C.], Medeiros, F.N.S.[Fátima N.S.], Santos Nobre, J.[Juvencio],
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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).
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Yoo, J.C., Ahn, C.W.,
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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
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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,
VC(29), No. 9, September 2013, pp. 861-870.
WWW Link. 1307
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Zhang, X.[Xin], Yang, Y.H.[Yee-Hong], Han, Z.G.[Zhi-Guang], Wang, H.[Hui], Gao, C.[Chao],
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, has become one of the most focused areas in computer vision in the new century. This article attempts to provide a comprehensive survey of the recent technical achievements. BibRef

Verdié, Y.[Yannick], Lafarge, F.[Florent],
Detecting parametric objects in large scenes by Monte Carlo sampling,
IJCV(106), No. 1, January 2014, pp. 57-75.
WWW Link. 1402
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Earlier:
Efficient Monte Carlo Sampler for Detecting Parametric Objects in Large Scenes,
ECCV12(III: 539-552).
Springer DOI 1210
Sampling rather than all points. BibRef

Niitsu, Y.[Yasushi], 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. BibRef

Yang, H.G.[Hui-Guang], Ahuja, N.[Narendra],
Automatic segmentation of granular objects in images: Combining local density clustering and gradient-barrier watershed,
PR(47), No. 6, 2014, pp. 2266-2279.
Elsevier DOI 1403
Image segmentation BibRef

Zimmermann, K.[Karel], Hurych, D.[David], Svoboda, T.[Tomáš],
Non-Rigid Object Detection with Local Interleaved Sequential Alignment (LISA),
PAMI(36), No. 4, April 2014, pp. 731-743.
IEEE DOI 1404
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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 classification based on collection of part detectors,
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IEEE DOI 1606
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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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Wang, S.[Shiping], Huang, A.[Aiping],
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IEEE DOI 1706
Adaptation models, Clutter, Detectors, Radar signal processing, Random variables, Shape, Surveillance, Constant false alarm rate (CFAR), invariance, radar detection, scale and power distributions, sliding window detector BibRef

Wang, Y., Zou, Y., Wang, W.,
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IP(27), No. 7, July 2018, pp. 3248-3263.
IEEE DOI 1805
computer vision, image classification, image reconstruction, image representation, image resolution, object density map estimation BibRef

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.
Elsevier DOI 1806
Object detection, Satellite imagery, Active learning, Distributed computing, Feature selection, Pattern recognition BibRef

Khan, M.A.U.[Mohammad A.U.], Khan, T.M.[Tariq M.], Bailey, D.G.[Donald G.], Kittaneh, O.[Omar],
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IEEE DOI 1811
Proposals, Computer architecture, Task analysis, Automobiles, Animals, Object detection, Generic-class object counting, counting with region proposals BibRef

Wang, R.[Rui], Xu, J.W.[Jing-Wen], Han, T.X.[Tony X.],
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Object instance detection, Pruned Alexnet, Binarized normed gradient, Data extension BibRef

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MsRi-CCF: Multi-Scale and Rotation-Insensitive Convolutional Channel Features for Geospatial Object Detection,
RS(10), No. 12, 2018, pp. xx-yy.
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Li, Y.[Yan], Zhang, J.[Junge], Huang, K.Q.[Kai-Qi], Zhang, J.G.[Jian-Guo],
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 BibRef

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
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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.
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Pang, J.M.[Jiang-Miao], Li, C.[Cong], Shi, J.P.[Jian-Ping], Xu, Z.H.[Zhi-Hai], Feng, H.J.[Hua-Jun],
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 (R˛-CNN) BibRef

Zhang, Y., Yuan, Y., Feng, Y., Lu, X.,
Hierarchical and Robust Convolutional Neural Network for Very 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) BibRef

Wan, F.[Fang], Wei, P.X.[Peng-Xu], Han, Z.J.[Zhen-Jun], Jiao, J.B.[Jian-Bin], Ye, Q.X.[Qi-Xiang],
Min-Entropy Latent Model for Weakly Supervised Object Detection,
PAMI(41), No. 10, October 2019, pp. 2395-2409.
IEEE DOI 1909
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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.
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Zhang, G.J.[Gong-Jie], Lu, S.[Shijian], Zhang, W.[Wei],
CAD-Net: A Context-Aware Detection Network for Objects in Remote Sensing Imagery,
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], Chen, G.Y.[Guo-Yue],
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IEICE(E103-D), No. 1, January 2020, pp. 152-162.
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Rahman, M.M., Tan, Y., Xue, J., Lu, K.,
Recent Advances in 3D Object Detection in the Era of Deep Neural Networks: A Survey,
IP(29), 2020, pp. 2947-2962.
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], Zhou, Z.L.[Zhi-Li], Yang, Y.M.[Yi-Min],
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Springer DOI 2006
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Wang, B.S.[Bi-Sheng], Cao, G.[Guo], Zhou, L.[Licun], Zhang, Y.Q.[You-Qiang], Shang, Y.F.[Yan-Feng],
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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., Ling, H.,
Object Discovery From a Single Unlabeled Image by Mining Frequent Itemsets With Multi-Scale Features,
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 BibRef

Hsu, C.C.[Cheng-Chun], Tsai, Y.H.[Yi-Hsuan], Lin, Y.Y.[Yen-Yu], Yang, M.H.[Ming-Hsuan],
Every Pixel Matters: Center-aware Feature Alignment for Domain Adaptive Object Detector,
ECCV20(IX:733-748).
Springer DOI 2011
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Kim, H.[Hanjae], Joung, S.[Sunghun], Kim, I.J.[Ig-Jae], Sohn, K.[Kwanghoon],
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
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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.
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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
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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
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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
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Arun, A.[Aditya], Jawahar, C.V., Kumar, M.P.[M. Pawan],
Dissimilarity Coefficient Based Weakly Supervised Object Detection,
CVPR19(9424-9433).
IEEE DOI 2002
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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
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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
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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
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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
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Barnea, E.[Ehud], Ben-Shahar, O.[Ohad],
Exploring the Bounds of the Utility of Context for Object Detection,
CVPR19(7404-7412).
IEEE DOI 2002
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Object Detection, Feature Fusion, Object Recognition, Convolutional Neural Networks, Deep Learning BibRef

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CVPR18(928-936)
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Detectors, Object detection, Training, Proposals, Electronics packaging, Streaming media, Cats BibRef

Kim, Y.H.[Yong-Hyun], Kang, B.N.[Bong-Nam], Kim, D.[Daijin],
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ICIP17(420-424)
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Data models, Deformable models, Detectors, Interpolation, Object detection, Pose estimation, Training, detection, gradient, normalization BibRef

Tychsen-Smith, L., Petersson, L.,
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ICCV17(428-436)
IEEE DOI 1802
convolution, deconvolution, neural nets, object detection, sampling methods, statistical distributions, BibRef

Redmon, J.[Joseph], Farhadi, A.[Ali],
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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],
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ACCV16(I: 385-399).
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CVPR16(2855-2863)
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CVPR16(761-769)
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IEEE DOI 1612
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speed up a standard sliding window detector. Detectors BibRef

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ICCV17(2363-2371)
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object detection, video signal processing, Long Short-Term Memory, association LSTM, Tools BibRef

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ICPR14(2407-2412)
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Dynamic programming BibRef

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CVPR13(3246-3253)
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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],
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Liu, Y.S.[Yi-Sheng], Chen, S.Y.[Shu-Yuan], Chao, Y.T.[Ya-Ting], Liu, R.S.[Ru-Sheng], Tsai, Y.C.[Yuan-Ching], Hsieh, J.S.[Jaw-Shu],
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Donoser, M., Bischof, H., Wiltsche, M.,
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ICIP06(757-760).
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Dupac, J.[Jan], Hlavác, V.[Václav],
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DAGM06(760-769).
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Xu, Q.[Qi], Chen, Y.Q.[Yan Qiu],
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Wang, W.X.[Wei-Xing],
Size and Shape Measure of Particles by Image Analysis,
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Lichtenauer, J.F.[Jeroen F.], Hendriks, E.A.[Emile A.], Reinders, M.J.T.[Marcel J.T.],
Isophote Properties as Features for Object Detection,
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Filters for object detection. BibRef

Forssén, P.E.[Per-Erik], Granlund, G.H.[Gösta H.],
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Mamlouk, A.M.[Amir Madany], Kim, J.T.[Jan T.], Barth, E.[Erhardt], Brauckmann, M.[Michael], Martinetz, T.[Thomas],
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Nehrbass, U., Olivo-Marin, J.C.,
Three Dimensional Spot Detection by Multiscale Analysis,
ICIP01(I: 317-320).
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Cucurachi, G.[Giorgio], Tascini, G.[Guido], Piazza, F.[Francesco],
Neural network for region detection,
CIAP97(II: 228-237).
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Cho, D.U.[Dong-Uk], Bae, J.J.,
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Davies, E.R., Barker, S.P.,
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Chapter on 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture continues in
Object Localization .


Last update:Nov 23, 2020 at 10:27:11