7.1.7 Feature, Object and Blob Detection Systems

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

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

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
BibRef

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
BibRef

Kerekes, J.P.[John P.],
Hyperspectral remote sensing subpixel object detection performance,
AIPR11(1-4).
IEEE DOI 1204
BibRef

Tzafestas, C.S.[Costas S.], Maragos, P.[Petros],
Shape Connectivity: Multiscale Analysis and Application to Generalized Granulometries,
JMIV(17), No. 2, September 2002, pp. 109-129.
DOI Link 0211
BibRef

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], Monasse, P.[Pascal],
Grain Filters,
JMIV(17), No. 3, November 2002, pp. 249-270.
DOI Link 0211
BibRef

Lee, K.M.[Kyoung-Mi], Street, W.N.[W. Nick],
Model-based detection, segmentation, and classification for image analysis using on-line shape learning,
MVA(13), No. 4, 2003, pp. 222-233.
WWW Link. 0304
BibRef

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
BibRef

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

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
BibRef

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
BibRef

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
WWW Link. BibRef 0702

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

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
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 Orientation,
MultMed(11), No. 5, 2009, pp. 892-905.
IEEE DOI 0907
BibRef

Gopalakrishnan, V.[Viswanath], Hu, Y.Q.[Yi-Qun], Rajan, D.[Deepu],
Random Walks on Graphs for Salient Object Detection in Images,
IP(19), No. 12, December 2010, pp. 3232-3242.
IEEE DOI 1011
BibRef
Earlier:
Random walks on graphs to model saliency in images,
CVPR09(1698-1705).
IEEE DOI 0906
BibRef

Gopalakrishnan, V.[Viswanath], Rajan, D.[Deepu], Hu, Y.Q.[Yi-Qun],
A Linear Dynamical System Framework for Salient Motion Detection,
CirSysVideo(22), No. 5, May 2012, pp. 683-692.
IEEE DOI 1202
BibRef
Earlier: A1, A3, A2:
Sustained Observability for Salient Motion Detection,
ACCV10(III: 732-743).
Springer DOI 1011
BibRef
And: A1, A3, A2:
Unsupervised Feature Selection for Salient Object Detection,
ACCV10(II: 15-26).
Springer DOI 1011
BibRef

Tuytelaars, T.[Tinne], Lampert, C.H.[Christoph H.], Blaschko, M.B.[Matthew B.], Buntine, W.[Wray],
Unsupervised Object Discovery: A Comparison,
IJCV(88), No. 2, June 2010, pp. xx-yy.
Springer DOI 1003
BibRef

Sharmanska, V.[Viktoriia], Quadrianto, N.[Novi], Lampert, C.H.[Christoph H.],
Augmented Attribute Representations,
ECCV12(V: 242-255).
Springer DOI 1210
BibRef

Ozdemir, B.[Bahadir], Aksoy, S.[Selim], Eckert, S.[Sandra], Pesaresi, M.[Martino], 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.
WWW Link. 1011
BibRef

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],
A scalable, high-precision, and low-noise detector of shift-invariant image locations,
PRL(32), No. 2, 15 January 2011, pp. 145-152.
Elsevier DOI 1101
Feature detection; Shift invariance; Multi-scale processing; Image-to-data structures processing BibRef

Lemaitre, C., Perdoch, M., Rahmoune, A., Matas, J.G., Miteran, J.,
Detection and matching of curvilinear structures,
PR(44), No. 7, July 2011, pp. 1514-1527.
Elsevier DOI 1103
Curvilinear structures; Wiry objects; Descriptor; Detector; Segmentation; Matching BibRef

Lemaitre, C.[Cédric], Miteran, J.[Johel], Matas, J.G.[Jiri G.],
Definition of a Model-Based Detector of Curvilinear Regions,
CAIP07(686-693).
Springer DOI 0708
BibRef

Murray, P., Marshall, S.,
A New Design Tool for Feature Extraction in Noisy Images Based on Grayscale Hit-or-Miss Transforms,
IP(20), No. 7, July 2011, pp. 1938-1948.
IEEE DOI 1107
BibRef

Vedaldi, A.[Andrea], Zisserman, A.[Andrew],
Efficient Additive Kernels via Explicit Feature Maps,
PAMI(34), No. 3, March 2012, pp. 480-492.
IEEE DOI 1201
BibRef
Earlier: CVPR10(3539-3546).
IEEE DOI 1006
BibRef

Vempati, S.[Sreekanth], Vedaldi, A.[Andrea], Zisserman, A.[Andrew], Jawahar, C.V.,
Generalized Rbf feature maps for Efficient Detection,
BMVC10(xx-yy).
HTML Version. 1009
BibRef

Vedaldi, A.[Andrea], Zisserman, A.[Andrew],
Sparse kernel approximations for efficient classification and detection,
CVPR12(2320-2327).
IEEE DOI 1208
BibRef

Vedaldi, A.[Andrea], Gulshan, V.[Varun], Varma, M.[Manik], Zisserman, A.[Andrew],
Multiple kernels for object detection,
ICCV09(606-613).
IEEE DOI 0909

See also Learning The Discriminative Power-Invariance Trade-Off. BibRef

Chatfield, K.[Ken], Simonyan, K.[Karen], Vedaldi, A.[Andrea], Zisserman, A.[Andrew],
Return of the Devil in the Details: Delving Deep into Convolutional Nets,
BMVC14(xx-yy).
HTML Version. 1410
BibRef

Chatfield, K.[Ken], Lempitsky, V.[Victor], Vedaldi, A.[Andrea], Zisserman, A.[Andrew],
The devil is in the details: An evaluation of recent feature encoding methods,
BMVC11(xx-yy).
HTML Version. 1110
Award, BMVC, HM Poster. BibRef

Ferraz, L.[Luis], Binefa, X.[Xavier],
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 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,
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], Otsu, N.[Nobuyuki],
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],
Hessian-Based Affine Adaptation of Salient Local Image Features,
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 Modality,
DICTA11(217-224).
IEEE DOI 1205
BibRef

Yoo, J.C., Ahn, C.W.,
Image matching using peak signal-to-noise ratio-based occlusion detection,
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,
VC(29), No. 9, September 2013, pp. 861-870.
WWW Link. 1307
BibRef

Zhang, X.[Xin], Yang, Y.H.[Yee-Hong], Han, Z.G.[Zhi-Guang], Wang, H.[Hui], Gao, C.[Chao],
Object class detection: A survey,
Surveys(46), No. 1, October 2013, pp. Article No 10.
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
BibRef
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.S.[Yasu-Shi], Iizuka, T.[Takaaki],
Improving light marker accuracy on camera images,
SPIE(Newsroom), February 18, 2014
DOI Link 1402
A novel method determines precise boundaries of the light markers used to find the center of a target in image processing applications. 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
BibRef
Earlier:
Exploiting Features: Locally Interleaved Sequential Alignment for Object Detection,
ACCV12(I:446-459).
Springer DOI 1304
Computational modeling BibRef

Cheng, G.[Gong], Han, J.W.[Jun-Wei], Zhou, P.C.[Pei-Cheng], Guo, L.[Lei],
Multi-class geospatial object detection and geographic image classification based on collection of part detectors,
PandRS(98), No. 1, 2014, pp. 119-132.
Elsevier DOI 1411
Geospatial object detection. Find specific objects or spatial pattern. For high resolution remote sensing applications. BibRef

Cheng, G.[Gong], Han, J.W.[Jun-Wei], Guo, L.[Lei], Liu, T.M.[Tian-Ming],
Learning coarse-to-fine sparselets for efficient object detection and scene classification,
CVPR15(1173-1181)
IEEE DOI 1510
BibRef

Peng, X.M.[Xiao-Ming],
Combine color and shape in real-time detection of texture-less objects,
CVIU(135), No. 1, 2015, pp. 31-48.
Elsevier DOI 1504
Real-time texture-less object detection BibRef

Chong, N.S.[Nguan Soon], Kho, Y.H.[Yau Hee], Wong, M.L.D.[Mou Ling Dennis],
Visual detection in omnidirectional view sensors,
SIViP(9), No. 4, May 2015, pp. 923-940.
Springer DOI 1504
BibRef

Diebold, J.[Julia], Tari, S.[Sibel], Cremers, D.[Daniel],
The Role of Diffusion in Figure Hunt Games,
JMIV(52), No. 1, May 2015, pp. 108-123.
Springer DOI 1505
Finding waldo. BibRef

Han, X.H.[Xian-Hua], Chen, Y.W.[Yen-Wei], Xu, G.[Gang],
High-Order Statistics of Weber Local Descriptors for Image Representation,
Cyber(45), No. 6, June 2015, pp. 1180-1193.
IEEE DOI 1506
Adaptation models BibRef

Han, X.H.[Xian-Hua], Chen, Y.W.[Yen-Wei],
HEp-2 Staining Pattern Recognition Using Stacked Fisher Network for Encoding Weber Local Descriptor,
PR(63), No. 1, 2017, pp. 542-550.
Elsevier DOI 1612
HEp-2 image representation BibRef
Earlier: Add A3: Xu, G.[Gang], MLMI15(85-93).
Springer DOI 1511
BibRef

Gao, L.[Lianru], Yang, B.[Bin], Du, Q.[Qian], Zhang, B.[Bing],
Adjusted Spectral Matched Filter for Target Detection in Hyperspectral Imagery,
RS(7), No. 6, 2015, pp. 6611.
DOI Link 1507
BibRef

Pedersoli, M.[Marco], Vedaldi, A.[Andrea], Gonzàlez, J.[Jordi], Roca, F.X.[F. Xavier],
A coarse-to-fine approach for fast deformable object detection,
PR(48), No. 5, 2015, pp. 1844-1853.
Elsevier DOI 1502
BibRef
Earlier: A1, A2, A3, Only: CVPR11(1353-1360).
IEEE DOI 1106
Object recognition BibRef

Gonfaus, J.M.[Josep M.], Pedersoli, M.[Marco], Gonzàlez, J.[Jordi], Vedaldi, A.[Andrea], Roca, F.X.[F. Xavier],
Factorized appearances for object detection,
CVIU(138), No. 1, 2015, pp. 92-101.
Elsevier DOI 1506
Object recognition BibRef

Pedersoli, M.[Marco], Gonzàlez, J.[Jordi], Bagdanov, A.D.[Andrew D.], Villanueva, J.J.[Juan J.],
Recursive Coarse-to-Fine Localization for Fast Object Detection,
ECCV10(VI: 280-293).
Springer DOI 1009
BibRef

Santosh, K.C., Wendling, L.[Laurent], Antani, S.K.[Sameer K.], Thoma, G.R.[George R.],
Overlaid Arrow Detection for Labeling Regions of Interest in Biomedical Images,
IEEE_Int_Sys(31), No. 3, May 2016, pp. 66-75.
IEEE DOI 1606
BibRef
Earlier:
Scalable Arrow Detection in Biomedical Images,
ICPR14(3257-3262)
IEEE DOI 1412
Biomedical imaging BibRef

Hong, J.K.[Jong-Kwang], Hong, Y.W.[Yong-Won], Uh, Y.J.[Young-Jung], Byun, H.R.[Hye-Ran],
Discovering overlooked objects: Context-based boosting of object detection in indoor scenes,
PRL(86), No. 1, 2017, pp. 56-61.
Elsevier DOI 1702
Object detection BibRef

Wang, S.P.[Shi-Ping], Huang, A.P.[Ai-Ping],
Salient object detection with low-rank approximation and l2,1-norm minimization,
IVC(57), No. 1, 2017, pp. 67-77.
Elsevier DOI 1702
Background: low rank; objects: sparse. BibRef

Weinberg, G.V.,
An Invariant Sliding Window Detection Process,
SPLetters(24), No. 7, July 2017, pp. 1093-1097.
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],
Deriving scale normalisation factors for a GLoG detector,
IET-IPR(12), No. 9, September 2018, pp. 1673-1682.
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Divide and Count: Generic Object Counting by Image Divisions,
IP(28), No. 2, February 2019, pp. 1035-1044.
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.],
Object instance detection with pruned Alexnet and extended training data,
SP:IC(70), 2019, pp. 145-156.
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Object instance detection, Pruned Alexnet, Binarized normed gradient, Data extension BibRef

Wu, X.[Xin], Hong, D.[Danfeng], Ghamisi, P.[Pedram], Li, W.[Wei], Tao, R.[Ran],
MsRi-CCF: Multi-Scale and Rotation-Insensitive Convolutional Channel Features for Geospatial Object Detection,
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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,
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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],
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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],
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GeoRS(57), No. 8, August 2019, pp. 5512-5524.
IEEE DOI 1908
Remote-sensing Region-based CNN. convolutional neural nets, feature extraction, geophysical image processing, image classification, remote sensing region-based convolutional neural network (R²-CNN) BibRef

Zhang, Y.L.[Yuan-Lin], Yuan, Y.[Yuan], Feng, Y.C.[Ya-Chuang], Lu, X.Q.[Xiao-Qiang],
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)
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Wan, F.[Fang], Wei, P.X.[Peng-Xu], Han, Z.J.[Zhen-Jun], Jiao, J.B.[Jian-Bin], Ye, Q.X.[Qi-Xiang],
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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,
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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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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],
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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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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.
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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,
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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,
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Yan, J.Q.[Jiang-Qiao], Zhao, L.J.[Liang-Jin], Diao, W.H.[Wen-Hui], Wang, H.Q.[Hong-Qi], Sun, X.[Xian],
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García-Domínguez, M.[Manuel], Domínguez, C.[César], Heras, J.[Jónathan], Mata, E.[Eloy], Pascual, V.[Vico],
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AutoML, Deep learning, Object detection, Transfer learning BibRef

Chen, J.[Jin], Wu, X.X.[Xin-Xiao], Duan, L.X.[Li-Xin], Chen, L.[Lin],
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IP(30), 2021, pp. 3970-3984.
IEEE DOI 2104
Object detection, Feature extraction, Detectors, Reinforcement learning, Proposals, Task analysis. BibRef

Wang, H.S.[Hong-Song], Liao, S.C.[Sheng-Cai], Shao, L.[Ling],
AFAN: Augmented Feature Alignment Network for Cross-Domain Object Detection,
IP(30), 2021, pp. 4046-4056.
IEEE DOI 2104
Training, Object detection, Feature extraction, Detectors, Generators, Semantics, Proposals, Object detection, unsupervised domain adaptation BibRef

Guo, Y.G.[Ya-Guang], Zou, Q.[Qi], Jin, L.[Lu],
A coarse to fine network for fast and accurate object detection in high-resolution images,
IET-CV(15), No. 4, 2021, pp. 274-282.
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Fang, X.[Xian], Kuang, Z.S.[Zeng-Sheng], Zhang, R.X.[Rui-Xun], Shao, X.L.[Xiu-Li], Wang, H.P.[Hong-Peng],
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Object detection, Bounding box regression, One-stage detector, Loss function, Non-maximum suppression BibRef

Oksuz, K.[Kemal], Cam, B.C.[Baris Can], Kalkan, S.[Sinan], Akbas, E.[Emre],
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IEEE DOI 2109
Survey, Object Detection. BibRef
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Generating Positive Bounding Boxes for Balanced Training of Object Detectors,
WACV20(883-892)
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Object detection, Taxonomy, Feature extraction, Deep learning, Pipelines, Neural networks, Pattern analysis, Object detection, objective imbalance. Generators, Detectors, Training, Object detection, Sampling methods, Pipelines, Proposals BibRef

Nie, J.[Jing], Pang, Y.W.[Yan-Wei], Zhao, S.J.[Sheng-Jie], Han, J.G.[Jun-Gong], Li, X.L.[Xue-Long],
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CirSysVideo(31), No. 9, September 2021, pp. 3456-3468.
IEEE DOI 2109
Feature extraction, Detectors, Object detection, Semantics, Data mining, Computer architecture, attention mechanism BibRef

Kim, J.U.[Jung Uk], Kim, S.T.[Seong Tae], Lee, H.J.[Hong Joo], Lee, S.[Sangmin], Ro, Y.M.[Yong Man],
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CirSysVideo(31), No. 9, September 2021, pp. 3529-3543.
IEEE DOI 2109
Detectors, Uncertainty, Training, Object detection, Automobiles, Feature extraction, Task analysis, Loss gradient modulation, two-stage region-based object detection BibRef

Wang, K.P.[Kun-Peng], Cai, J.X.[Jing-Xiang], Yao, J.[Juan], Liu, P.[Peng], Zhu, Z.Q.[Zhi-Qin],
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Wang, Y.[Yu], Zhang, R.[Rui], Zhang, S.[Shuo], Li, M.[Miao], Xia, Y.Y.[Yang-Yang], Zhang, X.S.[Xi-Shan], Liu, S.L.[Shao-Li],
Domain-Specific Suppression for Adaptive Object Detection,
CVPR21(9598-9607)
IEEE DOI 2111
Degradation, Adaptation models, Convolution, Semantics, Object detection, Feature extraction, Pattern recognition BibRef

Joseph, K.J., Khan, S.[Salman], Khan, F.S.[Fahad Shahbaz], Balasubramanian, V.N.[Vineeth N.],
Towards Open World Object Detection,
CVPR21(5826-5836)
IEEE DOI 2111
Protocols, Computational modeling, Object detection, Detectors, Benchmark testing, Pattern recognition BibRef

VS, V.[Vibashan], Gupta, V.[Vikram], Oza, P.[Poojan], Sindagi, V.A.[Vishwanath A.], Patel, V.M.[Vishal M.],
MeGA-CDA: Memory Guided Attention for Category-Aware Unsupervised Domain Adaptive Object Detection,
CVPR21(4514-4524)
IEEE DOI 2111
Training, Object detection, Benchmark testing, Feature extraction, Routing, Pattern recognition BibRef

Deng, J.[Jinhong], Li, W.[Wen], Chen, Y.H.[Yu-Hua], Duan, L.X.[Li-Xin],
Unbiased Mean Teacher for Cross-domain Object Detection,
CVPR21(4089-4099)
IEEE DOI 2111
Training, Adaptation models, Computational modeling, 3G mobile communication, Estimation, Object detection BibRef

Wang, T.[Tong], Zhu, Y.[Yousong], Zhao, C.[Chaoyang], Zeng, W.[Wei], Wang, J.Q.[Jin-Qiao], Tang, M.[Ming],
Adaptive Class Suppression Loss for Long-Tail Object Detection,
CVPR21(3102-3111)
IEEE DOI 2111
Training, Adaptation models, Vocabulary, Head, Object detection, Manuals BibRef

Guo, J.Y.[Jian-Yuan], Han, K.[Kai], Wang, Y.H.[Yun-He], Wu, H.[Han], Chen, X.[Xinghao], Xu, C.J.[Chun-Jing], Xu, C.[Chang],
Distilling Object Detectors via Decoupled Features,
CVPR21(2154-2164)
IEEE DOI 2111
Knowledge engineering, Semantics, Detectors, Object detection, Feature extraction, Pattern recognition, Neck BibRef

Zhang, S.Y.[Song-Yang], Li, Z.[Zeming], Yan, S.P.[Shi-Peng], He, X.M.[Xu-Ming], Sun, J.[Jian],
Distribution Alignment: A Unified Framework for Long-tail Visual Recognition,
CVPR21(2361-2370)
IEEE DOI 2111
Deep learning, Visualization, Image segmentation, Semantics, Object detection, Pattern recognition BibRef

Guo, J.Y.[Jian-Yuan], Han, K.[Kai], Wu, H.[Han], Zhang, C.[Chao], Chen, X.H.[Xing-Hao], Xu, C.J.[Chun-Jing], Xu, C.[Chang], Wang, Y.H.[Yun-He],
Positive-Unlabeled Data Purification in the Wild for Object Detection,
CVPR21(2652-2661)
IEEE DOI 2111
Purification, Training data, Image annotation, Object detection, Detectors, Benchmark testing, Semisupervised learning BibRef

Dai, Z.G.[Zhi-Gang], Cai, B.[Bolun], Lin, Y.[Yugeng], Chen, J.[Junying],
UP-DETR: Unsupervised Pre-training for Object Detection with Transformers,
CVPR21(1601-1610)
IEEE DOI 2111
Location awareness, Training data, Object detection, Transformers, Feature extraction, Natural language processing, Pattern recognition BibRef

Tan, J.[Jingru], Lu, X.[Xin], Zhang, G.[Gang], Yin, C.Q.[Chang-Qing], Li, Q.Q.[Quan-Quan],
Equalization Loss v2: A New Gradient Balance Approach for Long-tailed Object Detection,
CVPR21(1685-1694)
IEEE DOI 2111
Training, Codes, Object detection, Benchmark testing, Boosting, Pattern recognition BibRef

Ma, Y.C.[Yu-Chen], Liu, S.T.[Song-Tao], Li, Z.[Zeming], Sun, J.[Jian],
IQDet: Instance-wise Quality Distribution Sampling for Object Detection,
CVPR21(1717-1725)
IEEE DOI 2111
Training, Visualization, Semantics, Detectors, Object detection, Mixture models, Feature extraction BibRef

Ge, Z.[Zheng], Liu, S.T.[Song-Tao], Li, Z.[Zeming], Yoshie, O.[Osamu], Sun, J.[Jian],
OTA: Optimal Transport Assignment for Object Detection,
CVPR21(303-312)
IEEE DOI 2111

WWW Link. Code, Object Detection. Training, Costs, Codes, Transportation, Estimation, Object detection BibRef

Liu, J.[Ji], Li, D.[Dong], Zheng, R.Z.[Rong-Zhang], Tian, L.[Lu], Shan, Y.[Yi],
RankDetNet: Delving into Ranking Constraints for Object Detection,
CVPR21(264-273)
IEEE DOI 2111
Location awareness, Costs, Object detection, Pattern recognition, Classification algorithms BibRef

Plaut, E.[Elad], Ben Yaacov, E.[Erez], El Shlomo, B.[Bat],
3D Object Detection from a Single Fisheye Image Without a Single Fisheye Training Image,
OmniCV21(3654-3662)
IEEE DOI 2109
Training, Solid modeling, Training data, Object detection, Detectors, Network architecture BibRef

Pardo, A.[Alejandro], Xu, M.M.[Meng-Meng], Thabet, A.[Ali], Arbeláez, P.[Pablo], Ghanem, B.[Bernard],
BAOD: Budget-Aware Object Detection,
LXCV21(1247-1256)
IEEE DOI 2109
Uncertainty, Annotations, Supervised learning, Diversity reception, Optimization methods, Object detection BibRef

Kim, S.[Songeun], Park, S.Y.[Soon-Yong],
Expandable Spherical Projection and Feature Fusion Methods for Object Detection from Fisheye Images,
MVA21(1-5)
DOI Link 2109
Image edge detection, Object detection, Feature extraction, Cameras, Distortion, Real-time systems BibRef

Jaiswal, A.[Ayush], Wu, Y.[Yue], Natarajan, P.[Pradeep], Natarajan, P.[Premkumar],
Class-agnostic Object Detection,
WACV21(918-927)
IEEE DOI 2106
Training, Visualization, Protocols, Grounding, Object detection BibRef

Fang, F.[Fen], Xu, Q.[Qianli], Li, L.Y.[Li-Yuan], Gu, Y.[Ying], Lim, J.H.[Joo-Hwee],
Detecting Objects with High Object Region Percentage,
ICPR21(7173-7180)
IEEE DOI 2105
Training, Location awareness, Shape, Costing, Object detection, Detectors, Object-region-percentage, neural network BibRef

Liu, L.Q.[Li-Qiang], Wei, S.A.[Shi-An], Jiang, L.[Long], Wang, Y.T.[Ya-Tao],
Weighted Aggregating Feature Pyramid Network for Object Detection,
CVIDL20(347-353)
IEEE DOI 2102
feature extraction, image representation, object detection, lightweight convolutional module, object detection methods, Object detection BibRef

Bai, Y., Meng, Z.,
Feature Maps Channel Augmentation for Object Detection,
CVIDL20(125-129)
IEEE DOI 2102
object detection, optimisation, optimization solution, inter-channel relationship, Attention Mechanism BibRef

Su, P.[Peng], Wang, K.[Kun], Zeng, X.Y.[Xing-Yu], Tang, S.X.[Shi-Xiang], Chen, D.P.[Da-Peng], Qiu, D.[Di], Wang, X.G.[Xiao-Gang],
Adapting Object Detectors with Conditional Domain Normalization,
ECCV20(XI:403-419).
Springer DOI 2011
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Kim, H.[Hanjae], Joung, S.[Sunghun], Kim, I.J.[Ig-Jae], Sohn, K.H.[Kwang-Hoon],
Shape-Adaptive Kernel Network for Dense Object Detection,
ICIP20(2046-2050)
IEEE DOI 2011
Kernel, Shape, Object detection, Detectors, Convolution, Feature extraction, Strain, Dense object detection, object deformation BibRef

Zhao, X.Y.[Xiang-Yun], Schulter, S.[Samuel], Sharma, G.[Gaurav], Tsai, Y.H.[Yi-Hsuan], Chandraker, M.[Manmohan], Wu, Y.[Ying],
Object Detection with a Unified Label Space from Multiple Datasets,
ECCV20(XIV:178-193).
Springer DOI 2011
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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).
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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, 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, Noise measurement BibRef

Küppers, F., Kronenberger, J., Shantia, A., Haselhoff, A.,
Multivariate Confidence Calibration for Object Detection,
SAIAD20(1322-1330)
IEEE DOI 2008
Calibration, Detectors, Object detection, Logistics, Uncertainty, Task analysis, Standards BibRef

Pato, L.V., Negrinho, R., Aguiar, P.M.Q.,
Seeing without Looking: Contextual Rescoring of Object Detections for AP Maximization,
CVPR20(14598-14606)
IEEE DOI 2008
Detectors, Feature extraction, Visualization, Context modeling, Object detection, Proposals BibRef

Guo, J., Han, K., Wang, Y., Zhang, C., Yang, Z., Wu, H., Chen, X., Xu, C.,
Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection,
CVPR20(11402-11411)
IEEE DOI 2008
Detectors, Neck, Object detection, Feature extraction, Computer architecture, Search problems, Task analysis BibRef

Shen, Y., Ji, R., Chen, Z., Hong, X., Zheng, F., Liu, J., Xu, M., Tian, Q.,
Noise-Aware Fully Webly Supervised Object Detection,
CVPR20(11323-11332)
IEEE DOI 2008
Noise measurement, Detectors, Training, Object detection, Task analysis, Data models, Proposals BibRef

Tan, M., Pang, R., Le, Q.V.,
EfficientDet: Scalable and Efficient Object Detection,
CVPR20(10778-10787)
IEEE DOI 2008
Detectors, Feature extraction, Compounds, Object detection, Image resolution, Network architecture, Optimization BibRef

Ramanathan, V., Wang, R., Mahajan, D.,
DLWL: Improving Detection for Lowshot Classes With Weakly Labelled Data,
CVPR20(9339-9349)
IEEE DOI 2008
Proposals, Training, Data models, Object detection, Standards, Predictive models BibRef

Zhu, P., Wang, H., Saligrama, V.,
Don't Even Look Once: Synthesizing Features for Zero-Shot Detection,
CVPR20(11690-11699)
IEEE DOI 2008
Detectors, Visualization, Feature extraction, Training, Semantics, Object detection, Measurement BibRef

Zhao, N., Chua, T., Lee, G.H.,
SESS: Self-Ensembling Semi-Supervised 3D Object Detection,
CVPR20(11076-11084)
IEEE DOI 2008
Object detection, Perturbation methods, Proposals, Task analysis, Training BibRef

Zheng, Y., Huang, D., Liu, S., Wang, Y.,
Cross-domain Object Detection through Coarse-to-Fine Feature Adaptation,
CVPR20(13763-13772)
IEEE DOI 2008
Feature extraction, Subspace constraints, Object detection, Detectors, Task analysis, Semantics, Prototypes BibRef

Qiu, H., Li, H., Wu, Q., Shi, H.,
Offset Bin Classification Network for Accurate Object Detection,
CVPR20(13185-13194)
IEEE DOI 2008
Object detection, Feature extraction, Focusing, Detectors, Explosions, Proposals, Entropy BibRef

Chen, C., Liu, M., Meng, X., Xiao, W., Ju, Q.,
RefineDetLite: A Lightweight One-stage Object Detection Framework for CPU-only Devices,
EDLCV20(2997-3007)
IEEE DOI 2008
Detectors, Training, Feature extraction, Object detection, Convolution, Task analysis, Computational complexity BibRef

Ren, Z., Yu, Z., Yang, X., Liu, M., Lee, Y.J., Schwing, A.G., Kautz, J.,
Instance-Aware, Context-Focused, and Memory-Efficient Weakly Supervised Object Detection,
CVPR20(10595-10604)
IEEE DOI 2008
Proposals, Object detection, Training, Memory management, Detectors, Task analysis, Face BibRef

Li, H., Wu, Z., Zhu, C., Xiong, C., Socher, R., Davis, L.S.,
Learning From Noisy Anchors for One-Stage Object Detection,
CVPR20(10585-10594)
IEEE DOI 2008
Detectors, Training, Noise measurement, Proposals, Object detection, Standards, Head BibRef

Li, J.C.[Jia-Chen], Cheng, B.[Bowen], Feris, R.[Rogerio], Xiong, J.[Jinjun], Huang, T.S.[Thomas S.], Hwu, W.M.[Wen-Mei], Shi, H.[Humphrey],
Pseudo-IoU: Improving Label Assignment in Anchor-Free Object Detection,
MAI21(2378-2387)
IEEE DOI 2109
Measurement, Training, Location awareness, Computational modeling, Object detection BibRef

Ramakrishnan, K., Panda, R., Fan, Q., Henning, J., Oliva, A., Feris, R.,
Relationship Matters: Relation Guided Knowledge Transfer for Incremental Learning of Object Detectors,
CLVision20(1009-1018)
IEEE DOI 2008
Proposals, Detectors, Knowledge engineering, Object detection, Training, Task analysis, Knowledge transfer BibRef

Farhadi, M., Ghasemi, M., Vrudhula, S., Yang, Y.,
Enabling Incremental Knowledge Transfer for Object Detection at the Edge,
LPCV20(1591-1599)
IEEE DOI 2008
Adaptation models, Object detection, Computational modeling, Knowledge transfer, Feature extraction, Image edge detection, Performance evaluation BibRef

Li, Y., Pang, Y., Shen, J., Cao, J., Shao, L.,
NETNet: Neighbor Erasing and Transferring Network for Better Single Shot Object Detection,
CVPR20(13346-13355)
IEEE DOI 2008
Feature extraction, Detectors, Object detection, Nanoelectromechanical systems, Logic gates, Semantics BibRef

Chen, C., Zheng, Z., Ding, X., Huang, Y., Dou, Q.,
Harmonizing Transferability and Discriminability for Adapting Object Detectors,
CVPR20(8866-8875)
IEEE DOI 2008
Feature extraction, Training, Object detection, Semantics, Interpolation, Detectors, Task analysis BibRef

Wang, Z., Wu, Z., Lu, J., Zhou, J.,
BiDet: An Efficient Binarized Object Detector,
CVPR20(2046-2055)
IEEE DOI 2008
Detectors, Object detection, Neural networks, Feature extraction, Mutual information, Redundancy, Quantization (signal) BibRef

Varadarajan, S.[Srikrishna], Kant, S.[Sonaal], Srivastava, M.M.[Muktabh Mayank],
Benchmark for Generic Product Detection: A Low Data Baseline for Dense Object Detection,
ICIAR20(I:30-41).
Springer DOI 2007
Generic detection, to use across applications. BibRef

Srivastava, M.M.[Muktabh Mayank],
Bag of Tricks for Retail Product Image Classification,
ICIAR20(I:71-82).
Springer DOI 2007
BibRef

Zhang, H.[Haoyang], Wang, Y.[Ying], Dayoub, F.[Feras], Sünderhauf, N.[Niko],
VarifocalNet: An IoU-aware Dense Object Detector,
CVPR21(8510-8519)
IEEE DOI 2111
Location awareness, Training, Codes, Detectors, Computer architecture, Benchmark testing BibRef

Hall, D., Dayoub, F., Skinner, J., Zhang, H., Miller, D., Corke, P., Carneiro, G., Angelova, A., Sünderhauf, N.,
Probabilistic Object Detection: Definition and Evaluation,
WACV20(1020-1029)
IEEE DOI 2006
Uncertainty, Object detection, Detectors, Probabilistic logic, Task analysis, Semantics, Robots BibRef

Huang, Z., Ke, W., Huang, D.,
Improving Object Detection with Inverted Attention,
WACV20(1294-1302)
IEEE DOI 2006
Training, Heating systems, Detectors, Feature extraction, Tensile stress, Training data, Object detection BibRef

Yang, Z., Liu, S., Hu, H., Wang, L., Lin, S.,
RepPoints: Point Set Representation for Object Detection,
ICCV19(9656-9665)
IEEE DOI 2004
Code, Object Detection.
WWW Link. object detection, object recognition, point set representation, object detection, modern object detectors, Training BibRef

Li, X.Y.[Xiao-Yan], Kan, M.[Meina], Shan, S.G.[Shi-Guang], Chen, X.L.[Xi-Lin],
Weakly Supervised Object Detection With Segmentation Collaboration,
ICCV19(9734-9743)
IEEE DOI 2004
image classification, image representation, image segmentation, learning (artificial intelligence), object detection, Pascal, Image segmentation BibRef

Zhao, Y., Price, B., Cohen, S., Gurari, D.,
Unconstrained Foreground Object Search,
ICCV19(2030-2039)
IEEE DOI 2004
image classification, image retrieval, learning (artificial intelligence), object detection, Image color analysis BibRef

Jiang, P., Hou, Q., Cao, Y., Cheng, M., Wei, Y., Xiong, H.,
Integral Object Mining via Online Attention Accumulation,
ICCV19(2070-2079)
IEEE DOI 2004
Code, Object Detection.
WWW Link. image classification, image segmentation, object detection, object recognition, integral object mining, Benchmark testing BibRef

Li, F., Mo, Z., Wang, P., Liu, Z., Zhang, J., Li, G., Hu, Q., He, X., Leng, C., Zhang, Y., Cheng, J.,
A System-Level Solution for Low-Power Object Detection,
LPCV19(2461-2468)
IEEE DOI 2004
embedded systems, learning (artificial intelligence), object detection, video surveillance, video surveillance, Neural networks BibRef

Shao, S., Li, Z., Zhang, T., Peng, C., Yu, G., Zhang, X., Li, J., Sun, J.,
Objects365: A Large-Scale, High-Quality Dataset for Object Detection,
ICCV19(8429-8438)
IEEE DOI 2004
Dataset, Object Detection. feature extraction, image annotation, image classification, image segmentation, learning (artificial intelligence), Clocks BibRef

Wu, Z., Suresh, K., Narayanan, P., Xu, H., Kwon, H., Wang, Z.,
Delving Into Robust Object Detection From Unmanned Aerial Vehicles: A Deep Nuisance Disentanglement Approach,
ICCV19(1201-1210)
IEEE DOI 2004
Code, Object Detection.
WWW Link. autonomous aerial vehicles, learning (artificial intelligence), object detection, transforms, free meta-data, UAV images, Detectors BibRef

Wang, T.[Tao], Yuan, L.[Li], Zhang, X.P.[Xiao-Peng], Feng, J.S.[Jia-Shi],
Distilling Object Detectors With Fine-Grained Feature Imitation,
CVPR19(4928-4937).
IEEE DOI 2002
BibRef

Cai, L.[Lile], Zhao, B.[Bin], Wang, Z.[Zhe], Lin, J.[Jie], Foo, C.S.[Chuan Sheng], Aly, M.S.[Mohamed Sabry], Chandrasekhar, V.[Vijay],
MaxpoolNMS: Getting Rid of NMS Bottlenecks in Two-Stage Object Detectors,
CVPR19(9348-9356).
IEEE DOI 2002
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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
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
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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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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
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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
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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
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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
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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
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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
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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
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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
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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
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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

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
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Redmon, J., Divvala, S., Girshick, R., Farhadi, A.,
You Only Look Once: Unified, Real-Time Object Detection,
CVPR16(779-788)
IEEE DOI 1612
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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
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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).
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Learning Detectors Quickly with Stationary Statistics,
ACCV14(I: 99-114).
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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).
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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
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Earlier: A1, A3, A2:
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ICPR14(2814-2819)
IEEE DOI 1412
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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
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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
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Subspace Distribution Alignment for Unsupervised Domain Adaptation,
BMVC15(xx-yy).
DOI Link 1601
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BMVC14(xx-yy).
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categorical object detection. BibRef

Ehlers, A.[Arne], Scheuermann, B.[Björn], Baumann, F.[Florian], Rosenhahn, B.[Bodo],
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Nalpantidis, L.[Lazaros], Großmann, B.[Bjarne], Krüger, V.[Volker],
Fast and Accurate Unknown Object Segmentation for Robotic Systems,
ISVC13(II:318-327).
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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],
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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)
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Feature extraction BibRef

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patches, like parts of objects. BibRef

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SPTLI13(572-577)
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Chapter on 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture continues in
Spot Detection, Bright Spots .