7.1.7 Feature, Object, Blob Detection and Spot Detection Systems

Chapter Contents (Back)
Spots. Object Detection. Spot Detection. Blob Detection. Blob Segmentation. See also Fiducial Markers Design, Detection and Analysis.

Sklansky, J.,
Recognition of Convex Blobs,
PR(2), No. 1, January 1970, pp. 3-10.
WWW Link. 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., and 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.
WWW Link. 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., and 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.,
Blob Extraction by Relaxation,
DARPA79(61-65). BibRef

Hong, T.H., and 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., and 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., and Rosenfeld, A.,
Detecting and Extracting Compact Textured Regions Using Pyramids,
IVC(7), No. 2, May 1989, pp. 129-134.
WWW Link. Blob Extraction. Segmentation, Blobs. BibRef 8905

Rosenfeld, A., Sher, A.C.,
Detection and Delineation of Compact Objects Using Intensity Pyramids,
PR(21), No. 2, 1988, pp. 147-151.
WWW Link. BibRef 8800

Blanford, R.P., Tanimoto, S.L.,
Bright-Spot Detection in Pyramids,
CVGIP(43), No. 2, August 1988, pp. 133-149.
WWW Link. BibRef 8808

Rewo, L.,
Enhancement and Detection of Convex Objects Using Regression Models,
CVGIP(25), No. 2, February 1984, pp. 257-269.
WWW Link. Blob Extraction. Blob detection. BibRef 8402

Blostein, D., and Ahuja, N.,
A Multi-scale Region Detector,
CVGIP(45), No. 1, January 1989, pp. 22-41.
WWW Link. 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.
WWW Link. 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
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

Ancona, N.[Nicola], Cicirelli, G., Stella, E.[Ettore], Distante, A.,
Ball detection in static images with Support Vector Machines for classification,
IVC(21), No. 8, August 2003, pp. 675-692.
WWW Link. 0307
BibRef
Earlier:
Object detection in images run-time complexity and parameter selection of support vector machines,
ICPR02(II: 426-429).
IEEE DOI 0211
BibRef

Ancona, N.[Nicola], Maglietta, R.[Rosalia], Stella, E.[Ettore],
Data representations and generalization error in kernel based learning machines,
PR(39), No. 9, September 2006, pp. 1588-1603.
WWW Link. 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,
ICPR02(I: 210-213).
IEEE DOI 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).
WWW Link. 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

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

Pagčs, J.[Jordi], Salvi, J.[Joaquim], Collewet, C.[Christophe], Forest, J.[Josep],
Optimised De Bruijn patterns for one-shot shape acquisition,
IVC(23), No. 8, 1 August 2005, pp. 707-720.
Elsevier DOI 0508
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
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.
WWW Link. 0709
Genetic programming to generate code applied in windows across the image to extract objects. BibRef

Caucci, L.[Luca], Barrett, H.H.[Harrison H.], Devaney, N.[Nicholas], Rodríguez, J.J.[Jeffrey J.],
Application of the Hotelling and ideal observers to detection and localization of exoplanets,
JOSA-A(24), No. 12, December 2007, pp. B13-B24.
WWW Link. 0801
Object detection. Planets. 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

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
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

Lampert, C.H.[Christoph H.], Blaschko, M.B.[Matthew B.], Hofmann, T.[Thomas],
Efficient Subwindow Search: A Branch and Bound Framework for Object Localization,
PAMI(31), No. 12, December 2009, pp. 2129-2142.
IEEE DOI 0911
BibRef
Earlier:
Beyond sliding windows: Object localization by efficient subwindow search,
CVPR08(1-8).
IEEE DOI 0806
Award, CVPR. Efficient search for existence of object. BibRef

Blaschko, M.B.[Matthew B.],
Branch and Bound Strategies for Non-maximal Suppression in Object Detection,
EMMCVPR11(385-398).
Springer DOI 1107
BibRef

Lampert, C.H.[Christoph H.],
An efficient divide-and-conquer cascade for nonlinear object detection,
CVPR10(1022-1029).
IEEE DOI 1006
BibRef
Earlier:
Detecting objects in large image collections and videos by efficient subimage retrieval,
ICCV09(987-994).
IEEE DOI 0909
BibRef

Blaschko, M.B.[Matthew B.], Lampert, C.H.[Christoph H.],
Object Localization with Global and Local Context Kernels,
BMVC09(xx-yy).
PDF File. 0909
BibRef
Earlier:
Learning to Localize Objects with Structured Output Regression,
ECCV08(I: 2-15).
Springer DOI 0810
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

Lampert, C.H.[Christoph H.], Nickisch, H.[Hannes], Harmeling, S.[Stefan],
Attribute-Based Classification for Zero-Shot Visual Object Categorization,
PAMI(36), No. 3, March 2014, pp. 453-465.
IEEE DOI 1403
BibRef
Earlier:
Learning to detect unseen object classes by between-class attribute transfer,
CVPR09(951-958).
IEEE DOI 0906
computer vision BibRef

Smal, I., Loog, M., Niessen, W.J., Meijering, E.H.W.,
Quantitative Comparison of Spot Detection Methods in Fluorescence Microscopy,
MedImg(29), No. 2, February 2010, pp. 282-301.
IEEE DOI 1002
BibRef

Bai, X.Z.[Xiang-Zhi], Zhou, F.[Fugen],
Analysis of new top-hat transformation and the application for infrared dim small target detection,
PR(43), No. 6, June 2010, pp. 2145-2156.
Elsevier DOI 1003
Top-hat transformation; Structuring element; Infrared dim small target; Target detection 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
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Vedaldi, A.[Andrea], Zisserman, A.[Andrew],
Efficient Additive Kernels via Explicit Feature Maps,
PAMI(34), No. 3, March 2012, pp. 480-492.
IEEE DOI 1201
BibRef
Earlier: CVPR10(3539-3546).
IEEE DOI 1006
BibRef

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

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

Vedaldi, A.[Andrea], Gulshan, V.[Varun], Varma, M.[Manik], Zisserman, A.[Andrew],
Multiple kernels for object detection,
ICCV09(606-613).
IEEE DOI 0909
See also Learning The Discriminative Power-Invariance Trade-Off. BibRef

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

Pinheiro Marques, R.C.[Regis C.], Medeiros, F.N.S.[Fátima N.S.], Santos Nobre, J.[Juvencio],
SAR Image Segmentation Based on Level Set Approach and G_A^0 Model,
PAMI(34), No. 10, October 2012, pp. 2046-2057.
IEEE DOI 1208
Using SAR image properties. BibRef

Araujo, R.T.S., Medeiros, F.N.S., Costa, R.C.S., Pinheiro Marques, R.C.[Regis C.], Moreira, R.B., Silva, J.L.,
Spots segmentation in SAR images for remote sensing of environment,
Southwest04(95-99).
WWW Link. 0411
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

Boochs, F.[Frank], Kern, F.[Fredie], Schütze, R.[Rainer], Marbs, A.[Andreas],
Approaches for geometrical and semantic modelling of huge unstructured 3D point clouds,
PFG(2009), No. 1, 2009, pp. 65-77.
WWW Link. 1211
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Boochs, F., Karmacharya, A., Marbs, A.,
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Truong, H.Q.[Hung Quoc], Ben Hmida, H.[Helmi], Marbs, A.[Andreas], Boochs, F.[Frank],
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IEEE DOI 1304
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DOI Link 1311
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Detecting parametric objects in large scenes by Monte Carlo sampling,
IJCV(106), No. 1, January 2014, pp. 57-75.
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Efficient Monte Carlo Sampler for Detecting Parametric Objects in Large Scenes,
ECCV12(III: 539-552).
Springer DOI 1210
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Niitsu, Y.[Yasushi], Iizuka, T.[Takaaki],
Improving light marker accuracy on camera images,
SPIE(Newsroom), February 18, 2014
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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,
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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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Exploiting Features: Locally Interleaved Sequential Alignment for Object Detection,
ACCV12(I:446-459).
Springer DOI 1304
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Hsiao, E.[Edward], Hebert, M.[Martial],
Occlusion Reasoning for Object Detectionunder Arbitrary Viewpoint,
PAMI(36), No. 9, September 2014, pp. 1803-1815.
IEEE DOI 1408
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Earlier: CVPR12(3146-3153).
IEEE DOI 1208
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Coherent Occlusion Reasoning for Instance Recognition,
ACPR13(1-5)
IEEE DOI 1408
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Cheng, G.[Gong], Han, J.[Junwei], Zhou, P.[Peicheng], Guo, L.[Lei],
Multi-class geospatial object detection and geographic image classification based on collection of part detectors,
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Elsevier DOI 1411
Geospatial object detection. Find specific objects or spatial pattern. For high resolution remote sensing applications. BibRef

Cheng, G.[Gong], Han, J.[Junwei], Guo, L.[Lei], Liu, T.M.[Tian-Ming],
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CVPR15(1173-1181)
IEEE DOI 1510
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Peng, X.M.[Xiao-Ming],
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Chong, N.S.[Nguan Soon], Kho, Y.H.[Yau Hee], Wong, M.L.D.[Mou Ling Dennis],
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SIViP(9), No. 4, May 2015, pp. 923-940.
Springer DOI 1504
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Diebold, J.[Julia], Tari, S.[Sibel], Cremers, D.[Daniel],
The Role of Diffusion in Figure Hunt Games,
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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,
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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
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Springer DOI 1511
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Gao, L.[Lianru], Yang, B.[Bin], Du, Q.[Qian], Zhang, B.[Bing],
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Shi, Z.Y.[Zhi-Yuan], Hospedales, T.M.[Timothy M.], Xiang, T.[Tao],
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PAMI(37), No. 10, October 2015, pp. 1959-1972.
IEEE DOI 1509
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Earlier:
Bayesian Joint Topic Modelling for Weakly Supervised Object Localisation,
ICCV13(2984-2991)
IEEE DOI 1403
Bayesian; Joint Topic Modelling; Weakly Supervised Adaptation models BibRef

Shi, Z.Y.[Zhi-Yuan], Yang, Y.X.[Yong-Xin], Hospedales, T.M.[Timothy M.], Xiang, T.[Tao],
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ECCV14(II: 472-487).
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Pedersoli, M.[Marco], Vedaldi, A.[Andrea], Gonzŕlez, J.[Jordi], Roca, F.X.[F. Xavier],
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Elsevier DOI 1502
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IEEE DOI 1106
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Gonfaus, J.M.[Josep M.], Pedersoli, M.[Marco], Gonzŕlez, J.[Jordi], Vedaldi, A.[Andrea], Roca, F.X.[F. Xavier],
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CVIU(138), No. 1, 2015, pp. 92-101.
Elsevier DOI 1506
Object recognition BibRef

Pedersoli, M.[Marco], Gonzŕlez, J.[Jordi], Bagdanov, A.D.[Andrew D.], Villanueva, J.J.[Juan J.],
Recursive Coarse-to-Fine Localization for Fast Object Detection,
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Springer DOI 1009
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Zhang, M., Wu, T., Beeman, S.C., Cullen-McEwen, L., Bertram, J.F., Charlton, J.R., Baldelomar, E., Bennett, K.M.,
Efficient Small Blob Detection Based on Local Convexity, Intensity and Shape Information,
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biomedical MRI 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
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Scalable Arrow Detection in Biomedical Images,
ICPR14(3257-3262)
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Biomedical imaging BibRef

Halawani, A.[Alaa], Li, H.B.[Hai-Bo],
100 lines of code for shape-based object localization,
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Elsevier DOI 1609
Code, Object Detection. Object detection BibRef

Cinbis, R.G.[Ramazan Gokberk], Verbeek, J.[Jakob], Schmid, C.[Cordelia],
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PAMI(39), No. 1, January 2017, pp. 189-203.
IEEE DOI 1612
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Earlier:
Multi-fold MIL Training for Weakly Supervised Object Localization,
CVPR14(2409-2416)
IEEE DOI 1409
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Earlier:
Segmentation Driven Object Detection with Fisher Vectors,
ICCV13(2968-2975)
IEEE DOI 1403
Computational efficiency. object detection; object localization; weakly supervised training. fisher vectors; object detection See also Action and Event Recognition with Fisher Vectors on a Compact Feature Set. BibRef

Hong, J.[Jongkwang], Hong, Y.[Yongwon], Uh, Y.[Youngjung], Byun, H.R.[Hye-Ran],
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Object detection BibRef

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


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VCIP16(1-4)
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Clustering algorithms BibRef

Wang, B.[Bo], Shao, J.[Jie], He, C.[Chengkun], Hu, G.[Gang], Xu, X.[Xing],
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Lokoc, J.[Jakub], Kubon, D.[David], Blažek, A.[Adam],
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MMMod17(II: 3-14).
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Huberman, I.[Inbar], Fattal, R.[Raanan],
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CVPR16(2903-2911)
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ACCV16(I: 385-399).
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BORDER: An Oriented Rectangles Approach to Texture-Less Object Recognition,
CVPR16(2855-2863)
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Hoffman, J.[Judy], Gupta, S.[Saurabh], Darrell, T.J.[Trevor J.],
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Shrivastava, A., Gupta, A., Girshick, R.[Ross],
Training Region-Based Object Detectors with Online Hard Example Mining,
CVPR16(761-769)
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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)
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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, C.[Cewu], Lu, Y.Y.[Yong-Yi], Chen, H.[Hao], Tang, C.K.[Chi-Keung],
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ICCV15(2560-2568)
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Find square objects. 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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Accurate Object Detection with Location Relaxation and Regionlets Re-localization,
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Springer DOI 1504
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Feng, Y.[Youji], Wu, Y.H.[Yi-Hong], Fan, L.X.[Li-Xin],
Online Learning of Binary Feature Indexing for Real-Time SLAM Relocalization,
BD3DCV14(206-217).
Springer DOI 1504
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MMMod15(I: 234-245).
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Frintrop, S.[Simone], Garcia, G.M.[German Martin], Cremers, A.B.[Armin B.],
A Cognitive Approach for Object Discovery,
ICPR14(2329-2334)
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Ma, K.[Kai], Ben-Arie, J.[Jezekiel],
Compound Exemplar Based Object Detection by Incremental Random Forest,
ICPR14(2407-2412)
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Dynamic programming BibRef

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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)
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Mittal, S.[Sudhandhu], Karthik, M.S.[M. Siva], Kumar, S.[Suryansh], Krishna, K.M.[K. Madhava],
Small Object Discovery and Recognition Using Actively Guided Robot,
ICPR14(4334-4339)
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Accuracy BibRef

Bilen, H.[Hakan], Pedersoli, M.[Marco], Tuytelaars, T.[Tinne],
Weakly supervised object detection with convex clustering,
CVPR15(1081-1089)
IEEE DOI 1510
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Earlier:
Weakly Supervised Detection with Posterior Regularization,
BMVC14(xx-yy).
HTML Version. 1410
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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,
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Springer DOI 1611
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Earlier:
Subspace Distribution Alignment for Unsupervised Domain Adaptation,
BMVC15(xx-yy).
DOI Link 1601
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Earlier:
From Virtual to Reality: Fast Adaptation of Virtual Object Detectors to Real Domains,
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Russakovsky, O.[Olga], Deng, J.[Jia], Huang, Z.H.[Zhi-Heng], Berg, A.C.[Alexander C.], Fei-Fei, L.[Li],
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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,
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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

Li, Y.[Yali], He, F.[Fei], Lu, W.H.[Wen-Hao], Wang, S.J.[Sheng-Jin],
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Springer DOI 1304
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Zhou, C.L.[Chun-Luan], Yuan, J.S.[Jun-Song],
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BMVC14(xx-yy).
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Earlier:
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ACCV12(I:71-84).
Springer DOI 1304
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Bria, A.[Alessandro], Marrocco, C.[Claudio], Molinara, M.[Mario], Tortorella, F.[Francesco],
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Martelli, S.[Samuele], Cristani, M.[Marco], Bazzani, L.[Loris], Tosato, D.[Diego], Murino, V.[Vittorio],
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Dai, J.[Jifeng], Feng, J.J.[Jian-Jiang], Zhou, J.[Jie],
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Kusuma, G.P.[Gede Putra], Szabo, A.[Attila], Li, Y.[Yiqun], Lee, J.A.[Jimmy Addison],
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Hartl, A.[Andreas], Reitmayr, G.[Gerhard],
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ECCV12(II: 73-86).
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Russakovsky, O.[Olga], Lin, Y.Q.[Yuan-Qing], Yu, K.[Kai], Fei-Fei, L.[Li],
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ECCV12(II: 1-15).
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CVPR10(1070-1077).
IEEE DOI 1006
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Dubout, C.[Charles], Fleuret, F.[François],
Accelerated Training of Linear Object Detectors,
SPTLI13(572-577)
IEEE DOI 1309
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Earlier:
Exact Acceleration of Linear Object Detectors,
ECCV12(III: 301-311).
Springer DOI 1210
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Hoiem, D.[Derek], Chodpathumwan, Y.[Yodsawalai], Dai, Q.[Qieyun],
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Fast and Adaptive Deep Fusion Learning for Detecting Visual Objects,
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Cao, L.[Lu], Kobayashi, Y.[Yoshinori], Kuno, Y.[Yoshinori],
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Gan, L.[Lin], Zhang, H.[He], Zhang, X.J.[Xiang-Jin],
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IASP11(159-162).
IEEE DOI 1112
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Shah, B.N.[Brijesh N.], Shah, S.K.[Satish K.], Kosta, Y.P.,
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Zhao, X.[Xinyue], Satoh, Y., Takauji, H., Kaneko, S., Iwata, K., Ozaki, R.,
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AVSBS11(261-266).
IEEE DOI 1111
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Porikli, F.M., Ozkan, H.,
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AVSBS11(30-35).
IEEE DOI 1111
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Smirnov, P.[Pavel], Semenov, P.[Piotr], Redkin, A.[Alexander], Chun, A.[Anthony],
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CVS11(51-60).
Springer DOI 1109
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CIAP11(I: 575-584).
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Zhang, G.X.[Gao-Xiang], Jiang, F.[Feng], Zhao, D.B.[De-Bin], Sun, X.S.[Xiao-Shuai], Liu, S.H.[Shao-Hui],
Saliency Detection: A Self-Adaption Sparse Representation Approach,
ICIG11(461-465).
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Chen, G.[Guang], Han, T.X.[Tony X.], Lao, S.H.[Shi-Hong],
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Kim, H.C.[Hyun-Cheol], Kim, W.Y.[Whoi-Yul],
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Xiong, J.[Jian], Nguyen, T.M.[Thanh Minh], Wu, Q.M.J.[Q.M. Jonathan],
FPGA Implementation of Blob Recognition,
CRV11(125-131).
IEEE DOI 1105
BibRef

Zhang, Z.M.[Zi-Ming], Huang, J.W.[Jia-Wei], Li, Z.N.[Ze-Nian],
Learning Sparse Features On-Line for Image Classification,
ICIAR11(I: 122-131).
Springer DOI 1106
BibRef

Chiusano, G.[Gabriele], Staglianň, A.[Alessandra], Basso, C.[Curzio], Verri, A.[Alessandro],
DCE-MRI Analysis Using Sparse Adaptive Representations,
MLMI11(67-74).
Springer DOI 1109
BibRef

Staglianň, A.[Alessandra], Chiusano, G.[Gabriele], Basso, C.[Curzio], Santoro, M.[Matteo],
Learning Adaptive and Sparse Representations of Medical Images,
MCV10(130-140).
Springer DOI 1009
Sparse coding by learning dictionaries of features. BibRef

Semenovich, D.[Dimitri], Sowmya, A.[Arcot],
Geometry Aware Local Kernels for Object Recognition,
ACCV10(I: 490-503).
Springer DOI 1011
BibRef

Li, H.Y.[Hong-Yu], Chen, L.[Lei],
Removal of false positive in object detection with contour-based classifiers,
ICIP10(3941-3944).
IEEE DOI 1009
after Haar-based detection. BibRef

Schindler, A.[Andreas], Maier, G.[Georg],
Object detection in gray scale images based on invariant polynomial features,
ICIP10(4633-4636).
IEEE DOI 1009
BibRef

Petit, F.[Frederic], Capelle-Laize, A.S.[Anne-Sophie], Carre, P.[Philippe],
Hue-based quaternionic criterion for focused-color extraction,
ICIP10(1617-1620).
IEEE DOI 1009
Extract specific colored region. BibRef

Pan, K.[Kangyu], Kokaram, A.[Anil], Hillebrand, J.[Jens], Ramaswami, M.[Mani],
Gaussian mixture models for spots in microscopy using a new split/merge em algorithm,
ICIP10(3645-3648).
IEEE DOI 1009
BibRef

Liu, J.[Jiamin], White, J.M.[Jacob M.], Summers, R.M.[Ronald M.],
Automated detection of blob structures by Hessian analysis and object scale,
ICIP10(841-844).
IEEE DOI 1009
BibRef

Ming, A.[Anlong], Ma, H.[Huadong],
A blob detector in color images,
CIVR07(364-370).
DOI Link 0707
BibRef

Gao, K.[Ke], Zhang, Y.D.[Yong-Dong], Zhang, W.[Wei], Lin, S.X.[Shou-Xun],
Affine Stable Characteristic based sample expansion for object detection,
CIVR10(422-429).
DOI Link 1007
BibRef

Kobayashi, J.[Junya], Yamada, K.[Keiichi],
Detection of Abnormal Objects in a Scene Based on Local Features,
MVA09(34-).
PDF File. 0905
Trained with usual scenes, find things not in the training. BibRef

Su, J.Y.[Jing-Yong], Zhu, Z.Q.[Zhi-Qiang], Srivastava, A.[Anuj], Huffer, F.[Fred],
Detection of Shapes in 2D Point Clouds Generated from Images,
ICPR10(2640-2643).
IEEE DOI 1008
BibRef

Cho, M.[Minsu], Shin, Y.M.[Young Min], Lee, K.M.[Kyoung Mu],
Unsupervised detection and segmentation of identical objects,
CVPR10(1617-1624).
IEEE DOI Video of talk:
WWW Link. 1006
Grow from local feature matches. BibRef

Liu, H.R.[Hai-Rong], Yan, S.C.[Shui-Cheng],
Efficient structure detection via random consensus graph,
CVPR12(574-581).
IEEE DOI 1208
BibRef
And:
Common visual pattern discovery via spatially coherent correspondences,
CVPR10(1609-1616).
IEEE DOI Video of talk:
WWW Link. 1006
local features and spatial arrangements Not simple blobs, but more complex structures. BibRef

Zhang, Z.[Zhiqi], Cao, Y.[Yu], Salvi, D.[Dhaval], Oliver, K.[Kenton], Waggoner, J.W.[Jarrell W.], Wang, S.[Song],
Free-shape subwindow search for object localization,
CVPR10(1086-1093).
IEEE DOI 1006
BibRef

Pham, M.T.[Minh-Tri], Gao, Y.[Yang], Hoang, V.D.D.[Viet-Dung D.], Cham, T.J.[Tat-Jen],
Fast polygonal integration and its application in extending Haar-like features to improve object detection,
CVPR10(942-949).
IEEE DOI 1006
Fast technique for arbitrary polygon, not just rectangular window. BibRef

Lehmann, A.[Alain], Leibe, B.[Bastian], Van Gool, L.J.[Luc J.],
Feature-centric Efficient Subwindow Search,
ICCV09(940-947).
IEEE DOI 0909
Searching in object detection. See also Efficient Subwindow Search: A Branch and Bound Framework for Object Localization. BibRef

Nie, Q.[Qing], Li, W.M.[Wei-Ming], Zhan, S.Y.[Shou-Yi],
Classification Based on SPACT and Visual Saliency,
CISP09(1-5).
IEEE DOI 0910
Modified spatial PACT as local feature descriptor. BibRef

Gao, J.M.[Jing-Min], Sun, Y.[Yan],
The Jag-Wave Feature Detection in 2D Images,
CISP09(1-5).
IEEE DOI 0910
BibRef

Nguyen, T.B.[Thanh Binh], Chung, S.T.[Sun Tae],
An Improved Real-Time Blob Detection for Visual Surveillance,
CISP09(1-5).
IEEE DOI 0910
BibRef

Wang, A.L.[Ai-Li], Liu, P.G.[Pi-Gang], Chen, Y.S.[Yu-Shi],
Multiwavelet-Based Region of Interest Image Coding,
CISP09(1-4).
IEEE DOI 0910
BibRef

Kumar, P.[Praveen], Palaniappan, K.[Kannappan], Mittal, A.[Ankush], Seetharaman, G.[Guna],
Parallel Blob Extraction Using the Multi-core Cell Processor,
ACIVS09(320-332).
Springer DOI 0909
BibRef

Vacura, M.[Miroslav], Svatek, V.[Vojtech], Saathoff, C.[Carsten], Franz, T.[Thomas], Troncy, R.[Raphael],
Describing low-level image features using the COMM ontology,
ICIP08(49-52).
IEEE DOI 0810
Extract low level features with COMM rather than MPEG-7 standard. BibRef

Li, Z.D.[Zhi-Dong], Chen, J.[Jing],
On Semantic Object Detection with Salient Feature,
ISVC08(II: 782-791).
Springer DOI 0812
BibRef

Fulkerson, B.[Brian], Vedaldi, A.[Andrea], Soatto, S.[Stefano],
Class Segmentation and Object Localization with Superpixel Neighborhoods,
ICCV09(670-677).
IEEE DOI 0909
BibRef
Earlier:
Localizing Objects with Smart Dictionaries,
ECCV08(I: 179-192).
Springer DOI 0810
Category and location of objects. First pixel classification with reduced dictionary. Combined results. BibRef

Emaminejad, A., Brookes, M.,
FEUDOR: Feature Extraction Using Distinctive Octagonal Regions,
BMVC08(xx-yy).
PDF File. 0809
BibRef

Mahmood, A.[Arif],
Structure-less object detection using AdaBoost algorithm,
ICMV07(85-90).
IEEE DOI 0712
BibRef

Chin, B.[Barret], Zhang, M.J.[Meng-Jie],
Object Detection Using Neural Networks and Genetic Programming,
EvoIASP08(xx-yy).
Springer DOI 0804
BibRef

Baró, X.[Xavier], Vitriŕ, J.[Jordi],
Weighted Dissociated Dipoles: An Extended Visual Feature Set,
CVS08(xx-yy).
Springer DOI 0805
representation based on Haar-like filters for use in classification. BibRef

Baró, X.[Xavier], Vitriŕ, J.[Jordi],
Evolutionary Object Detection by Means of Naďve Bayes Models Estimation,
EvoIASP08(xx-yy).
Springer DOI 0804
BibRef

Jia, W.J.[Wen-Jing], Tien, D.[David], He, X.J.[Xiang-Jian], Hope, B.A.[Brian A.], Wu, Q.A.[Qi-Ang],
Applying Local Cooccurring Patterns for Object Detection from Aerial Images,
Visual07(478-489).
Springer DOI 0706
BibRef

Daskalakis, A.[Antonis], Cavouras, D.[Dionisis], Bougioukos, P.[Panagiotis], Kostopoulos, S.[Spiros], Kalatzis, I.[Ioannis], Kagadis, G.C.[George C.], Nikiforidis, G.[George],
Development of a Cascade Processing Method for Microarray Spot Segmentation,
IbPRIA07(I: 410-417).
Springer DOI 0706
BibRef

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],
Intelligent Spot Detection for 2-DE Gel Image,
PSIVT06(168-177).
Springer DOI 0612
BibRef

Donoser, M., Bischof, H., Wiltsche, M.,
Color Blob Segmentation by MSER Analysis,
ICIP06(757-760).
IEEE DOI 0610
BibRef

Dupac, J.[Jan], Hlavác, V.[Václav],
Stable Wave Detector of Blobs in Images,
DAGM06(760-769).
Springer DOI 0610
BibRef

Xu, Q.[Qi], Chen, Y.Q.[Yan Qiu],
Multiscale Blob Features for Gray Scale, Rotation and Spatial Scale Invariant Texture Classification,
ICPR06(IV: 29-32).
IEEE DOI 0609
BibRef

Wang, W.X.[Wei-Xing],
Size and Shape Measure of Particles by Image Analysis,
IWCIA06(253-262).
Springer DOI 0606
BibRef

Samur, R., Zagorodnov, V.,
Segmenting Small Regions in the Presence of Noise,
ICIP05(II: 1254-1257).
IEEE DOI 0512
BibRef

Hinz, S.,
Fast and Subpixel Precise Blob Detection and Attribution,
ICIP05(III: 457-460).
IEEE DOI 0512
BibRef

Lichtenauer, J.F.[Jeroen F.], Hendriks, E.A.[Emile A.], Reinders, M.J.T.[Marcel J.T.],
Isophote Properties as Features for Object Detection,
CVPR05(II: 649-654).
IEEE DOI 0507
Filters for object detection. BibRef

Forssén, P.E.[Per-Erik], Granlund, G.H.[Gösta H.],
Robust Multi-scale Extraction of Blob Features,
SCIA03(11-18).
Springer DOI 0310
BibRef

Mamlouk, A.M.[Amir Madany], Kim, J.T.[Jan T.], Barth, E.[Erhardt], Brauckmann, M.[Michael], Martinetz, T.[Thomas],
One-Class Classification with Subgaussians,
DAGM03(346-353).
Springer DOI 0310
Assume a gaussian distribution, then it is a template match. BibRef

Sossa Azuela, J.H., Guzmán Lugo, G., Sotelo Rangel, R.,
Counting the Number of Blobs in an Image,
ICIP01(I: 1086-1089).
IEEE DOI 0108
BibRef

Nehrbass, U., Olivo-Marin, J.C.,
Three Dimensional Spot Detection by Multiscale Analysis,
ICIP01(I: 317-320).
IEEE DOI 0108
BibRef

Cucurachi, G.[Giorgio], Tascini, G.[Guido], Piazza, F.[Francesco],
Neural network for region detection,
CIAP97(II: 228-237).
Springer DOI 9709
BibRef

Cho, D.U.[Dong-Uk], Bae, J.J.,
Fuzzy-set based feature extraction for objects of various shapes and appearances,
ICIP96(II: 983-986).
IEEE DOI 9610
BibRef

Davies, E.R., Barker, S.P.,
An analysis of hole detection schemes,
BMVC90(xx-yy).
PDF File. 9009
BibRef

Chapter on 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture continues in
Fiducial Markers Design, Detection and Analysis .


Last update:Sep 18, 2017 at 11:34:11