7.1.6 Feature, Object, Blob Detection and Spot Detection Systems

Chapter Contents (Back)
Spots. Object Detection. Spot Detection. Blob Detection. Blob Segmentation.

Sklansky, J.,
Recognition of Convex Blobs,
PR(2), No. 1, January 1970, pp. 3-10.
WWW Version. 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 Version. 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
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 Version. 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 Version. BibRef 8800

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

Rewo, L.,
Enhancement and Detection of Convex Objects Using Regression Models,
CVGIP(25), No. 2, February 1984, pp. 257-269.
WWW Version. 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 Version. 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

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

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 Version. 0206

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

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

Stefanou, M.S., Kerekes, J.P.,
A Method for Assessing Spectral Image Utility,
GeoRS(47), No. 6, June 2009, pp. 1698-1706.
IEEE DOI Link 0905

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 Link 1003

Kerekes, J.P.[John P.],
Hyperspectral remote sensing subpixel object detection performance,
IEEE DOI Link 1204

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

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

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

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 Version. 0307
Object detection in images run-time complexity and parameter selection of support vector machines,
ICPR02(II: 426-429).
IEEE DOI Link 0211

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 Version. 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 Link 0211

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.
HTML Version. 0304

Lee, K.M.[Kyoung-Mi], Street, W.N.[W. Nick],
Automatic Image Segmentation and Classification Using On-line Shape Learning,
IEEE Abstract. 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

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 Version. BibRef 0110

Sinzinger, E.D.[Eric D.],
Radial segmentation,
PRL(25), No. 12, September 2004, pp. 1337-1350.
WWW Version. 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.
WWW Version. 0407

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.
WWW Version. 0508

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.
WWW Version. 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 Version. BibRef 0604

Marks, R.L.[Richard L.],
Method for color transition detection,
US_Patent7,113,193, Sep 26, 2006
WWW Version. 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 Link 0701

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 Link 1003
Highlight on a Feature Extracted at Fine Scales: The Pointwise Lipschitz Regularity,
Springer DOI Link 0906

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 Link 0701
Connected rotation-invariant size-shape granulometries,
ICPR04(I: 688-691).
IEEE DOI Link 0409

Land, S.[Sander], Wilkinson, M.H.F.[Michael H.F.],
A Comparison of Spatial Pattern Spectra,
Springer DOI Link 0908

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

Broadwater, J.[Joshua], Chellappa, R.[Rama],
Hybrid Detectors for Subpixel Targets,
PAMI(29), No. 11, November 2007, pp. 1891-1903.
IEEE DOI Link 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 Version. 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 Version. 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 Version. 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 Version. Code, Landmarks. Techniques to achieve optimal results. Click to purchase this book BibRef 0800

Bogdanova, I., Bur, A., Hugli, H.,
Visual Attention on the Sphere,
IP(17), No. 11, November 2008, pp. 1-15.
IEEE DOI Link 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 Link 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 Link 0904
Earlier: A1, A3, Only:
Bottom-up saliency is a discriminant process,
IEEE DOI Link 0710
Earlier: A1, A3, Only:
Discriminant Interest Points are Stable,
IEEE DOI Link 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,
IEEE DOI Link 0810

Rosin, P.L.[Paul L.],
A simple method for detecting salient regions,
PR(42), No. 11, November 2009, pp. 2363-2371.
Elsevier DOI Link 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 Link 0907

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 Link 1011
Random walks on graphs to model saliency in images,
IEEE DOI Link 0906

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 Link 1202
Earlier: A1, A3, A2:
Sustained Observability for Salient Motion Detection,
ACCV10(III: 732-743).
Springer DOI Link 1011
And: A1, A3, A2:
Unsupervised Feature Selection for Salient Object Detection,
ACCV10(II: 15-26).
Springer DOI Link 1011

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 Link 0911
Beyond sliding windows: Object localization by efficient subwindow search,
IEEE DOI Link 0806
Award, CVPR, Best Paper. Efficient search for existence of object. BibRef

Blaschko, M.B.[Matthew B.],
Branch and Bound Strategies for Non-maximal Suppression in Object Detection,
Springer DOI Link 1107

Lampert, C.H.[Christoph H.],
An efficient divide-and-conquer cascade for nonlinear object detection,
IEEE DOI Link 1006
Detecting objects in large image collections and videos by efficient subimage retrieval,
IEEE DOI Link 0909

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

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 Link 1003

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

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 Link 1403
Learning to detect unseen object classes by between-class attribute transfer,
IEEE DOI Link 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 Link 1002

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 Link 1003
Top-hat transformation; Structuring element; Infrared dim small target; Target detection BibRef

Fiala, M.[Mark],
Designing Highly Reliable Fiducial Markers,
PAMI(32), No. 7, July 2010, pp. 1317-1324.
IEEE DOI Link 1006
ARTag: a Fiducial Marker System Using Digital Techniques,
CVPR05(II: 590-596).
IEEE DOI Link 0507
Special markers in image for alignment for AR applications. 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 Link 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 Link 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 Version. 1011

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 Link 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 Link 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 Link 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,
Springer DOI Link 0708

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 Link 1107

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

Vempati, S.[Sreekanth], Vedaldi, A.[Andrea], Zisserman, A.[Andrew], Jawahar, C.V.,
Generalized Rbf feature maps for Efficient Detection,
HTML Version. 1009

Vedaldi, A.[Andrea], Zisserman, A.[Andrew],
Sparse kernel approximations for efficient classification and detection,
IEEE DOI Link 1208

Vedaldi, A.[Andrea], Gulshan, V.[Varun], Varma, M.[Manik], Zisserman, A.[Andrew],
Multiple kernels for object detection,
IEEE DOI Link 0909
See also Learning The Discriminative Power-Invariance Trade-Off. 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,
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 Link 1202
A Scale Invariant Interest Point Detector for Discriminative Blob Detection,
Springer DOI Link 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 Link 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 Link 1203

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 Link 1203

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 Link 1202
Image Feature Extraction Using Gradient Local Auto-Correlations,
ECCV08(I: 346-358).
Springer DOI Link 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 Version. 1206
Earlier: A1, A3, A2:
Affine Adaptation of Local Image Features Using the Hessian Matrix,
IEEE DOI Link 0909
Blob detectors and corner features. BibRef

Lakemond, R.[Ruan], Fookes, C.[Clinton], Sridharan, S.[Sridha],
Negative Determinant of Hessian Features,
IEEE DOI Link 1205

Umakanthan, S., Denman, S., Sridharan, S., Fookes, C., Wark, T.,
Spatio Temporal Feature Evaluation for Action Recognition,
IEEE DOI Link 1303

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,
IEEE DOI Link 1205

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 Link 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,
IEEE Abstract. 0411

Alexe, B.[Bogdan], Deselaers, T.[Thomas], Ferrari, V.[Vittorio],
Measuring the Objectness of Image Windows,
PAMI(34), No. 11, November 2012, pp. 2189-2202.
IEEE DOI Link 1209
What is an object?,
IEEE DOI Link 1006
ClassCut for Unsupervised Class Segmentation,
ECCV10(V: 380-393).
Springer DOI Link 1009
And: A2, A1, A3:
Localizing Objects While Learning Their Appearance,
ECCV10(IV: 452-466).
Springer DOI Link 1009
Does an image window contain an object (any object). A generic object measure. Objects with well defined boundaries vs. amorphous background elements. BibRef

Deselaers, T.[Thomas], Ferrari, V.[Vittorio],
Visual and semantic similarity in ImageNet,
IEEE DOI Link 1106
Global and efficient self-similarity for object classification and detection,
IEEE DOI Link Video of talk:
WWW Version. 1006

Deselaers, T.[Thomas], Alexe, B.[Bogdan], Ferrari, V.[Vittorio],
Weakly Supervised Localization and Learning with Generic Knowledge,
IJCV(100), No. 3, December 2012, pp. 275-293.
WWW Version. 1210

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 Version. 1211

Boochs, F., Karmacharya, A., Marbs, A.,
Knowledge-based Object Detection In Laser Scanning Point Clouds,
DOI Link 1209

Truong, H.Q.[Hung Quoc], Ben Hmida, H.[Helmi], Marbs, A.[Andreas], Boochs, F.[Frank],
Integration of knowledge into the detection of objects in point clouds,
PDF Version. 1009

Pan, H.[Hong], Zhu, Y.P.[Ya-Ping], Xia, L.Z.[Liang-Zheng],
Efficient and accurate face detection using heterogeneous feature descriptors and feature selection,
CVIU(117), No. 1, January 2013, pp. 12-28.
Elsevier DOI Link 1212
Fusing multi-feature representation and PSO-Adaboost based feature selection for reliable frontal face detection,
IEEE DOI Link 1402
Cascade classifiers Face detection; PSO; Adaboost; Feature selection; Cascade classifier BibRef

Pan, H.[Hong], Zhu, Y.P.[Ya-Ping], Qin, A.K., Xia, L.Z.[Liang-Zheng],
Mining heterogeneous class-specific codebook for categorical object detection and classification,
IEEE DOI Link 1402
Class-specific codebook BibRef

Pan, H.[Hong], Zhu, Y.P.[Ya-Ping], Xia, S.[Siyu], Qin, K.[Kai],
Improved generic categorical object detection fusing depth cue with 2D appearance and shape features,
WWW Version. 1302

Pan, H.[Hong], Xia, L.Z.[Liang-Zheng], Nguyen, T.Q.[Truong Q.],
Robust object detection scheme using feature selection,
IEEE DOI Link 1009

Yanulevskaya, V.[Victoria], Uijlings, J.[Jasper], Geusebroek, J.M.[Jan-Mark],
Salient object detection: From pixels to segments,
IVC(31), No. 1, January 2013, pp. 31-42.
Elsevier DOI Link 1302
Salient object detection; Object-based visual attention theory; Proto-objects 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 Link 1304

Bagherinia, H.[Homayoun], Manduchi, R.[Roberto],
Robust real-time detection of multi-color markers on a cell phone,
RealTimeIP(8), No. 2, June 2013, pp. 207-223.
WWW Version. 1306

Bergamasco, F.[Filippo], Albarelli, A.[Andrea], Torsello, A.[Andrea],
Pi-Tag: a fast image-space marker design based on projective invariants,
MVA(24), No. 6, August 2013, pp. 1295-1310.
WWW Version. 1307
Image-Space Marker Detection and Recognition Using Projective Invariants,
IEEE DOI Link 1109

Bergamasco, F.[Filippo], Albarelli, A.[Andrea], Rodola, E.[Emanuele], Torsello, A.[Andrea],
RUNE-Tag: A high accuracy fiducial marker with strong occlusion resilience,
IEEE DOI Link 1106

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 Version. 1307

Torrent, A.[Albert], Lladó, X.[Xavier], Freixenet, J.[Jordi], Torralba, A.[Antonio],
A boosting approach for the simultaneous detection and segmentation of generic objects,
PRL(34), No. 13, 2013, pp. 1490-1498.
Elsevier DOI Link 1307
Simultaneous detection and segmentation for generic objects,
IEEE DOI Link 1201
Object detection. General framework, not just one kind of object. BibRef

Torrent, A.[Albert], Llado, X.[Xavier], Freixenet, J.[Jordi],
Semiautomatic labeling of generic objects for enlarging annotated image databases,
IEEE DOI Link 1302

Vondrick, C.[Carl], Khosla, A.[Aditya], Malisiewicz, T.[Tomasz], Torralba, A.[Antonio],
HOGgles: Visualizing Object Detection Features,
IEEE DOI Link 1403
hog; hoggles; object detection; visualization 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 Version. 1402
Efficient Monte Carlo Sampler for Detecting Parametric Objects in Large Scenes,
ECCV12(III: 539-552).
Springer DOI Link 1210
Sampling rather than all points. BibRef

Niitsu, Y.[Yasushi], Iizuka, T.[Takaaki],
Improving light marker accuracy on camera images,
SPIE(Newsroom), February 18, 2014
DOI Link 1402
A novel method determines precise boundaries of the light markers used to find the center of a target in image processing applications. BibRef

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

Garrido-Jurado, S., Muńoz-Salinas, R., Madrid-Cuevas, F.J., Marín-Jiménez, M.J.,
Automatic generation and detection of highly reliable fiducial markers under occlusion,
PR(47), No. 6, 2014, pp. 2280-2292.
Elsevier DOI Link 1403
Augmented reality 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 Link 1404
Exploiting Features: Locally Interleaved Sequential Alignment for Object Detection,
Springer DOI Link 1304
Computational modeling BibRef

Shi, Z.Y.[Zhi-Yuan], Hospedales, T.M.[Timothy M.], Xiang, T.[Tao],
Bayesian Joint Topic Modelling for Weakly Supervised Object Localisation,
IEEE DOI Link 1403
Bayesian; Joint Topic Modelling; Weakly Supervised Finding bounding box. BibRef

Cinbis, R.G.[Ramazan Gokberk], Verbeek, J.[Jakob], Schmid, C.[Cordelia],
Segmentation Driven Object Detection with Fisher Vectors,
IEEE DOI Link 1403
fisher vectors; object detection See also Action and Event Recognition with Fisher Vectors on a Compact Feature Set. BibRef

Manen, S.[Santiago], Guillaumin, M.[Matthieu], Van Gool, L.J.[Luc J.],
Prime Object Proposals with Randomized Prim's Algorithm,
IEEE DOI Link 1403
Object Detection; Object Proposal BibRef

Russakovsky, O.[Olga], Deng, J.[Jia], Huang, Z.H.[Zhi-Heng], Berg, A.C.[Alexander C.], Fei-Fei, L.[Li],
Detecting Avocados to Zucchinis: What Have We Done, and Where Are We Going?,
IEEE DOI Link 1403
categorical object detection. BibRef

Ehlers, A.[Arne], Scheuermann, B.[Björn], Baumann, F.[Florian], Rosenhahn, B.[Bodo],
Cleaning Up Multiple Detections Caused by Sliding Window Based Object Detectors,
Springer DOI Link 1311

Tan, T.N.[Tie-Niu], Huang, Y.Z.[Yong-Zhen], Zhang, J.G.[Jun-Ge],
Recent Progress on Object Classification and Detection,
Springer DOI Link 1311

Nalpantidis, L.[Lazaros], Großmann, B.[Bjarne], Krüger, V.[Volker],
Fast and Accurate Unknown Object Segmentation for Robotic Systems,
Springer DOI Link 1311

Ren, X.F.[Xiao-Feng], Ramanan, D.[Deva],
Histograms of Sparse Codes for Object Detection,
IEEE DOI Link 1309
Feature Learning; Object Detection; Sparse Coding; Supervised Training multiple features, beyond HoGradients. BibRef

Guo, X.[Xin], Liu, D.[Dong], Jou, B.[Brendan], Zhu, M.[Mojun], Cai, A.[Anni], Chang, S.F.[Shih-Fu],
Robust Object Co-detection,
IEEE DOI Link 1309
Objects of same category from a pool of similar objects. BibRef

Scharfenberger, C.[Christian], Waslander, S.L.[Steven L.], Zelek, J.S.[John S.], Clausi, D.A.[David A.],
Existence Detection of Objects in Images for Robot Vision Using Saliency Histogram Features,
IEEE DOI Link 1308
Feature extraction BibRef

Li, Y.[Yali], He, F.[Fei], Lu, W.H.[Wen-Hao], Wang, S.J.[Sheng-Jin],
Combining Fast Extracted Edge Descriptors and Feature Sharing for Rapid Object Detection,
Springer DOI Link 1304

Zhou, C.L.[Chun-Luan], Yuan, J.S.[Jun-Song],
Arbitrary-Shape Object Localization Using Adaptive Image Grids,
Springer DOI Link 1304

Ristin, M.[Marko], Gall, J.[Juergen], Van Gool, L.J.[Luc J.],
Local Context Priors for Object Proposal Generation,
Springer DOI Link 1304
Selective search to get hypotheses BibRef

Marchant, R., Jackway, P.T.,
Feature Detection from the Maximal Response to a Spherical Quadrature Filter Set,
IEEE DOI Link 1303

Bria, A.[Alessandro], Marrocco, C.[Claudio], Molinara, M.[Mario], Tortorella, F.[Francesco],
A ranking-based cascade approach for unbalanced data,
WWW Version. 1302
Use ranking rather than simply error. BibRef

Martelli, S.[Samuele], Cristani, M.[Marco], Bazzani, L.[Loris], Tosato, D.[Diego], Murino, V.[Vittorio],
Joining feature-based and similarity-based pattern description paradigms for object detection,
WWW Version. 1302

Dai, J.[Jifeng], Feng, J.J.[Jian-Jiang], Zhou, J.[Jie],
Mining sub-categories for object detection,
WWW Version. 1302

Zhang, J.[Junge], Zhao, X.[Xin], Huang, Y.Z.[Yong-Zhen], Huang, K.Q.[Kai-Qi], Tan, T.N.[Tie-Niu],
Semantic windows mining in sliding window based object detection,
WWW Version. 1302

Kusuma, G.P.[Gede Putra], Szabo, A.[Attila], Yiqun, L.[Li], Lee, J.A.[Jimmy Addison],
Appearance-based object recognition using weighted longest increasing subsequence,
WWW Version. 1302

Hartl, A.[Andreas], Reitmayr, G.[Gerhard],
Rectangular target extraction for mobile augmented reality applications,
WWW Version. 1302

Singh, S.[Saurabh], Gupta, A.[Abhinav], Efros, A.A.[Alexei A.],
Unsupervised Discovery of Mid-Level Discriminative Patches,
ECCV12(II: 73-86).
Springer DOI Link 1210
patches, like parts of objects. BibRef

Russakovsky, O.[Olga], Lin, Y.Q.[Yuan-Qing], Yu, K.[Kai], Fei-Fei, L.[Li],
Object-Centric Spatial Pooling for Image Classification,
ECCV12(II: 1-15).
Springer DOI Link 1210
Object centered spatial. Infer location, use that to get properties of object and background BibRef

Russakovsky, O.[Olga], Ng, A.Y.[Andrew Y.],
A Steiner tree approach to efficient object detection,
IEEE DOI Link 1006

Zheng, Y.B.[Ying-Bin], Jiang, Y.G.[Yu-Gang], Xue, X.Y.[Xiang-Yang],
Learning Hybrid Part Filters for Scene Recognition,
ECCV12(V: 172-185).
Springer DOI Link 1210
Not the whole object, but parts that may be shared with multiple objects. BibRef

Dubout, C.[Charles], Fleuret, F.[François],
Accelerated Training of Linear Object Detectors,
IEEE DOI Link 1309
Exact Acceleration of Linear Object Detectors,
ECCV12(III: 301-311).
Springer DOI Link 1210

Hoiem, D.[Derek], Chodpathumwan, Y.[Yodsawalai], Dai, Q.[Qieyun],
Diagnosing Error in Object Detectors,
ECCV12(III: 340-353).
Springer DOI Link 1210

Doulamis, N.D.[Nikolaos D.], Doulamis, A.D.[Anastasios D.],
Fast and Adaptive Deep Fusion Learning for Detecting Visual Objects,
Concept12(III: 345-354).
Springer DOI Link 1210

Cao, L.[Lu], Kobayashi, Y.[Yoshinori], Kuno, Y.[Yoshinori],
A Spatial-based Approach for Groups of Objects,
ISVC12(II: 597-608).
Springer DOI Link 1209
locating several identical objects grouped together. BibRef

Zaytseva, E.[Ekaterina], Seguí, S.[Santi], Vitriŕ, J.[Jordi],
Sketchable Histograms of Oriented Gradients for Object Detection,
Springer DOI Link 1209

Nasse, F.[Fabian], Fink, G.A.[Gernot A.],
A Bottom-up Approach for Learning Visual Object Detection Models from Unreliable Sources,
Springer DOI Link 1209

Verschae, R.[Rodrigo], Ruiz-del-Solar, J.[Javier],
TCAS: A Multiclass Object Detector for Robot and Computer Vision Applications,
ISVC12(I: 632-641).
Springer DOI Link 1209

Prest, A.[Alessandro], Leistner, C.[Christian], Civera, J.[Javier], Schmid, C.[Cordelia], Ferrari, V.[Vittorio],
Learning object class detectors from weakly annotated video,
IEEE DOI Link 1208

Hsiao, E.[Edward], Hebert, M.[Martial],
Occlusion reasoning for object detection under arbitrary viewpoint,
IEEE DOI Link 1208

Liu, K.[Kun], Wang, Q.[Qing], Driever, W.[Wolfgang], Ronneberger, O.[Olaf],
2D/3D rotation-invariant detection using equivariant filters and kernel weighted mapping,
IEEE DOI Link 1208

Wang, X.[Xiaoyu], Hua, G.[Gang], Han, T.X.[Tony X.],
Detection by detections: Non-parametric detector adaptation for a video,
IEEE DOI Link 1208
Trained object detector. BibRef

Neugebauer, C., Cameron-Jones, M., Horton, M.,
Learnt combination in object detector ensembles,
IEEE DOI Link 1203

Quast, K.[Katharina], Seeger, C.[Christoph], Trivedi, M.M.[Mohan M.], Kaup, A.[Andre],
Boosting based object detection using a geometric model,
IEEE DOI Link 1201

Gan, L.[Lin], Zhang, H.[He], Zhang, X.J.[Xiang-Jin],
Spot image processing and simulation of the laser proximity fuze,
IEEE DOI Link 1112

Shah, B.N.[Brijesh N.], Shah, S.K.[Satish K.], Kosta, Y.P.,
A seeded region growing algorithm for spot detection in medical image segmentation,
IEEE DOI Link 1112

Zhao, X.[Xinyue], Satoh, Y., Takauji, H., Kaneko, S., Iwata, K., Ozaki, R.,
Robust adapted object detection under complex environment,
IEEE DOI Link 1111

Porikli, F.M., Ozkan, H.,
Data driven frequency mapping for computationally scalable object detection,
IEEE DOI Link 1111

Smirnov, P.[Pavel], Semenov, P.[Piotr], Redkin, A.[Alexander], Chun, A.[Anthony],
Toward Accurate Feature Detectors Performance Evaluation,
Springer DOI Link 1109

Ferraretti, D.[Denis], Casarotti, L.[Luca], Gamberoni, G.[Giacomo], Lamma, E.[Evelina],
Spot Detection in Images with Noisy Background,
CIAP11(I: 575-584).
Springer DOI Link 1109

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,
IEEE DOI Link 1109

Chen, G.[Guang], Han, T.X.[Tony X.], Lao, S.H.[Shi-Hong],
Adapting an object detector by considering the worst case: A conservative approach,
IEEE DOI Link 1106

Kim, H.C.[Hyun-Cheol], Kim, W.Y.[Whoi-Yul],
Salient Region Detection Using Discriminative Feature Selection,
Springer DOI Link 1108

Xiong, J.[Jian], Nguyen, T.M.[Thanh Minh], Wu, Q.M.J.[Q.M. Jonathan],
FPGA Implementation of Blob Recognition,
IEEE DOI Link 1105

Xu, A.[Anqi], Dudek, G.[Gregory],
Fourier Tag: A Smoothly Degradable Fiducial Marker System with Configurable Payload Capacity,
IEEE DOI Link 1105

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 Link 1106

Chiusano, G.[Gabriele], Staglianň, A.[Alessandra], Basso, C.[Curzio], Verri, A.[Alessandro],
DCE-MRI Analysis Using Sparse Adaptive Representations,
Springer DOI Link 1109

Staglianň, A.[Alessandra], Chiusano, G.[Gabriele], Basso, C.[Curzio], Santoro, M.[Matteo],
Learning Adaptive and Sparse Representations of Medical Images,
Springer DOI Link 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 Link 1011

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

Schindler, A.[Andreas], Maier, G.[Georg],
Object detection in gray scale images based on invariant polynomial features,
IEEE DOI Link 1009

Petit, F.[Frederic], Capelle-Laize, A.S.[Anne-Sophie], Carre, P.[Philippe],
Hue-based quaternionic criterion for focused-color extraction,
IEEE DOI Link 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,
IEEE DOI Link 1009

Liu, J.[Jiamin], White, J.M.[Jacob M.], Summers, R.M.[Ronald M.],
Automated detection of blob structures by Hessian analysis and object scale,
IEEE DOI Link 1009

Ming, A.[Anlong], Ma, H.[Huadong],
A blob detector in color images,
DOI Link 0707

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,
DOI Link 1007

Kobayashi, J.[Junya], Yamada, K.[Keiichi],
Detection of Abnormal Objects in a Scene Based on Local Features,
PDF Version. 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,
IEEE DOI Link 1008

Kumar, V.B.G.[Vijay B.G.], Patras, I.[Ioannis],
A Discriminative Voting Scheme for Object Detection using Hough Forests,
HTML Version. 1009

Pedersoli, M.[Marco], Vedaldi, A.[Andrea], Gonzalez, J.[Jordi],
A coarse-to-fine approach for fast deformable object detection,
IEEE DOI Link 1106

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,
ECCV10(VI: 280-293).
Springer DOI Link 1009

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

Liu, H.R.[Hai-Rong], Yan, S.C.[Shui-Cheng],
Efficient structure detection via random consensus graph,
IEEE DOI Link 1208
Common visual pattern discovery via spatially coherent correspondences,
IEEE DOI Link Video of talk:
WWW Version. 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,
IEEE DOI Link 1006

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,
IEEE DOI Link 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,
IEEE DOI Link 0909
Searching in object detection. See also Efficient Subwindow Search: A Branch and Bound Framework for Object Localization. BibRef

dos Anjos, A.[Antonio], Shahbazkia, H.R.[Hamid Reza],
Automatic marker detection for blob images,
IEEE DOI Link 0912

Nie, Q.[Qing], Li, W.M.[Wei-Ming], Zhan, S.Y.[Shou-Yi],
Classification Based on SPACT and Visual Saliency,
IEEE DOI Link 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,
IEEE DOI Link 0910

Nguyen, T.B.[Thanh Binh], Chung, S.T.[Sun Tae],
An Improved Real-Time Blob Detection for Visual Surveillance,
IEEE DOI Link 0910

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

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

Vacura, M.[Miroslav], Svatek, V.[Vojtech], Saathoff, C.[Carsten], Franz, T.[Thomas], Troncy, R.[Raphael],
Describing low-level image features using the COMM ontology,
IEEE DOI Link 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 Link 0812

Fulkerson, B.[Brian], Vedaldi, A.[Andrea], Soatto, S.[Stefano],
Class Segmentation and Object Localization with Superpixel Neighborhoods,
IEEE DOI Link 0909
Localizing Objects with Smart Dictionaries,
ECCV08(I: 179-192).
Springer DOI Link 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,
PDF Version. 0809

Mahmood, A.[Arif],
Structure-less object detection using AdaBoost algorithm,
IEEE DOI Link 0712

Chin, B.[Barret], Zhang, M.J.[Meng-Jie],
Object Detection Using Neural Networks and Genetic Programming,
Springer DOI Link 0804

Baró, X.[Xavier], Vitriŕ, J.[Jordi],
Weighted Dissociated Dipoles: An Extended Visual Feature Set,
Springer DOI Link 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,
Springer DOI Link 0804

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,
Springer DOI Link 0706

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 Link 0706

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,
Springer DOI Link 0612

Donoser, M., Bischof, H., Wiltsche, M.,
Color Blob Segmentation by MSER Analysis,
IEEE DOI Link 0610

Dupac, J.[Jan], Hlavác, V.[Václav],
Stable Wave Detector of Blobs in Images,
Springer DOI Link 0610

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 Link 0609

Wang, W.X.[Wei-Xing],
Size and Shape Measure of Particles by Image Analysis,
Springer DOI Link 0606

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

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

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 Link 0507
Filters for object detection. BibRef

Claus, D.[David], Fitzgibbon, A.W.[Andrew W.],
Reliable Automatic Calibration of a Marker-Based Position Tracking System,
WACV05(I: 300-305).
IEEE DOI Link 0502

Claus, D.[David], Fitzgibbon, A.W.[Andrew W.],
Reliable Fiducial Detection in Natural Scenes,
ECCV04(Vol IV: 469-480).
WWW Version. 0405

Forssen, P.E.[Per-Erik], Lowe, D.G.[David G.],
Shape Descriptors for Maximally Stable Extremal Regions,
IEEE DOI Link 0710

Forssén, P.E.[Per-Erik], Granlund, G.H.[Gösta H.],
Robust Multi-scale Extraction of Blob Features,
WWW Version. 0310

Mamlouk, A.M.[Amir Madany], Kim, J.T.[Jan T.], Barth, E.[Erhardt], Brauckmann, M.[Michael], Martinetz, T.[Thomas],
One-Class Classification with Subgaussians,
HTML Version. 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 Link 0108

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

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

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 Link 9610

Davies, E.R., Barker, S.P.,
An analysis of hole detection schemes,
PDF Version. 9009

Chapter on 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture continues in
Interest Operators, Interest Points, Feature Points, Salient Points .

Last update:Apr 12, 2014 at 21:44:02