Home   >   CSC-OpenAccess Library   >    Manuscript Information
Survey of The Problem of Object Detection In Real Images
Dilip K. Prasad
Pages - 441 - 466     |    Revised - 15-11-2012     |    Published - 31-12-2012
Volume - 6   Issue - 6    |    Publication Date - December 2012  Table of Contents
MORE INFORMATION
KEYWORDS
Boosting, Object Detection, Machine learning, Survey.
ABSTRACT
Object detection and recognition are important problems in computer vision. Since these problems are meta-heuristic, despite a lot of research, practically usable, intelligent, real-time, and dynamic object detection/recognition methods are still unavailable. The accuracy level of any algorithm or even Google glass project is below 16% for over 22,000 object categories. With this accuracy, it’s practically unusable. This paper reviews the various aspects of object detection and the challenges involved. The aspects addressed are feature types, learning model, object templates, matching schemes, and boosting methods. Most current research works are highlighted and discussed. Decision making tips are included with extensive discussion of the merits and demerits of each scheme.
CITED BY (29)  
1 Shaukat, A., Gao, Y., Kuo, J. A., Bowen, B. A., & Mort, P. E. (2016). Visual classification of waste material for nuclear decommissioning. Robotics and Autonomous Systems, 75, 365-378.
2 Prasad, D. K., & Wenhe, L. (2015). Metrics and statistics of frequency of occurrence of metamerism in consumer cameras for natural scenes. JOSA A, 32(7), 1390-1402.
3 Gautam, S., Sivaraman, K. S., Muralidharan, H., & Baskar, A. (2015). Vision System with Audio Feedback to Assist Visually Impaired to Grasp Objects. Procedia Computer Science, 58, 387-394.
4 Klingensmith, M., Herrmann, M., & Srinivasa, S. S. (2016). Object Modeling and Recognition from Sparse, Noisy Data via Voxel Depth Carving. In Experimental Robotics (pp. 697-713). Springer International Publishing.
5 Sivaraman, K. S., Gautam, S., Sarvesh, S., Khullar, A., Baskar, A., & Vasudevan, S. K. (2015). Object Recognition by Feature Weighted Matrix-A Novel Approach. Indian Journal of Science and Technology, 8(S7), 278-291.
6 Lee, D., Park, C., Jang, M., & Kim, J. (2015, October). Fast object detection via re-use mechanism. In Control, Automation and Systems (ICCAS), 2015 15th International Conference on (pp. 1196-1198). IEEE.
7 Bergamini, M. L., & Kamlofsky, J. (2015, May). Estudio de descriptores geométricos para el diseño y optimización de algoritmos de reconocimiento de objetos digitales. In XVII Workshop de Investigadores en Ciencias de la Computación (Salta, 2015).
8 Mustafa, W., Xiong, H., Kraft, D., Szedmak, S., Piater, J., & Kruger, N. (2015, October). Multi-Label Object Categorization Using Histograms of Global Relations. In 3D Vision (3DV), 2015 International Conference on (pp. 309-317). IEEE.
9 Vasavi, S., & Rao, V. S. (2015). Metadata Based Object Detection and Classification Using Key Frame Extraction Method. Journal of Image and Graphics, 3(2).
10 Wang, X., Valdez, T. A., & Bi, J. (2015). Detecting tympanostomy tubes from otoscopic images via offline and online training. Computers in biology and medicine, 61, 107-118.
11 Pedersoli, M., Vedaldi, A., Gonzàlez, J., & Roca, X. (2015). A coarse-to-fine approach for fast deformable object detection. Pattern Recognition, 48(5), 1844-1853.
12 Sergieh, H. M. (2014). Search-based Automatic Image Annotation Using Geotagged Community Photos (Doctoral dissertation, Universitätsbibliothek der Universität Passau).
13 Jha, A., Gupta, K., & Sen, M. (2014, November). M2M communication system for networked robots with low memory footprint. In Information Technology Systems and Innovation (ICITSI), 2014 International Conference on (pp. 310-316). IEEE.
14 Kamlofsky, J., & Bergamini, M. L. (2014, October). Herramientas de análisis de imágenes digitales para la visión artificial. In XVI Workshop de Investigadores en Ciencias de la Computación.
15 Dayangac, E., & Hirtz, G. (2014, September). Object recognition for human behavior analysis. In Consumer Electronics??? Berlin (ICCE-Berlin), 2014 IEEE Fourth International Conference on (pp. 64-68). IEEE.
16 Prasad, D. K., Leung, M. K., Quek, C., & Brown, M. S. (2014). DEB: Definite error bounded tangent estimator for digital curves. Image Processing, IEEE Transactions on, 23(10), 4297-4310.
17 Tanaka, M., & Horiuchi, T. (2014). Investigating perceptual qualities of static surface appearance using real materials and displayed images. Vision research.
18 Simonian, A. (2014). Feedback control for planar parallel magnetic manipulation (Doctoral dissertation, Master’s thesis, Czech Technical University in Prague).
19 Rachmawati, E., Suwardi, I. S., & Khodra, M. L. (2014). Review of Local Descriptor in RGB-D Object Recognition. TELKOMNIKA (Telecommunication Computing Electronics and Control), 12(4), 1132-1141.
20 Peng, L., Yang, Y., Qi, X., & Wang, H. (2014, February). Highly accurate video object identification utilizing hint information. In Computing, Networking and Communications (ICNC), 2014 International Conference on (pp. 317-321). IEEE.
21 Bernay-Angeletti, C., Aufrère, R., Chapuis, R., & Bernay, C. Comparaison entre le RANSAC et un Algorithme Focalisant pour la Reconnaissance d’Objets.
22 Prasad, D. K., & Brown, M. S. (2013). Online tracking of deformable objects under occlusion using dominant points. JOSA A, 30(8), 1484-1491.
23 Prasad, D. K. (2013). Object detection in real images. arXiv preprint arXiv:1302.5189.
24 Prasad, D. K. (2013). Geometric primitive feature extraction-concepts, algorithms, and applications. arXiv preprint arXiv:1305.3885.
25 Prasad, D. K., & Quek, C. (2013, December). Comparison of error bounds for non-parametric dominant point detection. In Information, Communications and Signal Processing (ICICS) 2013 9th International Conference on (pp. 1-5). IEEE.
26 Anwar, A., & El Rube, I. (2013). An Assessment of Image Matching Algorithms in Depth Estimation. International Journal of Image Processing (IJIP), 7(2), 109.
27 Avrithis, Y. (2013). Publication & Citation List.
28 Klingensmith, M. 3D Shape Reconstruction and Recognition using Negative Space Information.
29 Einrichtung, B., & für Lehrerbildung, Z. OPUS-Passau.
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 Scribd 
5 SlideShare 
6 PdfSR 
A. Bar-Hillel and D. Weinshall, "Efficient learning of relational object class models,"International Journal of Computer Vision, vol. 77, pp. 175-198, 2008.
A. Bar-Hillel, T. Hertz, and D. Weinshall, "Object class recognition by boosting a part-based model," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2005, pp. 702-709.
A. Bosch, A. Zisserman, and X. Muñoz, "Scene classification using a hybrid generative/discriminative approach," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, pp. 712-727, 2008.
A. C. Berg, T. L. Berg, and J. Malik, "Shape matching and object recognition using low distortion correspondences," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2005, pp. 26-33.
A. Demiriz, K. P. Bennett, and J. Shawe-Taylor, "Linear programming boosting via column generation," Machine Learning, vol. 46, pp. 225-254, 2002.
A. E. C. Pece, "On the computational rationale for generative models," Computer Vision and Image Understanding, vol. 106, pp. 130-143, 2007.
A. J. Joshi, F. Porikli, and N. Papanikolopoulos, "Multi-class active learning for image classification," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 2372-2379.
A. M. Bronstein, M. M. Bronstein, A. M. Bruckstein, and R. Kimmel, "Partial similarity of objects, or how to compare a centaur to a horse," International Journal of Computer Vision,vol. 84, pp. 163-183, 2009.
A. Mohan, C. Papageorgiou, and T. Poggio, "Example-based object detection in images by components," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, pp.349-361, 2001.
A. Opelt, A. Pinz, and A. Zisserman, "Incremental learning of object detectors using a visual shape alphabet," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2006, pp. 3-10.
A. Opelt, A. Pinz, and A. Zisserman, "Learning an alphabet of shape and appearance for multi-class object detection," International Journal of Computer Vision, vol. 80, pp. 16-44,2008.
A. Opelt, A. Pinz, M. Fussenegger, and P. Auer, "Generic object recognition with boosting,"IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, pp. 416-431,2006.
A. Opelt, M. Fussenegger, A. Pinz, and P. Auer, "Weak hypotheses and boosting for generic object detection and recognition," in Lecture Notes in Computer Science vol. 3022,ed, 2004, pp. 71-84.
A. R. Pope and D. G. Lowe, "Probabilistic models of appearance for 3-D object recognition," International Journal of Computer Vision, vol. 40, pp. 149-167, 2000.
A. Stefan, V. Athitsos, Q. Yuan, and S. Sclaroff, "Reducing JointBoost-Based Multiclass Classification to Proximity Search," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 589-596.
A. Torralba, K. P. Murphy, and W. T. Freeman, "Sharing features: Efficient boosting procedures for multiclass object detection," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2004, pp. 762-769.
A. Torralba, K. P. Murphy, and W. T. Freeman, "Sharing visual features for multiclass and multiview object detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, pp. 854-869, 2007.
A. Y. S. Chia, S. Rahardja, D. Rajan, and M. K. H. Leung, "Structural descriptors for category level object detection," IEEE Transactions on Multimedia, vol. 11, pp. 1407-1421,2009.
A. Zaharescu, E. Boyer, and R. Horaud, "Keypoints and Local Descriptors of Scalar Functions on 2D Manifolds," International Journal of Computer Vision, vol. 100, pp. 78-98,Oct 2012.
A. Zalesny, V. Ferrari, G. Caenen, and L. Van Gool, "Composite texture synthesis,"International Journal of Computer Vision, vol. 62, pp. 161-176, 2005.
B. Epshtein and S. Ullman, "Feature hierarchies for object classification," in Proceedings of the IEEE International Conference on Computer Vision, 2005, pp. 220-227.
B. Leibe and B. Schiele, "Analyzing appearance and contour based methods for object categorization," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2003, pp. 409-415.
B. Leibe, A. Leonardis, and B. Schiele, "Robust object detection with interleaved categorization and segmentation," International Journal of Computer Vision, vol. 77, pp.259-289, 2008.
B. Ommer and J. Buhmann, "Learning the Compositional Nature of Visual Object Categories for Recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010.
B. Wu and R. Nevatia, "Detection and tracking of multiple, partially occluded humans by Bayesian combination of edgelet based part detectors," International Journal of Computer Vision, vol. 75, pp. 247-266, 2007.
C. Desai, D. Ramanan, and C. C. Fowlkes, "Discriminative Models for Multi-Class Object Layout," International Journal of Computer Vision, vol. 95, pp. 1-12, Oct 2011.
C. Dubout and F. Fleuret, "Tasting Families of Features for Image Classification," in IEEE International Conference on Computer Vision, 2011, pp. 929-936.
C. F. Olson and D. P. Huttenlocher, "Automatic target recognition by matching oriented edge pixels," IEEE Transactions on Image Processing, vol. 6, pp. 103-113, 1997.
C. F. Olson, "A general method for geometric feature matching and model extraction,"International Journal of Computer Vision, vol. 45, pp. 39-54, 2001.
C. Gu, J. J. Lim, P. Arbeláez, and J. Malik, "Recognition using regions," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 1030-1037.
C. H. Lampert and J. Peters, "Active structured learning for high-speed object detection," in Lecture Notes in Computer Science vol. 5748, ed, 2009, pp. 221-231.
C. H. Lampert, H. Nickisch, and S. Harmeling, "Learning to detect unseen object classes by between-class attribute transfer," in Proceedings of the IEEE Computer Vision and Pattern Recognition Workshop, 2009, pp. 951-958.
C. Harris and M. Stephens, "A combined corner and edge detector," presented at the Alvey Vision Conference, 1988.
C. Huang, H. Ai, Y. Li, and S. Lao, "High-performance rotation invariant multiview face detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, pp.671-686, 2007.
C. Wallraven, B. Caputo, and A. Graf, "Recognition with local features: The kernel recipe,"in Proceedings of the IEEE International Conference on Computer Vision, 2003, pp. 257-264.
C. Xu, J. Liu, and X. Tang, "2D shape matching by contour flexibility," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, pp. 180-186, 2009.
D. A. Ross and R. S. Zemel, "Learning parts-based representations of data," Journal of Machine Learning Research, vol. 7, pp. 2369-2397, 2006.
D. Cailliere, F. Denis, D. Pele, and A. Baskurt, "3D mirror symmetry detection using Hough transform," in Proceedings of the IEEE International Conference on Image Processing, San Diego, CA, 2008, pp. 1772-1775.
D. D. Le and S. Satoh, "Ent-Boost: Boosting using entropy measures for robust object detection," Pattern Recognition Letters, vol. 28, pp. 1083-1090, 2007.
D. G. Lowe, "Distinctive image features from scale-invariant keypoints," International Journal of Computer Vision, vol. 60, pp. 91-110, 2004.
D. G. Sim and R. H. Park, "Two-dimensional object alignment based on the robust oriented Hausdorff similarity measure," IEEE Transactions on Image Processing, vol. 10, pp. 475-483, 2001.
D. Gerónimo, A. D. Sappa, D. Ponsa, and A. M. López, "2D-3D-based on-board pedestrian detection system," Computer Vision and Image Understanding, p. In press, 2010.
D. K. Prasad and M. K. H. Leung, "A hybrid approach for ellipse detection in real images,"in 2nd International Conference on Digital Image Processing, Singapore, 2010, pp. 75460I-6.
D. K. Prasad and M. K. H. Leung, "An ellipse detection method for real images," in 25th International Conference of Image and Vision Computing New Zealand (IVCNZ 2010),Queenstown, New Zealand, 2010, pp. 1-8.
D. K. Prasad and M. K. H. Leung, "Error analysis of geometric ellipse detection methods due to quantization," in Fourth Pacific-Rim Symposium on Image and Video Technology(PSIVT 2010), Singapore, 2010, pp. 58 - 63.
D. K. Prasad and M. K. H. Leung, "Methods for ellipse detection from edge maps of real images," in Machine Vision - Applications and Systems, F. Solari, M. Chessa, and S.Sabatini, Eds., ed: InTech, 2012, pp. 135-162.
D. K. Prasad and M. K. H. Leung, "Polygonal representation of digital curves," in Digital Image Processing, S. G. Stanciu, Ed., ed: InTech, 2012, pp. 71-90.
D. K. Prasad and M. K. H. Leung, "Reliability/Precision Uncertainty in Shape Fitting Problems," in IEEE International Conference on Image Processing, Hong Kong, 2010, pp.4277-4280.
D. K. Prasad, "Adaptive traffic signal control system with cloud computing based online learning," in 8th International Conference on Information, Communications, and Signal Processing (ICICS 2011), Singapore, 2011.
D. K. Prasad, "Assessing error bound for dominant point detection," International Journal of Image Processing (IJIP), vol. 6, pp. 326-333, 2012.
D. K. Prasad, "High Availability based Migration Analysis to Cloud Computing for High Growth Businesses," International Journal of Computer Networks (IJCN), vol. 4, 2012.
D. K. Prasad, C. Quek, and M. K. H. Leung, "A non-heuristic dominant point detection based on suppression of break points," in Image Analysis and Recognition. vol. 7324, A.Campilho and M. Kamel, Eds., ed Aveiro, Portugal: Springer Berlin Heidelberg, 2012, pp.269-276.
D. K. Prasad, C. Quek, and M. K. H. Leung, "A precise ellipse fitting method for noisy data," in Image Analysis and Recognition. vol. 7324, A. Campilho and M. Kamel, Eds., ed Aveiro, Portugal: Springer Berlin Heidelberg, 2012, pp. 253-260.
D. K. Prasad, C. Quek, and M. K. H. Leung, "Fast segmentation of sub-cellular organelles,"International Journal of Image Processing (IJIP), vol. 6, pp. 317-325, 2012.
D. K. Prasad, C. Quek, M. K. H. Leung, and S. Y. Cho, "A parameter independent line fitting method," in Asian Conference on Pattern Recognition (ACPR), Beijing, China, 2011,pp. 441-445.
D. K. Prasad, M. K. H. Leung, and C. Quek, "ElliFit: An unconstrained, non-iterative, least squares based geometric Ellipse Fitting method," Pattern Recognition, 2013.
D. K. Prasad, M. K. H. Leung, and S. Y. Cho, "Edge curvature and convexity based ellipse detection method," Pattern Recognition, vol. 45, pp. 3204-3221, 2012.
D. K. Prasad, M. K. H. Leung, C. Quek, and S.-Y. Cho, "A novel framework for making dominant point detection methods non-parametric," Image and Vision Computing, vol. 30,pp. 843-859, 2012.
D. K. Prasad, R. K. Gupta, and M. K. H. Leung, "An Error Bounded Tangent Estimator for Digitized Elliptic Curves," in Discrete Geometry for Computer Imagery. vol. 6607, ed:Springer Berlin / Heidelberg, 2011, pp. 272-283.
D. Parikh, C. L. Zitnick, and T. Chen, "Unsupervised learning of hierarchical spatial structures in images," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 2743-2750.
E. Bart and S. Ullman, "Cross-generalization: Learning novel classes from a single example by feature replacement," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2005, pp. 672-679.
E. Borenstein and J. Malik, "Shape guided object segmentation," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2006, pp. 969-976.
E. Borenstein and S. Ullman, "Learning to segment," in Lecture Notes in Computer Science vol. 3023, ed, 2004, pp. 315-328.
E. Makinen and R. Raisamo, "Evaluation of gender classification methods with automatically detected and aligned faces," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, pp. 541-547, 2008.
E. Rahtu, J. Kannala, and M. Blaschko, "Learning a Category Independent Object Detection Cascade," in IEEE International Conference on Computer Vision, 2011, pp.1052-1059.
F. C. D. Tsai, "Robust affine invariant matching with application to line features," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1993,pp. 393-399.
F. H. Cheng, "Multi-stroke relaxation matching method for handwritten Chinese character recognition," Pattern Recognition, vol. 31, pp. 401-410, 1998.
F. Jurie and B. Triggs, "Creating efficient codebooks for visual recognition," in Proceedings of the IEEE International Conference on Computer Vision, 2005, pp. 604-610.
F. Li and M. K. H. Leung, "Two-stage approach for palmprint identification using Hough transform and Hausdorff distance," in Proceedings of the International Conference on Control, Automation, Robotics and Vision, Singapore, SINGAPORE, 2006, pp. 1302-1307.
F. Li, M. K. H. Leung, and X. Z. Yu, "A two-level matching scheme for speedy and accurate palmprint identification," in Proceedings of the International Multimedia Modeling Conference, Singapore, SINGAPORE, 2007, pp. 323-332.
F. Perronnin, "Universal and adapted vocabularies for generic visual categorization," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, pp. 1243-1256, 2008.
G. Borgefors, "Hierarchical Chamfer matching: A parametric edge matching algorithm,"IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 10, pp. 849-865,1988.
G. Bouchard and B. Triggs, "Hierarchical part-based visual object categorization," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2005,pp. 710-715.
G. Carneiro and A. D. Jepson, "The quantitative characterization of the distinctiveness and robustness of local image descriptors," Image and Vision Computing, vol. 27, pp. 1143-1156, 2009.
G. J. Burghouts and J. M. Geusebroek, "Performance evaluation of local colour invariants,"Computer Vision and Image Understanding, vol. 113, pp. 48-62, 2009.
G. Mori, S. Belongie, and J. Malik, "Efficient shape matching using shape contexts," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, pp. 1832-1837, 2005.
G. Tolias and Y. Avrithis, "Speeded-up, relaxed spatial matching," in IEEE International Conference on Computer Vision, 2011, pp. 1653-1660.
G. X. Huang, H. F. Chen, Z. L. Zhou, F. Yin, and K. Guo, "Two-class support vector data description," Pattern Recognition, vol. 44, pp. 320-329, Feb 2011.
H. Bischof, H. Wildenauer, and A. Leonardis, "Illumination insensitive recognition using eigenspaces," Computer Vision and Image Understanding, vol. 95, pp. 86-104, 2004.
H. Hakalahti, D. Harwood, and L. S. Davis, "Two-dimensional object recognition by matching local properties of contour points," Pattern Recognition Letters, vol. 2, pp. 227-234, 1984.
H. P. Moravec, "Rover visual obstacle avoidance," in Proceedings of the International Joint Conference on Artificial Intelligence, Vancouver, CANADA, 1981, pp. 785-790.
H. Schneiderman and T. Kanade, "Object detection using the statistics of parts,"International Journal of Computer Vision, vol. 56, pp. 151-177, 2004.
H. T. Comer and B. A. Draper, "Interest Point Stability Prediction," in Proceedings of the International Conference on Computer Vision Systems, Liege, 2009.
H. Wang and J. Oliensis, "Rigid shape matching by segmentation averaging," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, pp. 619-635, 2010.
H. Zhang, W. Gao, X. Chen, and D. Zhao, "Object detection using spatial histogram features," Image and Vision Computing, vol. 24, pp. 327-341, 2006.
H.-C. Liu and M. D. Srinath, "Partial shape classification using contour matching in distance transformation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12,pp. 1072-1079, 1990.
I. A. Rizvi and B. K. Mohan, "Improving the Accuracy of Object Based Supervised Image Classification using Cloud Basis Function Neural Network for High Resolution Satellite Images," International Journal of Image Processing (IJIP), vol. 4, pp. 342-353, 2010.
I. Ulusoy and C. M. Bishop, "Generative versus discriminative methods for object recognition," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2005, pp. 258-265.
J. A. Lasserre, C. M. Bishop, and T. P. Minka, "Principled hybrids of generative and discriminative models," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2006, pp. 87-94.
J. Gall and V. Lempitsky, "Class-specific hough forests for object detection," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2009, pp. 1022-1029.
J. H. Elder, S. J. D. Prince, Y. Hou, M. Sizintsev, and E. Olevskiy, "Pre-attentive and attentive detection of humans in wide-field scenes," International Journal of Computer Vision, vol. 72, pp. 47-66, 2007.
J. H. Friedman, "Greedy function approximation: A gradient boosting machine," Annals of Statistics, vol. 29, pp. 1189-1232, 2001.
J. H. Friedman, "Stochastic gradient boosting," Computational Statistics and Data Analysis,vol. 38, pp. 367-378, 2002.
J. J. LaViola Jr and R. C. Zeleznik, "A practical approach for writer-dependent symbol recognition using a writer-independent symbol recognizer," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, pp. 1917-1926, 2007.
J. J. Rodríguez, L. I. Kuncheva, and C. J. Alonso, "Rotation forest: A New classifier ensemble method," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.28, pp. 1619-1630, 2006.
J. J. Yokono and T. Poggio, "Object recognition using boosted oriented filter based local descriptors," IEEJ Transactions on Electronics, Information and Systems, vol. 129, 2009.
J. Li and N. M. Allinson, "A comprehensive review of current local features for computer vision," Neurocomputing, vol. 71, pp. 1771-1787, 2008.
J. Matas, O. Chum, M. Urban, and T. Pajdla, "Robust wide-baseline stereo from maximally stable extremal regions," Image and Vision Computing, vol. 22, pp. 761-767, 2004.
J. Porway, Q. Wang, and S. C. Zhu, "A Hierarchical and Contextual Model for Aerial Image Parsing," International Journal of Computer Vision, pp. 1-30, 2009.
J. R. Burrill, S. X. Wang, A. Barrow, M. Friedman, and M. Soffen, "Model-based matching using elliptical features," in Proceedings of SPIE - The International Society for Optical Engineering, 1996, pp. 87-97.
J. R. R. Uijlings, A. W. M. Smeulders, and R. J. H. Scha, "What is the spatial extent of an object?," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 770-777.
J. Shotton, "Contour and texture for visual recognition of object categories," Doctoral of Philosphy, Queen's College, University of Cambridge, Cambridge, 2007.
J. Shotton, A. Blake, and R. Cipolla, "Contour-based learning for object detection," in Proceedings of the IEEE International Conference on Computer Vision, 2005, pp. 503-510.
J. Shotton, A. Blake, and R. Cipolla, "Multiscale categorical object recognition using contour fragments," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.30, pp. 1270-1281, 2008.
J. Shotton, J. Winn, C. Rother, and A. Criminisi, "TextonBoost for image understanding:Multi-class object recognition and segmentation by jointly modeling texture, layout, and context," International Journal of Computer Vision, vol. 81, pp. 2-23, 2009.
J. Stottinger, A. Hanbury, N. Sebe, and T. Gevers, "Sparse Color Interest Points for Image Retrieval and Object Categorization," IEEE Transactions on Image Processing, vol. 21, pp.2681-2692, May 2012.
J. Wang, V. Athitsos, S. Sclaroff, and M. Betke, "Detecting objects of variable shape structure with Hidden State Shape Models," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, pp. 477-492, 2008.
J. Winn and J. Shotton, "The layout consistent random field for recognizing and segmenting partially occluded objects," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2006, pp. 37-44.
J. Wu, S. C. Brubaker, M. D. Mullin, and J. M. Rehg, "Fast asymmetric learning for cascade face detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30,pp. 369-382, 2008.
J. Zhang, M. Marszalek, S. Lazebnik, and C. Schmid, "Local features and kernels for classification of texture and object categories: A comprehensive study," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2006, pp.13-13.
J. Šochman and J. Matas, "Learning fast emulators of binary decision processes,"International Journal of Computer Vision, vol. 83, pp. 149-163, 2009.
K. Mikolajczyk and C. Schmid, "A performance evaluation of local descriptors," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, pp. 1615-1630, 2005.
K. Mikolajczyk and C. Schmid, "A performance evaluation of local descriptors," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2003,pp. 1615-1630.
K. Mikolajczyk and H. Uemura, "Action recognition with motion-appearance vocabulary forest," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2008, pp. 1-8.
K. Mikolajczyk, B. Leibe, and B. Schiele, "Multiple object class detection with a generative model," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2006, pp. 26-33.
K. Schindler and D. Suter, "Object detection by global contour shape," Pattern Recognition,vol. 41, pp. 3736-3748, 2008.
K. Tieu and P. Viola, "Boosting Image Retrieval," International Journal of Computer Vision,vol. 56, pp. 17-36, 2004.
K. Tzevanidis and A. Argyros, "Unsupervised learning of background modeling parameters in multicamera systems," Computer Vision and Image Understanding, vol. 115, pp. 105-116, Jan 2011.
K. Y. Chang, T. L. Liu, H. T. Chen, and S. H. Lai, "Fusing Generic Objectness and Visual Saliency for Salient Object Detection," in IEEE International Conference on Computer Vision, 2011, pp. 914-921.
L. Fei-Fei, R. Fergus, and P. Perona, "Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories," Computer Vision and Image Understanding, vol. 106, pp. 59-70, 2007.
L. Furst, S. Fidler, and A. Leonardis, "Selecting features for object detection using an AdaBoost-compatible evaluation function," Pattern Recognition Letters, vol. 29, pp. 1603-1612, 2008.
L. Jie, T. Tommasi, and B. Caputo, "Multiclass Transfer Learning from Unconstrained Priors," in IEEE International Conference on Computer Vision, 2011, pp. 1863-1870.
L. Juan and O. Gwun, "A Comparison of SIFT, PCA-SIFT and SURF," International Journal of Image Processing (IJIP), vol. 3, pp. 143-152, 2009.
L. Szumilas and H. Wildenauer, "Spatial configuration of local shape features for discriminative object detection," in Lecture Notes in Computer Science vol. 5875, ed, 2009,pp. 22-33.
L. Szumilas, H. Wildenauer, and A. Hanbury, "Invariant shape matching for detection of semi-local image structures," in Lecture Notes in Computer Science vol. 5627, ed, 2009,pp. 551-562.
L. Wu, Y. Hu, M. Li, N. Yu, and X. S. Hua, "Scale-invariant visual language modeling for object categorization," IEEE Transactions on Multimedia, vol. 11, pp. 286-294, 2009.
M. Amiri and H. R. Rabiee, "RASIM: A Novel Rotation and Scale Invariant Matching of Local Image Interest Points," IEEE Transactions on Image Processing, vol. 20, pp. 3580-3591, Dec 2011.
M. Andriluka, S. Roth, and B. Schiele, "Discriminative Appearance Models for Pictorial Structures," International Journal of Computer Vision, vol. 99, pp. 259-280, Sep 2012.
M. Bergtholdt, J. Kappes, S. Schmidt, and C. Schnörr, "A study of parts-based object class detection using complete graphs," International Journal of Computer Vision, vol. 87, pp. 93-117, 2010.
M. Ceccarelli and A. Petrosino, "The orientation matching approach to circular object detection," in Proceedings of the IEEE International Conference on Image Processing,2001, pp. 712-715.
M. Collins, R. E. Schapire, and Y. Singer, "Logistic regression, AdaBoost and Bregman distances," Machine Learning, vol. 48, pp. 253-285, 2002.
M. Culp, K. Johnson, and G. Michailidis, "Ada: An R package for stochastic boosting,"Journal of Statistical Software, vol. 17, pp. 1-27, 2006.
M. Enzweiler and D. M. Gavrila, "Monocular pedestrian detection: Survey and experiments," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, pp.2179-2195, 2009.
M. F. Demirci, A. Shokoufandeh, and S. J. Dickinson, "Skeletal shape abstraction from examples," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, pp.944-952, 2009.
M. Fritz, B. Leibe, B. Caputo, and B. Schiele, "Integrating representative and discriminant models for object category detection," in Proceedings of the IEEE International Conference on Computer Vision, 2005, pp. 1363-1370.
M. Jamieson, Y. Eskin, A. Fazly, S. Stevenson, and S. J. Dickinson, "Discovering hierarchical object models from captioned images," Computer Vision and Image Understanding, vol. 116, pp. 842-853, Jul 2012.
M. Maire, S. X. Yu, and P. Perona, "Object Detection and Segmentation from Joint Embedding of Parts and Pixels," in IEEE International Conference on Computer Vision,2011, pp. 2142-2149.
M. P. Kumar, P. H. S. Torr, and A. Zisserman, "OBJCUT: Efficient Segmentation Using Top-Down and Bottom-Up Cues," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, pp. 530-545, 2009.
M. V. Rohith, G. Somanath, D. Metaxas, and C. Kambhamettu, "D - Clutter: Building object model library from unsupervised segmentation of cluttered scenes," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 2783-2789.
M. Varma and A. Zisserman, "A statistical approach to material classification using image patch exemplars," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.31, pp. 2032-2047, 2009.
M. Villamizar, J. Andrade-Cetto, A. Sanfeliu, and F. Moreno-Noguer, "Bootstrapping Boosted Random Ferns for discriminative and efficient object classification," Pattern Recognition, vol. 45, pp. 3141-3153, Sep 2012.
N. Adluru and L. J. Latecki, "Contour grouping based on contour-skeleton duality,"International Journal of Computer Vision, vol. 83, pp. 12-29, 2009.
N. Alajlan, M. S. Kamel, and G. H. Freeman, "Geometry-based image retrieval in binary image databases," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.30, pp. 1003-1013, 2008.
N. Razavi, J. Gall, and L. Van Gool, "Scalable Multi-class Object Detection," in IEEE Conference on Computer Vision and Pattern Recognition, 2011, pp. 1505-1512.
O. C. Hamsici and A. M. Martinez, "Rotation invariant kernels and their application to shape analysis," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, pp.1985-1999, 2009.
O. Choi and I. S. Kweon, "Robust feature point matching by preserving local geometric consistency," Computer Vision and Image Understanding, vol. 113, pp. 726-742, 2009.
P. Carbonetto, G. Dorko', C. Schmid, H. Kuck, and N. De Freitas, "Learning to recognize objects with little supervision," International Journal of Computer Vision, vol. 77, pp. 219-237, 2008.
P. Dollar, Z. Tu, and S. Belongie, "Supervised learning of edges and object boundaries," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2006,pp. 1964-1971.
P. F. Felzenszwalb and D. P. Huttenlocher, "Pictorial structures for object recognition,"International Journal of Computer Vision, vol. 61, pp. 55-79, 2005.
P. F. Felzenszwalb and J. D. Schwartz, "Hierarchical matching of deformable shapes," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2007,pp. 1-8.
P. F. Felzenszwalb, "Learning models for object recognition," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2001, pp. 1056-1062.
P. F. Felzenszwalb, "Representation and detection of deformable shapes," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, pp. 208-220, 2005.
P. Felzenszwalb, "Object Detection Grammars," in IEEE International Conference on Computer Vision Workshops, 2011.
P. J. Bickel, Y. Ritov, and A. Zakai, "Some theory for generalized boosting algorithms,"Journal of Machine Learning Research, vol. 7, pp. 705-732, 2006.
P. Jain and A. Kapoor, "Active learning for large Multi-class problems," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 762-769.
P. K. Mallapragada, R. Jin, A. K. Jain, and Y. Liu, "SemiBoost: Boosting for semisupervised learning," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.31, pp. 2000-2014, 2009.
P. M. Roth, S. Sternig, H. Grabner, and H. Bischof, "Classifier grids for robust adaptive object detection," in Proceedings of the IEEE Computer Vision and Pattern Recognition,Miami, FL, 2009, pp. 2727-2734.
P. Schnitzspan, M. Fritz, S. Roth, and B. Schiele, "Discriminative structure learning of hierarchical representations for object detection," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 2238-2245.
P. Viola and M. J. Jones, "Robust Real-Time Face Detection," International Journal of Computer Vision, vol. 57, pp. 137-154, 2004.
P. Viola, M. J. Jones, and D. Snow, "Detecting pedestrians using patterns of motion and appearance," International Journal of Computer Vision, vol. 63, pp. 153-161, 2005.
P. Wang and Q. Ji, "Multi-view face and eye detection using discriminant features,"Computer Vision and Image Understanding, vol. 105, pp. 99-111, 2007.
R. Brunelli and T. Poggio, "Template matching: Matched spatial filters and beyond," Pattern Recognition, vol. 30, pp. 751-768, 1997.
R. C. Nelson and A. Selinger, "Cubist approach to object recognition," in Proceedings of the IEEE International Conference on Computer Vision, 1998, pp. 614-621.
R. E. Schapire and Y. Singer, "Improved boosting algorithms using confidence-rated predictions," Machine Learning, vol. 37, pp. 297-336, 1999.
R. Fergus, L. Fei-Fei, P. Perona, and A. Zisserman, "Learning object categories from Google's image search," in Proceedings of the IEEE International Conference on Computer Vision, 2005, pp. 1816-1823.
R. Fergus, P. Perona, and A. Zisserman, "A sparse object category model for efficient learning and exhaustive recognition," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2005, pp. 380-387.
R. Fergus, P. Perona, and A. Zisserman, "A visual category filter for google images," in Lecture Notes in Computer Science vol. 3021, ed, 2004, pp. 242-256.
R. Salakhutdinov, A. Torralba, and J. Tenenbaum, "Learning to Share Visual Appearance for Multiclass Object Detection," in IEEE Conference on Computer Vision and Pattern Recognition, 2011, pp. 1481-1488.
R. Sandler and M. Lindenbaum, "Optimizing gabor filter design for texture edge detection and classification," International Journal of Computer Vision, vol. 84, pp. 308-324, 2009.
S. Agarwal, A. Awan, and D. Roth, "Learning to detect objects in images via a sparse, partbased representation," IEEE Transactions on Pattern Analysis and Machine Intelligence,vol. 26, pp. 1475-1490, 2004.
S. Ali and M. Shah, "A supervised learning framework for generic object detection in images," in Proceedings of the IEEE International Conference on Computer Vision, 2005,pp. 1347-1354.
S. Avidan, "Ensemble tracking," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, pp. 261-271, 2007.
S. Baluja and H. A. Rowley, "Boosting sex identification performance," International Journal of Computer Vision, vol. 71, pp. 111-119, 2007.
S. Basalamah, A. Bharath, and D. McRobbie, "Contrast marginalised gradient template matching," in Lecture Notes in Computer Science vol. 3023, ed, 2004, pp. 417-429.
S. Belongie, J. Malik, and J. Puzicha, "Matching shapes," in Proceedings of the IEEE International Conference on Computer Vision, 2001, pp. 454-461.
S. Belongie, J. Malik, and J. Puzicha, "Shape matching and object recognition using shape contexts," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, pp.509-522, 2002.
S. Biswas, G. Aggarwal, and R. Chellappa, "Robust estimation of albedo for illuminationinvariant matching and shape recovery," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, pp. 884-899, 2009.
S. Chen, J. Q. Wang, Y. Ouyang, B. Wang, C. S. Xu, and H. Q. Lu, "Boosting part-sense multi-feature learners toward effective object detection," Computer Vision and Image Understanding, vol. 115, pp. 364-374, Mar 2011.
S. H. Chang, F. H. Cheng, W. H. Hsu, and G. Z. Wu, "Fast algorithm for point pattern matching: Invariant to translations, rotations and scale changes," Pattern Recognition, vol.30, pp. 311-320, 1997.
S. Lazebnik and M. Raginsky, "Supervised learning of quantizer codebooks by information loss minimization," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.31, pp. 1294-1309, 2009.
S. Lazebnik, C. Schmid, and J. Ponce, "A maximum entropy framework for part-based texture and object recognition," in Proceedings of the IEEE International Conference on Computer Vision, 2005, pp. 832-838.
S. Lazebnik, C. Schmid, and J. Ponce, "Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2006, pp. 2169-2178.
S. Maji and J. Malik, "Object detection using a max-margin hough transform," in Proceedings of the IEEE Computer Vision and Pattern Recognition, Miami, FL, 2009, pp.1038-1045.
S. Nilufar, N. Ray, and H. Zhang, "Object Detection With DoG Scale-Space: A Multiple Kernel Learning Approach," IEEE Transactions on Image Processing, vol. 21, pp. 3744-3756, Aug 2012.
S. Rivera and A. M. Martinez, "Learning deformable shape manifolds," Pattern Recognition,vol. 45, pp. 1792-1801, Apr 2012.
S. Z. Li and Z. Q. Zhang, "FloatBoost learning and statistical face detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, pp. 1112-1123, 2004.
T. H. Cho, "Object matching using generalized hough transform and chamfer matching," in Lecture Notes in Computer Science vol. 4099, ed, 2006, pp. 1253-1257.
T. M. Mitchell, Machine Learning: Mcgraw-Hill International Edition, 2010.
T. Tuytelaars and K. Mikolajczyk, "Local invariant feature detectors: A survey," Foundations and Trends in Computer Graphics and Vision, vol. 3, pp. 177-280, 2007.
T. Y. Ma and L. J. Latecki, "From Partial Shape Matching through Local Deformation to Robust Global Shape Similarity for Object Detection," in IEEE Conference on Computer Vision and Pattern Recognition, 2011, pp. 1441-1448.
T. Yeh, J. J. Lee, and T. Darrell, "Fast concurrent object localization and recognition," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2009,pp. 280-287.
V. Ferrari, L. Fevrier, F. Jurie, and C. Schmid, "Groups of adjacent contour segments for object detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30,pp. 36-51, 2008.
V. Ferrari, T. Tuytelaars, and L. Van Gool, "Object detection by contour segment networks," in Lecture Notes in Computer Science vol. 3953, ed, 2006, pp. 14-28.
V. Ferrari, T. Tuytelaars, and L. Van Gool, "Simultaneous object recognition and segmentation by image exploration," in Lecture Notes in Computer Science vol. 3021, ed,2004, pp. 40-54.
V. Ferrari, T. Tuytelaars, and L. Van Gool, "Simultaneous object recognition and segmentation from single or multiple model views," International Journal of Computer Vision, vol. 67, pp. 159-188, 2006.
W. T. Lee and H. T. Chen, "Histogram-based interest point detectors," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 1590-1596.
W. Zhang, B. Yu, G. J. Zelinsky, and D. Samaras, "Object class recognition using multiple layer boosting with heterogeneous features," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2005, pp. 323-330.
X. G. Wang, X. Bai, W. Y. Liu, and L. J. Latecki, "Feature Context for Image Classification and Object Detection," in IEEE Conference on Computer Vision and Pattern Recognition,2011, pp. 961-968.
X. Li, B. Yang, F. Zhu, and A. Men, "Real-time object detection based on the improved boosted features," in Proceedings of SPIE - The International Society for Optical Engineering, 2009.
X. Lin, Z. Zhu, and W. Deng, "Stereo matching algorithm based on shape similarity for indoor environment model building," in Proceedings of the IEEE International Conference on Robotics and Automation, 1996, pp. 765-770.
X. Ren, C. C. Fowlkes, and J. Malik, "Learning probabilistic models for contour completion in natural images," International Journal of Computer Vision, vol. 77, pp. 47-63, 2008.
X. Z. Yu and M. K. H. Leung, "Shape recognition using curve segment Hausdorff distance,"in Proceedings of the International Conference on Pattern Recognition, Hong Kong,PEOPLES R CHINA, 2006, pp. 441-444.
X. Z. Yu, M. K. H. Leung, and Y. S. Gao, "Hausdorff distance for shape matching," in Proceedings of the IASTED International Conference on Visualization, Imaging, and Image Processing, Marbella, SPAIN, 2004, pp. 819-824.
Y. Amit, D. Geman, and X. Fan, "A coarse-to-fine strategy for multiclass shape detection,"IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, pp. 1606-1621,2004.
Y. Aytar and A. Zisserman, "Tabula Rasa: Model Transfer for Object Category Detection," in IEEE International Conference on Computer Vision, 2011, pp. 2252-2259.
Y. Chen, L. Zhu, A. Yuille, and H. J. Zhang, "Unsupervised learning of probabilistic object models (POMs) for object classification, segmentation, and recognition using knowledge propagation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, pp.1747-1774, 2009.
Y. Chi and M. K. H. Leung, "A local structure matching approach for large image database retrieval," in Proceedings of the International Conference on Image Analysis and Recognition, Oporto, PORTUGAL, 2004, pp. 761-768.
Y. Chi and M. K. H. Leung, "Part-based object retrieval in cluttered environment," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, pp. 890-895, May 2007.
Y. Freund, "An adaptive version of the boost by majority algorithm," Machine Learning, vol.43, pp. 293-318, 2001.
Y. Freund, "Boosting a Weak Learning Algorithm by Majority," Information and Computation, vol. 121, pp. 256-285, 1995.
Y. Ke and R. Sukthankar, "PCA-SIFT: A more distinctive representation for local image descriptors," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2004, pp. 506-513.
Y. Li, L. Gu, and T. Kanade, "A robust shape model for multi-view car alignment," in Proceedings of the IEEE Computer Vision and Pattern Recognition Workshop, 2009, pp.2466-2473.
Y. Liu, X. L. Wang, H. Y. Wang, H. Zha, and H. Qin, "Learning Robust Similarity Measures for 3D Partial Shape Retrieval," International Journal of Computer Vision, pp. 1-24, 2009.
Y. N. Wu, Z. Si, H. Gong, and S. C. Zhu, "Learning Active Basis Model for Object Detection and Recognition," International Journal of Computer Vision, pp. 1-38, 2009.
Y. S. Gao and M. K. H. Leung, "Line segment Hausdorff distance on face matching,"Pattern Recognition, vol. 35, pp. 361-371, Feb 2002.
Z. He, T. Tan, Z. Sun, and X. Qiu, "Toward accurate and fast iris segmentation for iris biometrics," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, pp.1670-1684, 2009.
Z. Lin and L. S. Davis, "Shape-based human detection and segmentation via hierarchical part-template matching," IEEE Transactions on Pattern Analysis and Machine Intelligence,vol. 32, pp. 604-618, 2010.
Z. Si, H. Gong, Y. N. Wu, and S. C. Zhu, "Learning mixed templates for object recognition,"in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2009, pp. 272-279.
Z. Tu, "Probabilistic boosting-tree: Learning discriminative models for classification,recognition, and clustering," in Proceedings of the IEEE International Conference on Computer Vision, 2005, pp. 1589-1596.
Z. Tu, X. Chen, A. L. Yuille, and S. C. Zhu, "Image parsing: Unifying segmentation,detection, and recognition," International Journal of Computer Vision, vol. 63, pp. 113-140,2005.
Dr. Dilip K. Prasad
Nanyang Technological University - Singapore
dilipprasad@gmail.com