On Detection of Multiple Object Instances Using Hough Transforms

Hough transform-based methods for detecting multiple objects use nonmaxima suppression or mode seeking to locate and distinguish peaks in Hough images. Such postprocessing requires the tuning of many parameters and is often fragile, especially when objects are located spatially close to each other. In this paper, we develop a new probabilistic framework for object detection which is related to the Hough transform. It shares the simplicity and wide applicability of the Hough transform but, at the same time, bypasses the problem of multiple peak identification in Hough images and permits detection of multiple objects without invoking nonmaximum suppression heuristics. Our experiments demonstrate that this method results in a significant improvement in detection accuracy both for the classical task of straight line detection and for a more modern category-level (pedestrian) detection problem.

[1]  P.V.C. Hough,et al.  Machine Analysis of Bubble Chamber Pictures , 1959 .

[2]  Laurence A. Wolsey,et al.  Best Algorithms for Approximating the Maximum of a Submodular Set Function , 1978, Math. Oper. Res..

[3]  M. L. Fisher,et al.  An analysis of approximations for maximizing submodular set functions—I , 1978, Math. Program..

[4]  Dana H. Ballard,et al.  Generalizing the Hough transform to detect arbitrary shapes , 1981, Pattern Recognit..

[5]  Dorit S. Hochbaum,et al.  Heuristics for the fixed cost median problem , 1982, Math. Program..

[6]  Gérard Cornuéjols,et al.  Submodular set functions, matroids and the greedy algorithm: Tight worst-case bounds and some generalizations of the Rado-Edmonds theorem , 1984, Discret. Appl. Math..

[7]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems , 1988 .

[8]  Richard S. Stephens,et al.  Probabilistic approach to the Hough transform , 1991, Image Vis. Comput..

[9]  Alan L. Yuille,et al.  Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Maxim Sviridenko,et al.  An 0.828-approximation Algorithm for the Uncapacitated Facility Location Problem , 1999, Discret. Appl. Math..

[11]  Thomas P. Minka.  The ‘summation hack’ as an outlier model , 2003 .

[12]  Yvan G. Leclerc,et al.  Constructing simple stable descriptions for image partitioning , 1989, International Journal of Computer Vision.

[13]  Yali Amit,et al.  A coarse-to-fine strategy for multiclass shape detection , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Vladimir Kolmogorov,et al.  What metrics can be approximated by geo-cuts, or global optimization of length/area and flux , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[15]  Jamie Shotton,et al.  The Layout Consistent Random Field for Recognizing and Segmenting Partially Occluded Objects , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[16]  Pekka Ruusuvuori,et al.  Computational Framework for Simulating Fluorescence Microscope Images With Cell Populations , 2007, IEEE Transactions on Medical Imaging.

[17]  Vahab S. Mirrokni,et al.  Maximizing Non-Monotone Submodular Functions , 2011, 48th Annual IEEE Symposium on Foundations of Computer Science (FOCS'07).

[18]  Bernt Schiele,et al.  Robust Object Detection with Interleaved Categorization and Segmentation , 2008, International Journal of Computer Vision.

[19]  Delbert Dueck,et al.  Clustering by Passing Messages Between Data Points , 2007, Science.

[20]  Takeo Kanade,et al.  Mode-seeking by Medoidshifts , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[21]  Derek Hoiem,et al.  3D LayoutCRF for Multi-View Object Class Recognition and Segmentation , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[22]  James H. Elder,et al.  Efficient Edge-Based Methods for Estimating Manhattan Frames in Urban Imagery , 2008, ECCV.

[23]  Stefan Roth,et al.  People-tracking-by-detection and people-detection-by-tracking , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[24]  Joseph J. Lim,et al.  Recognition using regions , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[25]  Brendan J. Frey,et al.  FLoSS: Facility location for subspace segmentation , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[26]  Jitendra Malik,et al.  Object detection using a max-margin Hough transform , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[27]  Juergen Gall,et al.  Class-specific Hough forests for object detection , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[28]  Jitendra Malik,et al.  Poselets: Body part detectors trained using 3D human pose annotations , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[29]  Christopher K. I. Williams,et al.  Object localisation using the Generative Template of Features , 2009, Comput. Vis. Image Underst..

[30]  Charless C. Fowlkes,et al.  Discriminative Models for Multi-Class Object Layout , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[31]  Ryuzo Okada,et al.  Discriminative generalized hough transform for object dectection , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[32]  Luc Van Gool,et al.  PRISM: PRincipled Implicit Shape Model , 2009, BMVC.

[33]  Pushmeet Kohli,et al.  Graph Cut Based Inference with Co-occurrence Statistics , 2010, ECCV.

[34]  Anton Osokin,et al.  Fast Approximate Energy Minimization with Label Costs , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[35]  Andrew Zisserman,et al.  Learning To Count Objects in Images , 2010, NIPS.

[36]  Pushmeet Kohli,et al.  Geometric Image Parsing in Man-Made Environments , 2010, International Journal of Computer Vision.

[37]  Pushmeet Kohli,et al.  On Detection of Multiple Object Instances Using Hough Transforms , 2012, IEEE Trans. Pattern Anal. Mach. Intell..