Recognition without Correspondence using Multidimensional Receptive Field Histograms

The appearance of an object is composed of local structure. This local structure can be described and characterized by a vector of local features measured by local operators such as Gaussian derivatives or Gabor filters. This article presents a technique where appearances of objects are represented by the joint statistics of such local neighborhood operators. As such, this represents a new class of appearance based techniques for computer vision. Based on joint statistics, the paper develops techniques for the identification of multiple objects at arbitrary positions and orientations in a cluttered scene. Experiments show that these techniques can identify over 100 objects in the presence of major occlusions. Most remarkably, the techniques have low complexity and therefore run in real-time.

[1]  Dennis Gabor,et al.  Theory of communication , 1946 .

[2]  Keinosuke Fukunaga,et al.  Introduction to Statistical Pattern Recognition , 1972 .

[3]  Richard A. Young,et al.  SIMULATION OF HUMAN RETINAL FUNCTION WITH THE GAUSSIAN DERIVATIVE MODEL. , 1986 .

[4]  L Sirovich,et al.  Low-dimensional procedure for the characterization of human faces. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[5]  Yehezkel Lamdan,et al.  Object recognition by affine invariant matching , 2011, Proceedings CVPR '88: The Computer Society Conference on Computer Vision and Pattern Recognition.

[6]  A Computational Model Of Texture Segmentation , 1988, Twenty-Second Asilomar Conference on Signals, Systems and Computers.

[7]  Yehezkel Lamdan,et al.  Geometric Hashing: A General And Efficient Model-based Recognition Scheme , 1988, [1988 Proceedings] Second International Conference on Computer Vision.

[8]  John K. Tsotsos.  The Complexity of Perceptual Search Tasks , 1989, IJCAI.

[9]  Haim J. Wolfson,et al.  Model-Based Object Recognition by Geometric Hashing , 1990, ECCV.

[10]  Keinosuke Fukunaga,et al.  Introduction to statistical pattern recognition (2nd ed.) , 1990 .

[11]  W. Eric L. Grimson,et al.  Introduction to the Special Issue on Interpretation of 3-D Scenes-Part I , 1991, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Edward H. Adelson,et al.  The Design and Use of Steerable Filters , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[14]  David W. Jacobs,et al.  Space and Time Bounds on Indexing 3D Models from 2D Images , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Hans Knutsson,et al.  Preattentive gaze control for robot vision , 1992 .

[16]  Anil K. Jain,et al.  Texture classification and segmentation using multiresolution simultaneous autoregressive models , 1992, Pattern Recognit..

[17]  William H. Press,et al.  Numerical Recipes in C, 2nd Edition , 1992 .

[18]  Jitendra Malik,et al.  A Computational Framework for Determining Stereo Correspondence from a Set of Linear Spatial Filters , 1991, ECCV.

[19]  Andrew Zisserman,et al.  Geometric invariance in computer vision , 1992 .

[20]  W. Eric L. Grimson,et al.  Introduction to the Special Issue on Interpretation of 3-D Scenes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  Jitendra Malik,et al.  Computational framework for determining stereo correspondence from a set of linear spatial filters , 1992, Image Vis. Comput..

[22]  Rakesh Mohan,et al.  Systematic design of indexing strategies for object recognition , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[23]  Andrew Zisserman,et al.  Applications of Invariance in Computer Vision , 1993, Lecture Notes in Computer Science.

[24]  R. Deriche.  Recursively Implementing the Gaussian and its Derivatives , 1993 .

[25]  D. Ballard,et al.  Object recognition using steerable filters at multiple scales , 1993, [1993] Proceedings IEEE Workshop on Qualitative Vision.

[26]  Robert A. Hummel,et al.  Distributed Bayesian object recognition , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[27]  Hiroshi Murase,et al.  Learning and recognition of 3D objects from appearance , 1993, [1993] Proceedings IEEE Workshop on Qualitative Vision.

[28]  Gérard G. Medioni,et al.  Finding Waldo, or focus of attention using local color information , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[29]  John Daugman,et al.  High Confidence Visual Recognition of Persons by a Test of Statistical Independence , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[30]  Nathan Intrator,et al.  Three-Dimensional Object Recognition Using an Unsupervised BCM Network: The Usefulness of Distinguishing Features , 1993, Neural Computation.

[31]  J.B. Burns,et al.  View Variation of Point-Set and Line-Segment Features , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[32]  Kris Popat,et al.  Cluster-based probability model applied to image restoration and compression , 1994, Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing.

[33]  Rajesh P. N. Rao,et al.  Seeing Behind Occlusions , 1994, ECCV.

[34]  G. Healey,et al.  Using Illumination Invariant Color Histogram Descriptors for Recognit ion , 1994 .

[35]  Deformable Kernels for Early Vision , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[36]  Brian V. Funt,et al.  Color Constant Color Indexing , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[37]  Rajesh P. N. Rao,et al.  An Active Vision Architecture Based on Iconic Representations , 1995, Artif. Intell..

[38]  Rajesh P. N. Rao,et al.  Object indexing using an iconic sparse distributed memory , 1995, Proceedings of IEEE International Conference on Computer Vision.

[39]  Kenji Nagao,et al.  Recognizing 3D objects using photometric invariant , 1995, Proceedings of IEEE International Conference on Computer Vision.

[40]  David G. Lowe,et al.  Learning to recognize objects in images: acquiring and using probabilistic models of appearance , 1995 .

[41]  J. Hornegger,et al.  Statistical learning, localization, and identification of objects , 1995, Proceedings of IEEE International Conference on Computer Vision.

[42]  Glenn Healey,et al.  Combining color and geometric information for the illumination invariant recognition of 3-D objects , 1995, Proceedings of IEEE International Conference on Computer Vision.

[43]  Jiri Matas,et al.  On representation and matching of multi-coloured objects , 1995, Proceedings of IEEE International Conference on Computer Vision.

[44]  Dragutin Petkovic,et al.  Query by Image and Video Content: The QBIC System , 1995, Computer.

[45]  James Lee Hafner,et al.  Efficient Color Histogram Indexing for Quadratic Form Distance Functions , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[46]  M. Basseville.  Information : entropies, divergences et moyennes , 1996 .

[47]  David G. Lowe,et al.  Learning Appearance Models for Object Recognition , 1996, Object Representation in Computer Vision.

[48]  Bernt Schiele,et al.  Object Recognition Using Multidimensional Receptive Field Histograms , 1996, ECCV.

[49]  Katsushi Ikeuchi,et al.  Recognition of the multi specularity objects for bin-picking task , 1996, Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. IROS '96.

[50]  Cordelia Schmid,et al.  Combining greyvalue invariants with local constraints for object recognition , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[51]  Bartlett W. Mel.  SEEMORE: Combining Color, Shape, and Texture Histogramming in a Neurally Inspired Approach to Visual Object Recognition , 1997, Neural Computation.

[52]  Cordelia Schmid,et al.  Local Grayvalue Invariants for Image Retrieval , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[53]  Cordelia Schmid,et al.  Bayesian Decision Versus Voting for Image Retrieval , 1997, CAIP.

[54]  Rajesh P. N. Rao,et al.  Dynamic Model of Visual Recognition Predicts Neural Response Properties in the Visual Cortex , 1997, Neural Computation.

[55]  Cordelia Schmid,et al.  Comparing and evaluating interest points , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[56]  Bernt Schiele,et al.  Comprehensive Colour Image Normalization , 1998, ECCV.

[57]  Hayit Greenspan,et al.  Color- and Texture-based Image Segmentation Using the Expectation-Maximization Algorithm and its Application to Content-Based Image Retrieval. , 1998, ICCV 1998.

[58]  Jitendra Malik,et al.  Color- and texture-based image segmentation using EM and its application to content-based image retrieval , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[59]  Thomas S. Huang,et al.  Maximum Likelihood Detection , 1999 .

[60]  William H. Press,et al.  Numerical recipes in C , 2002 .

[61]  J. Koenderink,et al.  Representation of local geometry in the visual system , 1987, Biological Cybernetics.

[62]  Michael J. Swain,et al.  Color indexing , 1991, International Journal of Computer Vision.

[63]  Rachid Deriche,et al.  Using Canny's criteria to derive a recursively implemented optimal edge detector , 1987, International Journal of Computer Vision.

[64]  W. Eric L. Grimson,et al.  A study of affine matching with bounded sensor error , 1992, International Journal of Computer Vision.

[65]  Hiroshi Murase,et al.  Visual learning and recognition of 3-d objects from appearance , 2005, International Journal of Computer Vision.

[66]  M.,et al.  Statistical and Structural Approaches to Texture , 2022 .