Viewpoint Induced Deformation Statistics and the Design of Viewpoint Invariant Features: Singularities and Occlusions

We study the set of domain deformations induced on images of three-dimensional scenes by changes of the vantage point. We parametrize such deformations and derive empirical statistics on the parameters, that show a kurtotic behavior similar to that of natural image and range statistics. Such a behavior would suggest that most deformations are locally smooth, and therefore could be captured by simple parametric maps, such as affine ones. However, we show that deformations induced by singularities and occluding boundaries, although rare, are highly salient, thus warranting the development of dedicated descriptors. We therefore illustrate the development of viewpoint invariant descriptors for singularities, as well as for occluding boundaries. We test their performance on scenes where the current state of the art based on affine-invariant region descriptors fail to establish correspondence, highlighting the features and shortcomings of our approach.

[1]  Cordelia Schmid,et al.  Scale & Affine Invariant Interest Point Detectors , 2004, International Journal of Computer Vision.

[2]  David Mumford,et al.  Statistics of range images , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[3]  Bill Triggs,et al.  Detecting Keypoints with Stable Position, Orientation, and Scale under Illumination Changes , 2004, ECCV.

[4]  Matthew A. Brown,et al.  Invariant Features from Interest Point Groups , 2002, BMVC.

[5]  Gabriela Csurka,et al.  Visual categorization with bags of keypoints , 2002, eccv 2004.

[6]  Cordelia Schmid,et al.  Shape recognition with edge-based features , 2003, BMVC.

[7]  Pietro Perona,et al.  Object class recognition by unsupervised scale-invariant learning , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[8]  Yair Weiss,et al.  Learning object detection from a small number of examples: the importance of good features , 2004, CVPR 2004.

[9]  Mark Meyer,et al.  Discrete Differential-Geometry Operators for Triangulated 2-Manifolds , 2002, VisMath.

[10]  Ulf Grenander,et al.  General Pattern Theory: A Mathematical Study of Regular Structures , 1993 .

[11]  Michel Vidal-Naquet,et al.  Visual features of intermediate complexity and their use in classification , 2002, Nature Neuroscience.

[12]  Dale Purves,et al.  Image/source statistics of surfaces in natural scenes , 2003, Network.

[13]  Andrew Zisserman,et al.  Video Google: a text retrieval approach to object matching in videos , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[14]  Cordelia Schmid,et al.  3D object modeling and recognition using affine-invariant patches and multi-view spatial constraints , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[15]  Jiri Matas,et al.  Robust wide-baseline stereo from maximally stable extremal regions , 2004, Image Vis. Comput..

[16]  Tony Lindeberg,et al.  Feature Detection with Automatic Scale Selection , 1998, International Journal of Computer Vision.

[17]  Song-Chun Zhu,et al.  Towards a mathematical theory of primal sketch and sketchability , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[18]  Stefano Soatto,et al.  Features for recognition: viewpoint invariance for non-planar scenes , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[19]  Timothy F. Cootes,et al.  Active Shape Models-Their Training and Application , 1995, Comput. Vis. Image Underst..

[20]  Martial Hebert,et al.  Incorporating Background Invariance into Feature-Based Object Recognition , 2005, 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05) - Volume 1.

[21]  Tinne Tuytelaars,et al.  Integrating multiple model views for object recognition , 2004, CVPR 2004.

[22]  Nuno Vasconcelos Feature selection by maximum marginal diversity: optimality and implications for visual recognition , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[23]  Jitendra Malik,et al.  Textons, contours and regions: cue integration in image segmentation , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[24]  Luc Van Gool,et al.  Simultaneous Object Recognition and Segmentation by Image Exploration , 2004, ECCV.

[25]  Horst Bischof,et al.  A novel performance evaluation method of local detectors on non-planar scenes , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.

[26]  Horst Bischof,et al.  Rapid Object Recognition from Discriminative Regions of Interest , 2004, AAAI.

[27]  H. Bischof,et al.  ROBUST AND FULLY AUTOMATED IMAGE REGISTRATION USING INVARIANT FEATURES , 2004 .

[28]  Andrew Zisserman,et al.  Viewpoint invariant texture matching and wide baseline stereo , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[29]  Pietro Perona,et al.  A sparse object category model for efficient learning and exhaustive recognition , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[30]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[31]  Cordelia Schmid,et al.  An Affine Invariant Interest Point Detector , 2002, ECCV.

[32]  Cordelia Schmid,et al.  The Geometry and Matching of Curves in Multiple Views , 1998, ECCV.

[33]  David W. Jacobs,et al.  In search of illumination invariants , 2001, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[34]  Stefan Carlsson,et al.  Appearance Based Qualitative Image Description for Object Class Recognition , 2004, ECCV.

[35]  Pietro Perona,et al.  A Bayesian approach to unsupervised one-shot learning of object categories , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[36]  C. Schmid,et al.  Object Class Recognition Using Discriminative Local Features , 2005 .

[37]  Lior Wolf,et al.  Feature selection for unsupervised and supervised inference: the emergence of sparsity in a weighted-based approach , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[38]  Luc Van Gool,et al.  Wide Baseline Stereo Matching based on Local, Affinely Invariant Regions , 2000, BMVC.

[39]  Michael J. Black,et al.  On the Spatial Statistics of Optical Flow , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[40]  Pietro Perona,et al.  Unsupervised Learning of Models for Recognition , 2000, ECCV.

[41]  Cordelia Schmid,et al.  On Pencils of Tangent Planes and the Recognition of Smooth 3D Shapes from Silhouettes , 2002, ECCV.

[42]  Trevor Darrell,et al.  Efficient image matching with distributions of local invariant features , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[43]  Cordelia Schmid,et al.  A Comparison of Affine Region Detectors , 2005, International Journal of Computer Vision.

[44]  M. Brady,et al.  Scale Saliency: a novel approach to salient feature and scale selection , 2003 .

[45]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.