Recognition of planar object classes

We present a new framework for recognizing planar object classes, which is based on local feature detectors and a probabilistic model of the spatial arrangement of the features. The allowed object deformations are represented through shape statistics, which are learned from examples. Instances of an object in an image are detected by finding the appropriate features in the correct spatial configuration. The algorithm is robust with respect to partial occlusion, detector false alarms, and missed features. A 94% success rate was achieved for the problem of locating quasi-frontal views of faces in cluttered scenes.

[1]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[2]  D. Kendall.  SHAPE MANIFOLDS, PROCRUSTEAN METRICS, AND COMPLEX PROJECTIVE SPACES , 1984 .

[3]  F. Bookstein.  A statistical method for biological shape comparisons. , 1984, Journal of theoretical biology.

[4]  F. Bookstein.  Size and Shape Spaces for Landmark Data in Two Dimensions , 1986 .

[5]  D. Kendall.  A Survey of the Statistical Theory of Shape , 1989 .

[6]  A. Yuille.  Deformable Templates for Face Recognition , 1991, Journal of Cognitive Neuroscience.

[7]  K. Mardia,et al.  General shape distributions in a plane , 1991, Advances in Applied Probability.

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

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

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

[11]  Roberto Brunelli,et al.  Face Recognition: Features Versus Templates , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Christoph von der Malsburg,et al.  A Neural System for the Recognition of Partially Occluded Objects in Cluttered Scenes: A Pilot Study , 1993, Int. J. Pattern Recognit. Artif. Intell..

[13]  Joachim M. Buhmann,et al.  Distortion Invariant Object Recognition in the Dynamic Link Architecture , 1993, IEEE Trans. Computers.

[14]  Timothy F. Cootes,et al.  Use of active shape models for locating structures in medical images , 1994, Image Vis. Comput..

[15]  Gilles Burel,et al.  Detection and localization of faces on digital images , 1994, Pattern Recognit. Lett..

[16]  Arthur R. Pope,et al.  Modeling Positional Uncertainty in Object Recognition , 1994 .

[17]  Pietro Perona,et al.  Automating the hunt for volcanoes on Venus , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[18]  Adrian F. M. Smith,et al.  Bayesian Faces via Hierarchical Template Modeling , 1994 .

[19]  Michael C. Burl,et al.  Finding faces in cluttered scenes using random labeled graph matching , 1995, Proceedings of IEEE International Conference on Computer Vision.

[20]  Timothy F. Cootes,et al.  Automatic interpretation of human faces and hand gestures using flexible models. , 1995 .

[21]  P. Perona,et al.  Face Localization via Shape Statistics , 1995 .

[22]  F. Bookstein.  The Morphometric Synthesis for landmarks and edge- elements in images , 1995 .

[23]  Timothy F. Cootes,et al.  Combining point distribution models with shape models based on finite element analysis , 1994, Image Vis. Comput..

[24]  Takeo Kanade,et al.  Human Face Detection in Visual Scenes , 1995, NIPS.