A model for dynamic shape and its applications

Variation in object shape is an important visual cue for deformable object recognition and classification. In this paper, we present an approach to model gradual changes in the 2-D shape of an object. We represent 2-D region shape in terms of the spatial frequency content of the region contour using Fourier coefficients. The temporal changes in these coefficients are used as the temporal signatures of the shape changes. Specifically, we use autoregressive model of the coefficient series. We demonstrate the efficacy of the model on several applications. First, we use the model parameters as discriminating features for object recognition and classification. Second, we show the use of the model for synthesis of dynamic shape using the model learned from a given image sequence. Third, we show that, with its capability of predicting shape, the model can be used to predict contours of moving regions, which can be used as initial estimates for the contour, based tracking methods.

[1]  King-Sun Fu,et al.  Shape Discrimination Using Fourier Descriptors , 1977, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  G. Schwarz Estimating the Dimension of a Model , 1978 .

[3]  Dimitris N. Metaxas,et al.  Dynamic 3D models with local and global deformations: deformable superquadrics , 1990, [1990] Proceedings Third International Conference on Computer Vision.

[4]  Frederic Fol Leymarie,et al.  Tracking Deformable Objects in the Plane Using an Active Contour Model , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  William T. Reeves Particle systems—a technique for modeling a class of fuzzy objects , 1993 .

[6]  Michael Isard,et al.  Contour Tracking by Stochastic Propagation of Conditional Density , 1996, ECCV.

[7]  Arnold Neumaier,et al.  Estimation of parameters and eigenmodes of multivariate autoregressive models , 2001, TOMS.

[8]  Richard Szeliski,et al.  Tracking with Kalman snakes , 1993 .

[9]  G. C. Tiao,et al.  An introduction to multiple time series analysis. , 1993, Medical care.

[10]  Eugene Fiume,et al.  Depicting fire and other gaseous phenomena using diffusion processes , 1995, SIGGRAPH.

[11]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[12]  Xiao Liu,et al.  Geometric analysis of constrained curves for image understanding , 2003 .

[13]  Narendra Ahuja,et al.  Vision based fire detection , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[14]  Alex Pentland,et al.  Closed-form solutions for physically-based shape modeling and recognition , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[15]  Karl Sims,et al.  Particle animation and rendering using data parallel computation , 1990, SIGGRAPH.

[16]  Marc Levoy,et al.  Fast texture synthesis using tree-structured vector quantization , 2000, SIGGRAPH.

[17]  Stefano Soatto,et al.  Dynamic Textures , 2003, International Journal of Computer Vision.

[18]  Andrew Blake,et al.  Affine-invariant contour tracking with automatic control of spatiotemporal scale , 1993, 1993 (4th) International Conference on Computer Vision.

[19]  Mubarak Shah,et al.  Flame recognition in video , 2000, Proceedings Fifth IEEE Workshop on Applications of Computer Vision.

[20]  Martin Szummer,et al.  Temporal texture modeling , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[21]  Glenn Healey,et al.  A system for real-time fire detection , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[22]  Sven Loncaric,et al.  A survey of shape analysis techniques , 1998, Pattern Recognit..

[23]  Theodosios Pavlidis,et al.  A review of algorithms for shape analysis , 1978 .

[24]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[25]  Stefano Soatto,et al.  Deformotion: Deforming Motion, Shape Average and the Joint Registration and Approximation of Structures in Images , 2003, International Journal of Computer Vision.