Adaptive Parametrically Deformable Contours

In this paper, we introduce an unsupervised contour estimation strategy based on parametrically deformable models. The problem is formulated in a (statistical) parameter estimation framework with the parameters of both the contour the and observation model (the likelihood function) being considered unknown. Although other choices could fit in our formulation, we focus on Fourier and B-spline contour descriptors. To estimate the optimal parametrization order (e.g., the number of Fourier coefficients) we adopt the minimum description length (MDL) principle. The result is a parametrically deformable contour with an adaptive degree of smoothness and which also autonomously estimates the observation model parameters.

[1]  U. Grenander,et al.  Structural Image Restoration through Deformable Templates , 1991 .

[2]  Carl de Boor,et al.  A Practical Guide to Splines , 1978, Applied Mathematical Sciences.

[3]  E. Kreyszig.  Introductory Functional Analysis With Applications , 1978 .

[4]  Mário A. T. Figueiredo,et al.  Bayesian estimation of ventricular contours in angiographic images , 1992, IEEE Trans. Medical Imaging.

[5]  John Porrill,et al.  Active region models for segmenting medical images , 1994, Proceedings of 1st International Conference on Image Processing.

[6]  C VemuriBaba,et al.  Shape Modeling with Front Propagation , 1995 .

[7]  Laurent D. Cohen,et al.  Finite-Element Methods for Active Contour Models and Balloons for 2-D and 3-D Images , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Laurent D. Cohen,et al.  On active contour models and balloons , 1991, CVGIP Image Underst..

[9]  Anil K. Jain,et al.  Object Matching Using Deformable Templates , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Dante C. Youla,et al.  Generalized Image Restoration by the Method of Alternating Orthogonal Projections , 1978 .

[11]  Michael I. Miller,et al.  REPRESENTATIONS OF KNOWLEDGE IN COMPLEX SYSTEMS , 1994 .

[12]  James S. Duncan,et al.  Deformable boundary finding influenced by region homogeneity , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[13]  Baba C. Vemuri,et al.  Shape Modeling with Front Propagation: A Level Set Approach , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  David G. Luenberger,et al.  Linear and nonlinear programming , 1984 .

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

[16]  J. Rissanen.  A UNIVERSAL PRIOR FOR INTEGERS AND ESTIMATION BY MINIMUM DESCRIPTION LENGTH , 1983 .

[17]  Demetri Terzopoulos,et al.  Topologically adaptable snakes , 1995, Proceedings of IEEE International Conference on Computer Vision.

[18]  C. D. Meyer,et al.  Generalized inverses of linear transformations , 1979 .

[19]  Anil K. Jain,et al.  Vehicle Segmentation and Classification Using Deformable Templates , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[21]  Anil K. Jain.  Fundamentals of Digital Image Processing , 2018, Control of Color Imaging Systems.

[22]  Alan L. Yuille,et al.  Deformable templates , 1993 .

[23]  José M. N. Leitão,et al.  Wall position and thickness estimation from sequences of echocardiographic images , 1996, IEEE Trans. Medical Imaging.

[24]  Petia Radeva,et al.  A snake for model-based segmentation , 1995, Proceedings of IEEE International Conference on Computer Vision.

[25]  Ramesh C. Jain,et al.  Using Dynamic Programming for Solving Variational Problems in Vision , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[26]  Geir Storvik,et al.  A Bayesian Approach to Dynamic Contours Through Stochastic Sampling and Simulated Annealing , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[27]  Alan L. Yuille,et al.  Generalized Deformable Models, Statistical Physics, and Matching Problems , 1990, Neural Computation.

[28]  James S. Duncan,et al.  Boundary Finding with Parametrically Deformable Models , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[29]  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..