Tracking Deformable Objects in the Plane Using an Active Contour Model

The problems of segmenting a noisy intensity image and tracking a nonrigid object in the plane are discussed. In evaluating these problems, a technique based on an active contour model commonly called a snake is examined. The technique is applied to cell locomotion and tracking studies. The snake permits both the segmentation and tracking problems to be simultaneously solved in constrained cases. A detailed analysis of the snake model, emphasizing its limitations and shortcomings, is presented, and improvements to the original description of the model are proposed. Problems of convergence of the optimization scheme are considered. In particular, an improved terminating criterion for the optimization scheme that is based on topographic features of the graph of the intensity image is proposed. Hierarchical filtering methods, as well as a continuation method based on a discrete sale-space representation, are discussed. Results for both segmentation and tracking are presented. Possible failures of the method are discussed. >

[1]  P. Burt.  Fast filter transform for image processing , 1981 .

[2]  Laurent D. Cohen,et al.  A finite element method applied to new active contour models and 3D reconstruction from cross sections , 1990, [1990] Proceedings Third International Conference on Computer Vision.

[3]  Frank P. Ferrie,et al.  Cell Tracking: A Modeling and Minimization Approach , 1982, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  H. Saunders,et al.  Finite element procedures in engineering analysis , 1982 .

[5]  D M Shotton,et al.  Video-enhanced light microscopy and its applications in cell biology. , 1988, Journal of cell science.

[6]  James S. Duncan,et al.  Parametrically deformable contour models , 1989, Proceedings CVPR '89: IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[7]  Demetri Terzopoulos,et al.  Matching deformable models to images: Direct and iterative solutions , 1980 .

[8]  Martin D. Levine,et al.  Understanding blood cell motion , 1982, Comput. Graph. Image Process..

[9]  Ugo Montanari,et al.  On the optimal detection of curves in noisy pictures , 1971, CACM.

[10]  F. Leymarie.  Tracking and Describing Deformable Objects Using Active Contour Models , 1990 .

[11]  P. J. Burt,et al.  Fast Filter Transforms for Image Processing , 1981 .

[12]  J. Lackie.  Cell Movement and Cell Behaviour , 1986, Springer Netherlands.

[13]  Sharat Chandran,et al.  Global minima via dynamic programming: energy minimizing active contours , 1991, Optics & Photonics.

[14]  M. Levine,et al.  Computer Assisted Analyses of Cell Locomotion and Chemotaxis , 1986 .

[15]  Demetri Terzopoulos,et al.  The Role of Constraints and Discontinuities in Visible-Surface Reconstruction , 1983, IJCAI.

[16]  Ramin Samadani.  Adaptive snakes: control of damping and material parameters , 1991, Optics & Photonics.

[17]  Oliver Wr.  Cell locomotion as shape change. , 1989 .

[18]  Martin D. Levine,et al.  Cell segmentation revisited , 1991 .

[19]  Catherine Garbay,et al.  Image Structure Representation and Processing: A Discussion of Some Segmentation Methods in Cytology , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Steven W. Zucker,et al.  The Organization Of Curve Detection: Coarse Tangent Fields And Fine Spline Coverings , 1988, [1988 Proceedings] Second International Conference on Computer Vision.

[21]  Guy L. Scott,et al.  The Alternative Snake - and Other Animals , 1987, Alvey Vision Conference.

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

[23]  David J. Evans,et al.  Algorithm 512: A Normalized Algorithm for Solution of Positive Definite Symmetric Quindiagonal Systems of Linear Equations [F4] , 1977, TOMS.

[24]  A. Boyarsky,et al.  The quantification of blood cell motion by a method of automatic digital picture processing , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  G. Smith,et al.  Numerical Solution of Partial Differential Equations: Finite Difference Methods , 1978 .

[26]  Frederic Fol Leymarie,et al.  Simulating the Grassfire Transform Using an Active Contour Model , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[27]  P. Noble,et al.  A Markov chain characterization of human neutrophil locomotion under neutral and chemotactic conditions. , 1977, Canadian journal of physiology and pharmacology.

[28]  Demetri Terzopoulos,et al.  On Matching Deformable Models to Images , 1987, Topical Meeting on Machine Vision.

[29]  G. Stewart.  Introduction to matrix computations , 1973 .

[30]  Martin D. Levine,et al.  Vision in Man and Machine , 1985 .

[31]  Andrew Blake,et al.  Visual Reconstruction , 1987, Deep Learning for EEG-Based Brain–Computer Interfaces.

[32]  A de Boisfleury-Chevance,et al.  Locomotion of white blood cells: a biophysical analysis. , 1989, Blood cells.

[33]  Alex Pentland,et al.  Closed-Form Solutions for Physically Based Shape Modeling and Recognition , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

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

[35]  P. Whaite,et al.  Darboux frames, snakes, and super-quadrics: geometry from the bottom-up , 1989, [1989] Proceedings. Workshop on Interpretation of 3D Scenes.

[36]  Frederic Fol Leymarie,et al.  Shape Features Using Curvature Morphology , 1989, Other Conferences.

[37]  P. G. Ciarlet,et al.  Introduction to Numerical Linear Algebra and Optimisation , 1989 .

[38]  Martin D. Levine,et al.  Multiple Resolution Skeletons , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.