Snakes: Active contour models

A snake is an energy-minimizing spline guided by external constraint forces and influenced by image forces that pull it toward features such as lines and edges. Snakes are active contour models: they lock onto nearby edges, localizing them accurately. Scale-space continuation can be used to enlarge the capture region surrounding a feature. Snakes provide a unified account of a number of visual problems, including detection of edges, lines, and subjective contours; motion tracking; and stereo matching. We have used snakes successfully for interactive interpretation, in which user-imposed constraint forces guide the snake near features of interest.

[1]  A. N. Tikhonov,et al.  REGULARIZATION OF INCORRECTLY POSED PROBLEMS , 1963 .

[2]  G. Sperling Binocular Vision: A Physical and a Neural Theory , 1970 .

[3]  Bernard Widrow,et al.  The "Rubber-Mask" Technique I. Pattern Measurement and Analysis , 1973 .

[4]  Martin A. Fischler,et al.  The Representation and Matching of Pictorial Structures , 1973, IEEE Transactions on Computers.

[5]  Alberto Martelli,et al.  An application of heuristic search methods to edge and contour detection , 1976, CACM.

[6]  G. Kanizsa Subjective contours. , 1976, Scientific American.

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

[8]  Azriel Rosenfeld,et al.  An Application of Relaxation Labeling to Line and Curve Enhancement , 1977, IEEE Transactions on Computers.

[9]  I. Gladwell,et al.  A Survey of Numerical Methods for Partial Differential Equations , 2021, An Introduction to Numerical Methods and Analysis.

[10]  D Marr,et al.  A computational theory of human stereo vision. , 1979, Proceedings of the Royal Society of London. Series B, Biological sciences.

[11]  T. Poggio,et al.  A computational theory of human stereo vision , 1979, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[12]  W. Eric L. Grimson,et al.  Shape Encoding and Subjective Contours , 1980, AAAI.

[13]  D Marr,et al.  Theory of edge detection , 1979, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[14]  B. Julesz,et al.  A disparity gradient limit for binocular fusion. , 1980, Science.

[15]  David J. Burr,et al.  Elastic Matching of Line Drawings , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  S. Zucker Computational and Psychophysical Experiments in Grouping: Early Orientation Selection , 1983 .

[17]  Andrew P. Witkin,et al.  Scale-Space Filtering , 1983, IJCAI.

[18]  T. Poggio,et al.  Ill-Posed Problems and Regularization Analysis in Early Vision , 1984 .

[19]  E. Hildreth The computation of the velocity field , 1984, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[20]  Tomaso Poggio,et al.  Computational vision and regularization theory , 1985, Nature.

[21]  Demetri Terzopoulos,et al.  Regularization of Inverse Visual Problems Involving Discontinuities , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  David Marr,et al.  Visual information processing: artificial intelligence and the sensorium of sight , 1987 .

[23]  Refractor Vision , 2000, The Lancet.

[24]  Demetri Terzopoulos,et al.  Signal matching through scale space , 1986, International Journal of Computer Vision.

[25]  Demetri Terzopoulos,et al.  Symmetry-seeking models and 3D object reconstruction , 1988, International Journal of Computer Vision.

[26]  S. Ullman,et al.  Filling-in the gaps: The shape of subjective contours and a model for their generation , 1976, Biological Cybernetics.