Adaptive Potential Active Hypercontours

In this paper, the idea of adaptive potential active hypercontours (APAH) as a new method of construction of an optimal classifier is presented. The idea of active hypercontours generalizes the traditional active contour methods, which are extensively developed in image analysis, and allows the application of their concepts in other classification tasks. In the presented implementation of APAH the evolution of the potential hypercontour is controlled by simulated annealing algorithm (SA). The method has been evaluated on the IRIS and MNIST databases and compared with traditional classification techniques.

[1]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

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

[3]  Timothy F. Cootes,et al.  Active Shape Models-Their Training and Application , 1995, Comput. Vis. Image Underst..

[4]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[5]  Ioannis Pitas,et al.  3-D Image Processing Algorithms , 2000 .

[6]  Arkadiusz Tomczyk,et al.  On the Relationship Between Active Contours and Contextual Classification , 2005, CORES.

[7]  A. Yezzi,et al.  On the relationship between parametric and geometric active contours , 2000, Conference Record of the Thirty-Fourth Asilomar Conference on Signals, Systems and Computers (Cat. No.00CH37154).

[8]  Guillermo Sapiro,et al.  Geodesic Active Contours , 1995, International Journal of Computer Vision.

[9]  Carl G. Looney,et al.  Pattern recognition using neural networks: theory and algorithms for engineers and scientists , 1997 .

[10]  Arkadiusz Tomczyk Active hypercontours and contextual classification , 2005, 5th International Conference on Intelligent Systems Design and Applications (ISDA'05).

[11]  David N. Levin,et al.  "Brownian Strings": Segmenting Images with Stochastically Deformable Contours , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Sankar K. Pal,et al.  Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing , 1999 .

[13]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .