Autonomous agents for edge detection and continuity perception on otolith images

Abstract An automatic method for edge detection on biological images (otolith images) using a multi-agent system is presented. One of the major problems encountered during an automatic contour detection is the lack of structure continuity perception. In this paper we present a new approach to perceive continuity based on a 2D reconstruction of closed contours using a multi-agent system. Each agent is provided with sensors on the image, which allow it to follow local intensity extremes. The purpose is to detect alternative light and dark concentric structures in an image. To improve the detection of these reactive agents, we have added high-level information about the shape of the contour. An application to fish otolith growth ring detection is presented in this paper.

[1]  Alan L. Yuille,et al.  Feature extraction from faces using deformable templates , 2004, International Journal of Computer Vision.

[2]  Leonid Sheremetov,et al.  Weiss, Gerhard. Multiagent Systems a Modern Approach to Distributed Artificial Intelligence , 2009 .

[3]  Raymond W. Smith,et al.  Computer processing of line images: A survey , 1987, Pattern Recognit..

[4]  Vincent Rodin,et al.  Use of deformable template for two-dimensional growth ring detection of otoliths by digital image processing:: Application to plaice (Pleuronectes platessa) otoliths , 2000 .

[5]  Vincent Rodin,et al.  Multiagent system for detecting concentric strias , 1997, Optics & Photonics.

[6]  Michael Wooldridge,et al.  Proceedings of the Second International Conference on Autonomous Agents, Minneapolis/St. Paul, MN USA, May 9-13, 1998 , 1998 .

[7]  Djemel Ziou,et al.  Line detection using an optimal IIR filter , 1991, Pattern Recognit..

[8]  Robert M. Haralick,et al.  Ridges and valleys on digital images , 1983, Comput. Vis. Graph. Image Process..

[9]  Olivier Boissier,et al.  ASIC: An Architecture for Social and Individual Control and its Application to Computer Vision , 1994, MAAMAW.

[10]  Jacques Ferber,et al.  Les Systèmes multi-agents: vers une intelligence collective , 1995 .

[11]  Alain Boucher.  Une approche décentralisée et adaptative de la gestion d'informations en vision ; application à l'interprétation d'images de cellules en mouvement. (A decentralized and adaptive approach for the information management in vision ; application to living cell image interpretation) , 1999 .

[12]  Anne Guillaud,et al.  Parameterization of a multiagent system for roof edge detection: an application to growth ring detection on fish otoliths , 2000, Electronic Imaging.

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

[14]  Julie A. Adams,et al.  Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence , 2001, AI Mag..

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

[16]  Abdessalam Benzinou,et al.  Growth ring detection on fish otoliths by a graph construction , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[17]  Walter D. Fisher.  On Grouping for Maximum Homogeneity , 1958 .

[18]  Catherine Garbay,et al.  A multi-agent system to segment living cells , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[19]  J. Canny.  Finding Edges and Lines in Images , 1983 .

[20]  Terry E. Weymouth,et al.  Using Dynamic Programming For Minimizing The Energy Of Active Contours In The Presence Of Hard Constraints , 1988, [1988 Proceedings] Second International Conference on Computer Vision.

[21]  H. Bezdek.  Reverberation at 75 kHz to a depth of 1 km in the Pacific Ocean—a negligible factor in attenuation , 1973 .