SPECIAL ISSUE of IMAGE and VISION COMPUTING JOURNAL on Evolutionary Computation in Computer Vision --------------------------------------------- Call for Papers ----------------- Theme of the Special Issue Evolutionary computation is a term used to encompass a variety of population-based problem solving techniques that mimic the natural process of Darwinian evolution. Current research in evolutionary computation has resulted in powerful, yet versatile problem solving mechanisms for searching, adaptation, learning and optimization in a variety of application domains. The main avenues for research in evolutionary computation are genetic algorithms, genetic programming, evolutionary strategies and evolutionary programming. Genetic algorithms stress chromosomal operators, genetic programming deals with operators on more general hierarchical structures, evolution strategies emphasize behavioral changes at the level of the individual whereas evolutionary programming stresses behavioral changes at the level of the species. The common factor underlying all the above approaches to evolutionary computation is the emphasis on an ensemble of solution structures, and the evaluation and evolution of these structures via specialized operators that mimic their biological counterparts, in response to an ever changing environment. Problems in computer vision and image understanding have always called for powerful problem solving techniques. This special issue will focus on problem solving techniques for computer vision and image understanding that are based on the paradigm of evolutionary computation. The topics of relevance to the special issue include but are not necessarily limited to: Low-level vision Evolutionary optimization, adaptation and learning algorithms for edge detection, image segmentation, figure-ground separation, texture analysis, feature selection, shape-from-XYZ and surface reconstruction. High-level vision Evolutionary computation for object recognition, scene analysis, indexing and search of model/image databases, and high-level learning of symbolic descriptions. Active vision Evolutionary computation for autonomous vision-guided navigation, path planning, sensing strategies, sensor integration, visual servoing, vergence and gaze control, hand-eye coordination, active tracking and vision-guided task planning. Neural vision Evolutionary computation for learning, adaptation and optimization of neural network structure and topology for computer vision problems. Guest Editor Dr. Suchendra M. Bhandarkar Department of Computer Science 415 Boyd Graduate Studies Research Center The University of Georgia Athens, GA 30602-7404, U.S.A. Telephone: (706) 542-1082 FAX: (706) 542-2966 E-mail: suchi@cs.uga.edu Submission Procedure Five copies of manuscripts (not exceeding 35 double-spaced pages in 12 pt size font including figures and tables) describing previously unpublished and original research should be submitted to reach the guest editor on or before January 31, 1997. All submissions will be peer reviewed for originality, significance, technical content and relevance to the special issue.