Reducing bias and inefficiency in the selection algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Context-Based Vision: Recognizing Objects Using Information from Both 2D and 3D Imagery
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part I
A data parallel algorithm for solving the region growing problem on the connection machine
Journal of Parallel and Distributed Computing - Special issue on data parallel algorithms and programming
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Knowledge-based image understanding systems: a survey
Computer Vision and Image Understanding
Finding salient regions in images: nonparametric clustering for image segmentation and grouping
Computer Vision and Image Understanding - Special issue on content-based access for image and video libraries
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Computer and Robot Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
A class of constrained clustering algorithms for object boundary extraction
IEEE Transactions on Image Processing
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In this paper, we propose a competitive image segmentation algorithm. It is a dynamic evolving optimization method, which we call the population algorithm. The method is inspired from nature, where the image segments are a population of entities that struggle for the limited image space and settle territory expansion conflicts locally without central authority. Hence, it is a region-based segmentation approach that locally considers region boundary adjustments in a dynamic way. Experiments confirm that this metaphor indeed applies when the image segmentation problem is modeled accordingly.