This paper presents an individual evolutionary Strategy devised for fast image analysis applications. The example problem chosen is obstacle detection using a pair of cameras. The algorithm evolves a population of threedimensional points (‘flies’) in the cameras fields of view, using a low complexity fitness function giving highest values to flies likely to be on the surfaces of 3-D obstacles. The algorithm uses classical sharing, mutation and crossover operators. The final result is a fraction of the population rather than a single individual. Some test results are presented and potential extensions to real-time image sequence processing, mobile objects tracking and mobile robotics are discussed.
CITATION STYLE
Louchet, J. (2000). From hough to darwin: An individual evolutionary strategy applied to artificial vision. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1829, pp. 145–161). Springer Verlag. https://doi.org/10.1007/10721187_11
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