Abstract
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 three-dimensional 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.
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Louchet, J. (2000). From Hough to Darwin: An Individual Evolutionary Strategy Applied to Artificial Vision. In: Fonlupt, C., Hao, JK., Lutton, E., Schoenauer, M., Ronald, E. (eds) Artificial Evolution. AE 1999. Lecture Notes in Computer Science, vol 1829. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10721187_11
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DOI: https://doi.org/10.1007/10721187_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-67846-5
Online ISBN: 978-3-540-44908-9
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