Title :
Genetic algorithms for object recognition in a complex scene
Author :
Swets, Daniel L. ; Punch, Bill ; Weng, John
Author_Institution :
Michigan State Univ., East Lansing, MI, USA
Abstract :
A realworld computer vision module must deal with a wide variety of environmental parameters. Object recognition, one of the major tasks of this vision module, typically requires a preprocessing step to locate objects in the scenes that ought to be recognized. Genetic algorithms are a search technique for dealing with a very large search space, such as the one encountered in image segmentation or object recognition. The article describes a technique for using genetic algorithms to combine the image segmentation and object recognition steps for a complex scene. The results show that this approach is a viable method for successfully combining the image segmentation and object recognition steps for a computer vision module
Keywords :
computer vision; genetic algorithms; image segmentation; object recognition; search problems; complex scene; computer vision module; environmental parameters; genetic algorithms; image segmentation; object recognition; preprocessing step; search technique; Computer vision; Genetic algorithms; Image recognition; Image retrieval; Image segmentation; Information retrieval; Layout; Navigation; Object recognition; Robots;
Conference_Titel :
Image Processing, 1995. Proceedings., International Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-8186-7310-9
DOI :
10.1109/ICIP.1995.537549