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| Authors: | Samarjit Chakraborty, Sudipta De, Kalyanmoy Deb |
| Group: | Computer Engineering |
| Type: | Inproceedings |
| Title: | Model-Based Object Recognition from a Complex Binary Imagery using Genetic Algorithm |
| Year: | 1999 |
| Month: | May |
| Pub-Key: | CDD99 |
| Book Titel: | Lecture Notes in Computer Science. Proceedings of the 1st European Workshop on Evolutionary Computation in Image Analysis and Signal Processing (EvoIA |
| Volume: | 1596 |
| Pages: | 150-161 |
| Publisher: | Springer-Verlag |
| Abstract: | This paper describes a technique for model-based object recognition in a noisy and cluttered environment, by extending the work presented in an earlier study by the authors. In order to accurately model small irregularly shaped objects, the model and the image are represented by their binary edge maps, rather then approximating them with straight line segments. The problem is then formulated as that of finding the best describing match between a hypothesized object and the image. A special form of template matching is used to deal with the noisy environment, where the templates are generated on-line by a Genetic Algorithm. For experiments, two complex test images have been considered and the results when compared with standard techniques indicate the scope for further research in this direction. |
| Location: | Göteborg, Sweden |
| Resources: | [BibTeX] [Paper as PDF] |
