Empirical Evaluation Methods in Computer Vision by Henrik I. Christensen, Jonathon Phillips, H. I. Christensen,

By Henrik I. Christensen, Jonathon Phillips, H. I. Christensen, P. Jonathon Phillips

This article presents finished insurance of tools for the empirical evaluate of laptop imaginative and prescient ideas. the sensible use of desktop imaginative and prescient calls for empirical review to make sure that the general approach has a assured functionality. The paintings includes articles that hide the layout of experiments for review, diversity picture segmentation, the overview of face acceptance and diffusion tools, photograph matching utilizing correlation tools, and the functionality of scientific photograph processing algorithms.

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The training set is used to fix the parameters of algorithms, while performance metrics are computed on images from the test set using the fixed parameters. The training/test set partitioning has not gained much attention so far and is typically done randomly. 2). The resulting discrete optimization task turns out to a MVhard problem. Thus, we have to resort to approximate approaches that find suboptimal solutions in reasonable time. 3). 5). The training/test image set design technique proposed in this paper is very general and can be easily applied to other problem domains.

The worst partition provides us, on the one hand, an extreme test situation, in which the training and test images have quite different characteristics with respect to the angle feature. On the other hand, it represents an upper limit of the badness of manual partitions which a human operator may produce in his efforts to create a good partition. The ordinary genetic algorithm is faster than the hybrid one. The results are slightly worse than, but comparable to, those by the hybrid GA. Thus, in all cases both genetic algorithms are able to improve the manual partition.

Machine Vision and Applications, 9(5/6): 215-340, 1997. 5. L. L. Wainwright, Using LibGA to develop genetic algorithms for solving combinatorial optimization problems, in L. ), Practical Handbook of Genetic Algorithms: Applications, Volume I, 143-172, CRC Press, 1995. 6. J. C. R. Hancock, Inexact graph matching using genetic search, Pattern Recognition, 30(6): 953-970, 1997. 7. A. M. Spears, Using genetic algorithms to solve NPcomplete problems, Proc. of 3rd Int. Conf. on Genetic Algorithms, Fairfax, VA, 124-132, 1989.

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