Conclusion

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Conclusion#

We have seen that segmentation consists of dividing an image into several homogeneous regions. The homogeneity of a region is based on color, texture, contours, etc.

The methods for segmentation are very diverse, and we have only seen a few of them. Other existing methods include active contours (also known as snakes), level sets, Markovian models, etc. In addition to these “model-based” methods, deep learning methods obtained significant increases in the results.

At last, we list some criteria used to evaluate the quality of a segmentation method. These criteria are useful to comparing different segmentation methods, as presented in Lab 4.

References#

  • J.B. MacQueen, “Some Methods for classification and Analysis of Multivariate Observations”, Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, p. 281–297, 1967

  • N. Otsu, “A threshold selection method from gray-level histograms”, IEEE Transactions on Systems, Man, and Cybernetics, vol. 9, no 1, p. 62-66, 1979.

  • H. Steinhaus, “Sur la division des corps matériels en parties”, Bull. Acad. Polon. Sci., vol. 4, no 12, p. 801-804, 1957.

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