Edge Detection in Hyperspectral Imaging: Multivariate Statistical Approaches
Edge detection is well developed area of image analysis. Many various kinds of techniques were designed for one-channel images. Also, a considerable attention was paid to edge detection in color, multispectral, and hyperspectral images. However, there are still many open issues in edge detection in multichannel images. For example, even the definition of multichannel edge is rather empirical and is not well established. In this paper statistical pattern recognition methodology is used to approach the problem of edge detection by considering image pixels as points in a multidimensional feature space. Appropriate multivariate techniques are used to retrieve information which can be useful for edge detection. The proposed approaches were tested on the real-world data.
KeywordsProbability Density Function Edge Detection Hyperspectral Image Joint Probability Density Function Multivariate Statistical Approach
- 7.Lim, J.S.: Two-dimensional signal and image processing. Prentice-Hall, Englewood Cliffs (1990)Google Scholar
- 8.Kanade, T., Shafer, S.: Image understanding research at cmu. In: Proceedings of an Image Understanding Workshop, vol. I, pp. 32–40 (1987)Google Scholar
- 9.Tao, H., Huang, T.S.: Color image edge detection using cluster analysis. In: Proceedings of the 1997 IEEE International Conference on Image Processing (ICIP 1997), vol. 1, pp. 834–837 (1997)Google Scholar
- 12.Alshatti, W., Lambert, P.: Using eigenvectors of a vector field for deriving a second directional derivative operator for color images. In: Chetverikov, D., Kropatsch, W.G. (eds.) CAIP 1993. LNCS, vol. 719, pp. 149–156. Springer, Heidelberg (1993)Google Scholar
- 14.Pietikainen, M., Harwood, D.: Edge information in color images based on histograms of differences. In: Proceedings of the 8th International Conference on Pattern Recognition Conference (ICPR 1986), vol. 1, pp. 594–596 (1986)Google Scholar