N. Qaiser, M. Hussain, N. Qaiser, and M. Iqbal (Pakistan)
Moment invariants; Hu moments; Moment masks; Texture segmentation; Rotation invariance; Probabilistic Neural Network.
In machine vision, rotation invariant feature extraction is one of the most challenging texture analysis tasks, because pattern orientation itself contributes substantially to extracted features. As a consequence, the prime objective of such techniques has always been to extract features that maintain reasonable discrimination while achieving invariance. This paper addresses the issue by proposing a novel moment invariant based feature set for efficient rotation invariant texture segmentation. In deriving proposed feature set, a moment mask based technique has been employed innovatively and in the process only seven moment images are computed. A Fisher discriminant analysis based criterion has been devised to evaluate the rotation invariance/discrimination capabilities of proposed feature set in a systematic and quantitative way. The effectiveness of the solution has been verified through segmentation as well as supervised classification of benchmark textures taken from Brodatz album. The results show significant improvement when compared with an existing technique.
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