Unsupervised/supervised texture segmentation and its application to real-world data
Devesh Kumar Patel, T.J. Stonham · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1992
We present a texture segmentation technique which can be adapted for a broad category of applications. A Texture Co-occurrence Spectrum is generated for each texture sample by extracting information from all directions around a pixel. A Combined Unsupervised/Supervised clustering algorithm, then groups the Co-occurrence spectra in feature space into clusters representing homogeneous textured regions. The method as presented is applied to, and shown to be capable of segmenting natural texture composites and real-world images such as silica particle micrographs and aerial images.© (1992) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.