Statistical Recognition of Textured Patterns from Local Spectral Decomposition
2:00 p.m., Thursday, August 26, 1993
12th floor conference room, 719 Broadway
Unsupervised segmentation of an image into homogeneously textured regions and the recognition of known texture patterns have been important tasks in computer vision. This thesis presents a new set of algorithms and describes an implemented system which performs these tasks. Initial features are computed from a local multi-channel spectral decomposition of the image that is implemented with Gabor filters. Textures are not assumed to have a band limited frequency spectrum and there is no supposition regarding the image contents: it may contain some unknown texture patterns or regions with no textures at all. Stability of features is enhanced by employing a method for smoothing reliable measurements. Both recognition and segmentation procedures use robust statistical algorithms and are performed locally for small image patches. In particular, statistical classification with principal components is used for recognition. Further accuracy is achieved by employing spatial consistency constraints. When a slanted texture is projected on the image plane, the patterns undergo systematic changes in the density, area, and directionality of the texture elements. Recognition is made invariant to such transformations by representing texture classes with multiple descriptors. These descriptors are computed from carefully selected 3-D views of the patterns. Simulated projection of textures from arbitrary viewpoints are obtained by using a new texture mapping algorithm. The segmentation algorithm overcomes the non-stationarity of the features by employing a new, robust similarity measure. The performance of these methods is demonstrated by applying them to real images.