Haralick Texture Features Expanded Into The Spectral Domain
Abstract
Robert M. Haralick, et. al., described a technique for computing texture features based on gray-level spatial dependencies using a Gray Level Co-occurrence Matrix (GLCM)1. The traditional GLCM process quantizes a gray-scale image into a small number of discrete gray-level bins. The number and arrangement of spatially co-occurring gray-levels in an image is then statistically analyzed. The output of the traditional GLCM process is a gray-scale image with values corresponding to the intensity of the statistical measure. A method to calculate Spectral Texture is modeled on Haralick's texture features. This Spectral Texture Method uses spectral-similarity spatial dependencies (rather than gray-level spatial dependencies). In the Spectral Texture Method, a spectral image is quantized based on discrete spectral angle ranges. Each pixel in the image is compared to an exemplar spectrum, and a quantized image is created in which pixel values correspond to a spectral similarity value. Statistics are calculated on spatially co-occurring spectral-similarity values. Comparisons between Haralick Texture Features and the Spectral Texture Method results are made, and possible uses of Spectral Texture features are discussed.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 2006
- Accession Number
- ADA573658
Entities
People
- Angela M. Puetz
- R. C. Olsen
Organizations
- Naval Postgraduate School