An FPGA based co-processor for GLCM texture features measurement

Abstract

Gray Level Co-occurrence Matrix (GLCM), one of the best known texture analysis methods, estimates image properties related to second-order statistics. These image properties commonly known as texture features can be used for image classification, image segmentation, and remote sensing applications. In this paper, we present an FPGA based co-processor to accelerate the extraction of texture features from GLCM. Handel-C, a recently developed C-like programming language for hardware design, has been used for the FPGA implementation of GLCM texture features measurement. Results show that the FPGA has better speed performances when compared to a general purpose processor for the extraction of GLCM features.