Ahmed Bouridane

An FPGA based coprocessor for calculating Grey level co-occurrence matrix

Grey level co-occurrence matrix (GLCM), one of the beat known tool for texture analysis, 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. However, their computations are highly intensive especially for very large images such as medical ones. Therefore, methods to accelerate their computations are highly desired. This paper proposes the use of reconfigurable hardware to accelerate the calculation of GLCM. The performances of the co-processor proposed are then assessed and compared against a microprocessor based solution