Accelerating the Computation of GLCM AND Haralick Texture Features on Reconfigurable Hardware (WA-P6)

Author(s) : Muhammad Atif Tahir (Queens University Belfast, UK) Ahmed Bouridane (Queens University Belfast, UK) Fatih Kurugollu (Queens University Belfast, UK) Abbes Amira (Queens University Belfast, UK)

Abstract : Grey Level Co-occurrence Matrix (GLCM), one of the best known tool for texture analysis, estimate image properties related to second-order statistics. These image properties commonly known as Haralick 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 and Haralick texture features. The performances of the proposed co-processor are then assessed and compared against a microprocessor based solution.

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