An efficient implementation of GLCM algorithm in FPGA
Hassene Mnif2018, 2018 International Conference on Internet of Things, Embedded Systems and Communications (IINTEC)
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Abstract
This paper presents hardware (HW) architecture for fast parallel computation of Gray Level Cooccurrence Matrix (GLCM) in high throughput image analysis applications. GLCM has proven to be a powerful basis for use in texture classification. Various textural parameters calculated from the GLCM help understand the details about the overall image content. However, the calculation of GLCM is very computationally intensive. In this paper, an FPGA accelerator for fast calculation of GLCM is designed and implemented. We propose an FPGA-based architecture for parallel computation of symmetric cooccurrence matrices. This architecture was implemented on a Xilinx Zedboard and Virtex 5 FPGAs using Vivado HLS. The performance is then compared against other implementations. The validation results show an optimization on the order of 33% in latency number by contribution to the literature implementation.
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{ a.tahir,m.a.roula,a.bouridane,f.kurugollu,a.amira} @qub.ac.uk 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 he 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 Clike programming language for hardware design, has been used for the FF'GA implementation of GLCM texture features measurement. Results show that the FF'GA has better speed performances when compared to a general purpose processor for the extraction of GLCM features.
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