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2 February 2009 Assessing fabric stain release with a GPU implementation of statistical snakes
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Proceedings Volume 7251, Image Processing: Machine Vision Applications II; 725108 (2009) https://doi.org/10.1117/12.806370
Event: IS&T/SPIE Electronic Imaging, 2009, San Jose, California, United States
Abstract
Stain release is the degree to which a stained substrate approaches its original unsoiled appearance as a result of care procedure. Stain release has a significant impact on the pricing of the fabric and, hence, needs to be quantified in an objective manner. In this paper, an automatic approach for the objective assessment of fabric stain release that utilizes region-based statistical snakes, is presented. This deformable contour approach employs a pressure energy term in the parametric snake model in conjunction with statistical information (hence, statistical snakes) extracted from the image to segment the stain and subsequently assign a stain release grade. This algorithm has been parallelized on a General Purpose Graphical Processing Unit (GPGPU) for accelerated and simultaneous segmentation of multiple stains on a fabric. The computational power of the GPGPU is attributed to its hardware and software architecture, which enables multiple and identical snake kernels to be processed in parallel on several streaming processors. The detection and segmentation results of this machine vision scheme are illustrated as part of the validation study. These results establish the efficacy of the proposed approach in producing accurate results in a repeatable manner. In addition, this paper presents a comparison between the benchmarking results for the algorithm on the CPU and the GPGPU.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
S. Kamalakannan, A. Gururajan, M. Shahriar, M. M. Hill, J. Anderson, H. Sari-Sarraf, and E. F. Hequet "Assessing fabric stain release with a GPU implementation of statistical snakes", Proc. SPIE 7251, Image Processing: Machine Vision Applications II, 725108 (2 February 2009); https://doi.org/10.1117/12.806370
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