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Stream Processing of a Neural Classifier II
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Author(s): M. Martínez-Zarzuela (University of Valladolid, Spain), F. J. Díaz Pernas (University of Valladolid, Spain), D. González Ortega (University of Valladolid, Spain), J. F. Díez Higuera (University of Valladolid, Spain)and M. Antón Rodríguez (University of Valladolid, Spain)
Copyright: 2009
Pages: 7
Source title:
Encyclopedia of Artificial Intelligence
Source Author(s)/Editor(s): Juan Ramón Rabuñal Dopico (University of A Coruña, Spain), Julian Dorado (University of A Coruña, Spain)and Alejandro Pazos (University of A Coruña, Spain)
DOI: 10.4018/978-1-59904-849-9.ch219
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Abstract
This article presents a real-time Fuzzy ART neural classifier for skin segmentation implemented on a Graphics Processing Unit (GPU). GPUs have evolved into powerful programmable processors, becoming increasingly used in time-dependent research fields such as dynamics simulation, database management, computer vision or image processing. GPUs are designed following a Stream Processing Model and each new generation of commodity graphics cards incorporates rather more powerful and flexible GPUs (Owens, 2005). In the last years General Purpose GPU (GPGPU) computing has established as a well-accepted application acceleration technique. The GPGPU phenomenon belongs to larger research areas: homogeneous and heterogenous multi-core computing. Research in these fields is driven by factors as the Moore’s Gap. Today’s uni-processors follow a 90/100 rule, where 90 percent of the processor is passive and 10 percent is doing active work. By contrast, multi-core processors try to follow the same general rule but with 10 percent passive and 90 percent active processors when working at full throughput. Single processor Central Processing Units (CPUs) were designed for executing general purpose programs comprised of sequential instructions operating on single data. Designers tried to optimize complex control requirements with minimum latency, thus many transistors in the chip are devoted to branch prediction, out of order execution and caching. In the article Stream Processing of a Neural Classifier I several terms and concepts related to GPGPU were introduced. A detailed description of the Fuzzy ART ANN implementation on a commodity graphics card, exploiting the GPU’s parallelism and vector capabilities, was given. In this article, the aforementioned Fuzzy ART GPU-designed implementation is configured for robust real-time skin recognition. Both learning and testing processes are done on the GPU using chrominance components in TSL (Tint, Saturation and Luminance) color space. The Fuzzy ART ANN implementation recognizes skin tone pixels at a rate of 270 fps on an NVIDIA GF7800GTX GPU.
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