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GPU Implementation of Deformable Pattern Recognition Using Prototype-parallel Displacement Computation

机译:GPU实现变形模式识别使用Prototype-parallel位移计算

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摘要

In this paper, for the reduction of the computation time of a deformable approach to pattern recognition, prototype-parallel displacement computation on GPUs (PPDC-GPU) is proposed. The displacement computation used in this study has the virtue of simplicity and consists of locally parallel processing, therefore it is expected to be suitable for the implementation on graphics processing units (GPUs). In the proposed method, large plates of image and displacement function are generated from input images, prototypes, and displacement functions on the main memory, and then these plates are transferred collectively to the video memory. After the parallel computation of displacement between the input image plate and the prototype image plate on the GPU, the displacement function plates are transferred back to the main memory. The simulation results show that PPDC-GPU reduces the computation time to less than 10% of the ordinary implementation on CPUs. This study especially focused on handwritten character recognition, since it is one of the most fundamental and important problems in the field of computer vision and pattern recognition. However the proposed framework can be widely applied to other problems, for instance, face recognition or object recognition.
机译:本文的减少变形方法的计算时间模式识别、prototype-parallel在gpu (PPDC-GPU)位移计算建议。本研究有简单和的美德由局部并行处理、因此,将适合的实现图形处理单元(gpu)。形象和位移函数生成从输入图像、原型和位移这些功能在主内存,然后板块集体转移到视频内存。输入图像板和之间的位移板”这一原型意象的GPU,位移函数板块转移回来到主内存。PPDC-GPU减少了计算时间不到10%的普通的实现cpu。手写字符识别的,因为它是的一个最基本的和重要的在计算机视觉领域和问题模式识别。可广泛应用于其他框架问题,例如,人脸识别或对象识别。

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