GPU implementation of deformable pattern recognition using prototype-parallel displacement computation

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 suitable for the implementation on graphical 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 computing the 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.