Amarjot Singh, S.N. Omkar^,

DIC an extremely effective, non-contact analysis tool applied here to biomechanics research, in order to examine the strain pattern due to wrist extension and calf stretching experiments. The DIC code developed computes the in-plane strain with a correlation function, using pictures taken before and after extension, using a CCD camera. The shift between the initial picture and subsequent one is calculated by computing cross-correlation using FFT. The intermediate FFT cross correlation step can be computationally expensive depending upon the field of application, like biomechanics involving high computational power. This paper explains the methodology for harnessing the power of GPU for Image Processing and Computer Vision, thereby providing dramatic speedups on commodity, readily available graphics hardware. Further, a brief review of the DIC algorithms mapped to the GPU vision is presented. The latest NVIDIA CUDA programming model is explained in order to achieve parallelism without the need for graphics expertise. The paper also gives a detailed description of GPU architectures, GPU computing, the software environments used for programming followed by the advantages and disadvantages of the technique. Further, the paper proves the capability and superiority of powerful GPU computing with CPU on the basis of runtime analysis, applied to biomechanical experiments. The paper presents the results with huge speed ups in both biomechanical experiments.

digital image correlation, GPU computing, parallel computing, CUDA