Presented a performance evaluation of different target architectures which are FPGA (Xilinx Virtex 2 Pro), GPU (Nvidia Geforce 8880) and CPU (Xeon dual core 3 GHz) performance based on back projection technique with several ways to speed up. They have reduced the computational time for these three different architectures through memory parallelized architecture.
Reviewed performance on FPGA (Altera Stratix III) and GPU (Nvidia Geforce GTX 280) performance based on strengths and limitations. The same DP algorithm has been applied to FPGA and GPU. The evaluations have been made on internal clocks, memory space, and disparity range of 128 and 256.
Reviewed performance on FPGA (Xilinx Virtex 4) and GPU (Nvidia Geforce GTX 7900) performance based on optical flow, stereo vision, and local image features including energy, orientation, and phase.
Presented performance comparison on FPGA (cyclone II) and GPU (Nvidia Geforce GTX 295) for image convolution processing. The performance is measured through clock cycle ratio and execution time.
Presented performance comparison on FPGA (Altera Stratix III), GPU (Nvidia Geforce GTX 295) and CPU (Xeon Quad Core W3520) for sliding window applications. They have used SAD algorithm for all three architectures. The performance comparison is based on energy efficiency and time consuming for image processing. Multiple windows sizes (4 × 4, 9 × 9, 16 × 16, 25 × 25, and 45 × 45) have been used to evaluate the best performance architecture.
Presented the speed performance of CPU (AMD Opteron processor 6366HE, Intel Xeon processor E5-2620) and GPU (Nvidia GeForce GTX 770) on the pyramidal stereo algorithm.
