high performance computing on graphics processing units: hgpu.org

Low level computer vision algorithms have extreme computational requirements. In this work we compare two real-time architectures developed using FPGA and GPU devices for the computation of phase-based optical flow, stereo and local image features (energy, orientation and phase). The presented approach requires a massive degree of parallelism to achieve real-time performance and allows us to compare FPGA and GPU design strategies and trade-offs in a much more complex scenario than previous contributions. Based on this analysis, we provide suggestions to real-time system designers for selecting the most suitable technology, and for optimizing system development on this platform, for a number of diverse applications.