Abstract:To solve the general problems surrounding the application of genetic algorithms in stereo matching, two measures are proposed. Firstly, the strategy of simplified population-based incremental learning (PBIL) is adopted to reduce the problems with memory consumption and search inefficiency, and a scheme for controlling the distance of neighbors for disparity smoothness is inserted to obtain a wide-area consistency of disparities. In addition, an alternative version of the proposed algorithm, without the use of a probability vector, is also presented for simpler set-ups. Secondly, programmable graphics-hardware (GPU) consists of multiple multi-processors and has a powerful parallelism which can perform operations in parallel at low cost. Therefore, in order to decrease the running time further, a model of the proposed algorithm, which can be run on programmable graphics-hardware (GPU), is presented for the first time. The algorithms are implemented on the CPU as well as on the GPU and are evaluated by experiments. The experimental results show that the proposed algorithm offers better performance than traditional BMA methods with a deliberate relaxation and its modified version in terms of both running speed and stability. The comparison of computation times for the algorithm both on the GPU and the CPU shows that the former has more speed-up than the latter, the bigger the image size is.
To solve the general problems of genetic algorithms applied in stereo matching, two measures are proposed. Firstly, the strategy of the simplified population-based incremental learning (PBIL) is adopted to decrease the problems in memory consumption and searching inefficiency, as well as a scheme controlling the distance of neighbors for disparity smoothness is inserted to obtain a wide-area consistency of disparities. In addition, an alternative version of the proposed algorithm without using a probability vector is also presented for simpler set-ups. Secondly, to decrease the running time further, a model of the proposed algorithm which can be run on programmable graphics-hardware (GPU) is newly given. The algorithms are implemented on the CPU as well as the GPU and evaluated by experiments. The experimental results show the proposed algorithm has better performance than traditional BMA methods with a deliberate relaxation and its modified version in both running speed and stability. The comparison in computation times for the algorithm both on GPU and CPU shows that the former has more speed-up than the latter, the bigger the image size is.
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