Efficient stereo vision algorithms for resource-limited systems
In most circumstances, determining an acceptable trade-off between speed and accuracy when selecting a stereo vision algorithm for implementation is dependent on the target application. This work attempts to provide a perspective on the efficiency of existing real-time stereo vision algorithms in terms of this trade-off. This work also provides an example of modifying an existing highly accurate stereo vision algorithm to increase its runtime performance while trying to limit the loss in accuracy. The modifications can be used to increase efficiency of several other local stereo vision algorithms due to sharing some common components. Such an example demonstrates the challenge of making efficient trade-offs in accuracy for runtime performance. It is shown that the modifications resulted in an 8X speedup over the original algorithm, with accuracy results comparable to existing real-time algorithms.
efficient stereo vision algorithm dense disparity stereo vision stereo image-processing for resource-limited systems intensity profile shape matching