A cooperative search algorithm for highly parallel implementation of RANSAC for model estimation on Tilera MIMD architecture

In this paper, we present a novel and fast algorithm for highly parallel implementation of the RANSAC on a many-core MIMD architecture, the Tilera. RANSAC is widely used in image processing applications for homography model estimation. It also represents one of the most computation intensive image processing tasks since it requires evaluation of a large number of models from a given data set. Therefore, increasing the efficiency in its computation by exploiting a massive degree of parallelism is the key enabling factor for many of its applications. Emerging highly parallel architectures such as Tilera provide such an opportunity of exploiting parallelism in many computations. In addition to its low power consumption and excellent GOPs per Watt performance, radiation-hard version of Tilera has also been developed which makes it one of the best candidates for future aerospace applications. In this paper, we first present a novel variant of the RANSAC by incorporating the concept of backtracking. We then present this variant as a cooperative search algorithm with excellent features for highly parallel implementation. In fact, our parallel implementation results in an asynchronous algorithm with a very limited communication requirement. Any processor performs a global broadcasting if and when it finds a partial solution better than previous one. We present our results for an extensive set of data with varying degree of outliers. Our practical results clearly demonstrate that excellent speedup in the computation is achieved by using 57 cores of the Tilera. In fact, for certain cases, our Cooperative Search Algorithms even achieve super-linear speedup, i.e., a speedup greater than 57. We discuss that such a result could have been indeed expected and can be used for other applications.