Abstract
Local stereo matching algorithms are very popular in the recent years. The adaptive support weight algorithms can give high accuracy results such as global methods. This paper proposed a support aggregation approach for stereo matching that computed support weight in sparse support window mask. The improvement from the previous work is that the new support weight can reduce the computation into the fourth of the earlier work and help to reach the optimum correspondence. It means sparse support weight affects the time computation that is needed in stereo matching and optimizes the disparity. This support weight is used to accomplish the stereo matching evaluation using this method.
Highlights
Research in stereo vision has been an issue for several years, and it has again become very popular in recent years
The description of the stereo estimation difficulty is the method of evaluating a disparity map of two or more images of the scene
Results and Discussion using sparse window mask will give faster result compare to the previous method proposed in [5]
Summary
Research in stereo vision has been an issue for several years, and it has again become very popular in recent years. The local method optimizations are simpler compared to the global optimization algorithms for example graph cuts, belief propagation [5,6], that are more complicated and more accurate Researchers developed these methods in an energy optimization of the whole, and the aim is to minimize the estimated stereo correspondence energy. The local methods normally do not occupy iterative works to give fast and simplicity executions; because they do not consume full cost volume and compared to other approaches, they need lower memory. These methods are suitable for real-time applications on appropriate conditions. This work has a contribution to the stereo matching improvement, Irijanti / Communications in Science and Technology 2(1) (2017) 24-28 in speed up the computation
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