Extraction of visual landmarks using improved feature matching technique for stereo vision applications

Stereo vision is becoming more and more common in three-dimensional visualization, autonomous vehicle navigation, path finding, object recognition, and other computer vision applications. In this paper, we present an approach for extracting visual landmarks from images acquired by a stereoscopic camera. The scale invariant feature transform (SIFT) technique with self-organizing map is used to detect and recognize visual landmarks. Our methodology is based on winner calculation technique, and the main idea is to keep in the database only distinctive features or landmarks in order to minimize detection time. We will demonstrate that this methodology is more efficient than ordinary SIFT algorithm or speeded up robust features (SURF) matching technique. Improved feature group matching with computation time better than SIFT and the SURF has been observed in the experiments with a variety of stereo image pairs.