An improved region-growth algorithm for dense matching

Purpose: Improve the accuracy and speed of the region-growth algorithm between two 2D images. Design/methodology/approach: The algorithm includes two parts: the selection of seeds points and propagation. Some improvements are made in each one. For the first part, the best-first strategy is used to assure the accuracy of seeds. The epipolar line constraint and continuity constraint reduce the double phase matching course into single phase matching. For the second one, a dynamic and adaptive window is adopted instead of the large window. Findings: In the first section, the process of searching and the computational duties are decreased in large extent. And in the second one, the adaptive window makes the searching course more efficient in time and space. It is really difficult to get the most suitable window to search for the points as soon as possible. If it can be easily got, it will advance the efficiency of search. It is the future work. Practical implications: The method can be used in many different images, such as the structural images and the facial images. Originality/value: The original value is the region-growth algorithm, and in this paper I made some betterments to advance the efficiency and accuracy.