Keypoint detection by cascaded fast

When the FAST method for detecting corner features at high speed is applied to images that include complex textures (regions that include foliage, shrubbery, etc.), many corners that are not needed for object recognition are detected because FAST defines corner features on the basis of a 16-pixel bounding circle. To overcome that problem, we propose the Cascaded FAST that defines corners on the basis of similarity in terms of intensity, continuity and orientation in a broader range of areas (20, 16, and 12 pixel bounding circles). Also, cascading three decision trees trained by the FAST approach enables high-speed corner detection in which non-corners are eliminated early in the process. Furthermore, Cascaded FAST determines scale by using an image pyramid and determines orientation at high speed by using a framework for referencing surrounding pixels.

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