Some Extensions of the Converging Squares Algorithm for Image Feature Analysis
In [1], the converging squares algorithm was introduced as a method designed to effectively and efficiently locate peaks in data of two dimensions or higher. In this correspondence, the performance of the algorithm on a signal in noise is examined, and some extensions of the algorithm-beyond peak-picking-are introduced. The minimum-area enclosing square is one extension, which locates an image region in a uniform background, and finds the smallest square which entirely encloses it. The maximum-difference enclosing square is another extension by which a global feature of the image is found which separates it into a foreground square region and background region, based on the maximum statistical difference between the two. Some applications of these extensions are shown, including object location, tracking of a moving object, and adaptive binarization.