This paper presents a tractable and empirically-accurate algorithm, along with an integrated framework, realizing a mid-level visual process for pattern recognition. The algorithm and overall framework takes advantage of hypotheses provided by a high-level visual process, thereby, attempting to extract a region in an image based on these hypotheses. The main focus is to recognize analytical, as well as non-analytical objects from binary images. The novel approach is based on a study of the Hough Transform and its generalized version. To show the overall usefulness of the framework, an extensive series of experiments were performed. An analysis of absolute and percent error accuracy is presented, indicating the effectiveness of this works approach. Finally, we review the main body of research in this area and identify the key patents that have emerged in this field.
Keywords: Distance transform, hough transform, inverse hough transform, hypothesis support, low-level vision, intermediatelevel vision, high-level vision, image understanding, PIHT, SI-PSF
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