Detection of a compound object is a critical problem in target recognition. For example, buildings form an important class of shapes whose recognition is important in many remote sensing based applications. Due to the coarse resolution of imaging sensors, adjacent buildings in the scenes appear as a single compound shape object. These compound objects can be represented as the union of a set of disjoint rectilinear shaped objects. Separating the individual buildings from the resulting compound objects in a segmented image is often difficult but important nevertheless. In this paper we propose a new and efficient technique to decompose a compound shape into a set of simple rectilinear shapes. First, the true interior and exterior corner points of the compound object are extracted. A modified corner detector based on polygonal approximation is proposed to accurately determine the boundaries of compound shapes. The compound shape is then split at the interior corner points to minimize the difference between the perimeter of the compound object and the sum of the perimeters of the decomposed objects. We have systematically compared the results our algorithm with those of existing approaches and the results show that the proposed algorithm is more accurate than the algorithms in the literature in terms of accuracy of perimeter estimation and computational cost. Keywords: Compound object, pattern recognition, feature extraction, corner point detection, shape analysis, building extraction Full Text: PDF.
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