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Detecting Rectangular Objects in Urban Imagery - A Re-Segmentation Approach

Image segmentation is a broad area, which covers strategies for splitting one input image into its components. This paper aims to present a re-segmentation approach applied to urban imagery, where the interest elements (houses roofs) are considered to have a rectangular shape. Our technique finds and generates rectangular objects, leaving the remaining objects as background. With an over-segmented image we connect adjacent objects in a graph structure, known as Region Adjacency Graph – RAG. We then go into the graph, searching for best cuts that may result in segments more rectangular, in a relaxation-like approach. Graph search considers information about object class, through a pre-classification stage using Self-Organizing Maps algorithm. Results show that the method was able to find rectangular elements, according user-defined parameters, such as maximum levels of graph searching and minimum degree of rectangularity for interest objects.