In this paper, an algorithm for grouping edges belonging to straight lines is presented. The algorithm uses as input data a labeled set of edge points represented by a list of coordinate-label pairs. The output is a graph whose nodes are rectilinear segments linked by relational properties. Collinearity, convergence, and parallelism can be easily taken into account. The main novelty of the method lies in extending the use of the Hough transform to a symbolic domain (i.e., labeled edges); it is shown that edge labeling can be used to partition the Hough space and to isolate contributions coming from different image areas. Moreover, it is demonstrated that a simple focusing mechanism can be applied (in order to speed up the matching with 3D models) by using relational properties provided by the output graph. In order to confirm the algorithm′s performances, results on synthetic images containing randomly generated textures of straight lines are presented. Finally, a complex road image is considered to point out the advantages of using the proposed representation and the attention-focusing mechanism to solve real-world problems.
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