Recognition of two dimensional objects based on a novel generalized Hough transform method

In this paper, we present a model-based recognition system for identifying and estimating the pose of two dimensional arbitrary shapes subject to Euclidean and similarity transformations. A novel and effective paradigm based on the modification of the generalized Hough transform (GHT) is presented. In contrast to the classical GHT and the existing Hough-based methods, the storage space and computational complexity of the proposed method are reduced significantly by using an efficient voting scheme in conjunction with invariant geometric features. In the framework of the proposed system, the classical four dimensional Hough space is casted to a two dimensional Hough space. For many existing Hough-based methods, a scaling factor bound must be pre-specified based on prior knowledge of the given scene. In contrast, no such scaling factor bound is required in our proposed paradigm. Extensive experimental results are presented to verify the performance merits of our recognition system