A NEW MINIMUM SPANNING TREE-BASED METHOD FOR SHAPE DESCRIPTION AND MATCHING WORKING IN DISCRETE COSINE SPACE
In this article, a new minimum spanning tree-based method for shape description and matching is proposed. Its properties are checked through the problem of graphical symbols recognition. Recognition invariance in front shift and multi-oriented noisy objects was studied in the context of small and low resolution binary images. The approach seems to have many desirable properties, even if the construction of graphs induces an expensive algorithmic cost. In order to reduce time computing, an alternative solution based on image compression concepts is provided. The recognition is realized in a compact space, namely the Discrete Cosine space. The use of block discrete cosine transform is discussed and justified. The experimental results led on the GREC2003 database show that the proposed method is characterized by a good discrimination power, a real robustness to noise, with an acceptable time computing. The position with a reference approach like Zernike moments is also investigated to measure the relevance of the proposed technique.
image compression discrete cosine transform document analysis graphics recognition region based shape descriptor feature extraction minimum spanning tree