Hierarchical Decomposition Of Skeletons And Research Of The Significance Based On Mathematical Morphology

With the dramatic increase in image resources, object recognition has become the important issue in the image processing. The thesis originates from the project named object recognition in the multi-source image. Object recognition is closely related to object representation. With the in-depth study of various object representations, selecting the appropriate feature space is critical. The shape of the object contains a lot of visual information, becoming the most commonly used feature space. With the in-depth study, it is found that skeleton is widely used in object recognition. Extract skeleton, decompose it and organize segments into a graph. The graph contains all the information and lay foundation for the use of graph matching technology in object recognition. Meanwhile, set the threshold of significance to reduce the impact of noise on the skeleton.In the thesis, first study the basic theory and skeleton extraction. Study the basic concepts of skeleton and skeleton extraction algorithms based on the mathematical morphology. Achieve morphological skeleton transform algorithm. Propose the idea to improve the algorithm.Then study the skeleton decomposition technique. By contrast, choose the hierarchical decomposition. Through the experiment, analyze the deficiencies existing in the original algorithm, and through additional definition, corrective action, the introduction of ring sets, improve the effectiveness and efficiency. Finally get the meaningful and complete segment.Study the definition of the significance. Study the definitions based on different skeleton extraction algorithms and decomposition algorithms. Define the significance according to the skeleton extraction and hierarchical decomposition.