Texture And Contour-Based Object Segmentation

With the development of computer technology, digital image technology has become the principal mean of the image vision research. But now human beings are still unable to build an autonomous vision system which is able to meet the standards of biological vision. There is one important factor that the biological visual system can easily complete complex image segmentation, on the other hand, the segmentation of machine vision is very difficult to meet the appropriate speed and accuracy. Therefore, for the further development of machine vision technology, it is necessary to study the image segmentation technique to find a rapid and effective method.In this thesis, combined with the priori knowledge and objective information,and guided by mechanim of human vision, more works has been done as follows: First, the objective of this work is the detection of object classes. The thesis first develops a novel technique to extract class-discriminative boundary fragments and the texture features near the boundary then boosting is used to select discriminative boundary fragments (weak detectors) to form a strong"Boundary-Fragment-Model"detector. A new appearance model is built with those entire detectors and the texture features. And then, the boundary fragment and the texture features and used to complete detection. To the end, a new fast cluster algorithm is used to deal with the centroid image. The generative aspect of the model is used to determine an approximate segmentation. In addition, this thesis presents an extensive evaluation of the new method on a series of test images and compares its performance with the existed methods which are from the literature. As it is shown in the experiment, the new method outperforms previously published methods with the overlap part of the object in multiple-object scene.And then, this thesis researches the differerce among the three kinds of snake model. According to the characteristics of the snake model, this thesis takes the results of the T&C-SEG method as the initial contour of the snake model. Then, the GVF-Balloon Snake model is choosen to complete the segmentation. Experiments show that this method can enhance the effect of segmentation.Finally, this thesis analyzes the advantages and disadvantages between bottom-up segmentation method and top-down segmentation method, and researches the modular segmentation method between snake model and the template-based image segmentation method. According to the characteristics of natural image, this thesis develops a combined image segmentation method. Experiments show that the method can significantly enhance the efficiency of segmentation.