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Research on Image Segmentation and Scene Understanding Using Discriminative Learning Method
Author: WangJianLe
Tutor: HuangYaPing
School: Beijing Jiaotong University
Course: Computer Science and Technology
Keywords: Texture features Geometric characteristics Discriminant Texture image segmentation Scene Image Understanding
CLC: TP391.41
Type: Master's thesis
Year: 2011
Downloads: 103
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Abstract
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Image segmentation is a key and difficult issues of the computer vision field , this paper, a new fusion of texture and geometric characteristics of the framework of image segmentation , image segmentation problem into a classification problem to be solved . Handled by different objects , further study of the problem of the choice of features and discriminant learning method , and in texture image segmentation , outdoor scenes and indoor scene images were discussed in depth in the understanding of three typical applications . The main work of the paper are: a fusion of texture and geometric features discriminant framework for image segmentation , optimization target segmentation problem into a classification problem . For texture image segmentation , further divided into two sub-problems interactive segmentation based on texture features and texton - based scene segmentation study . Gradient histogram features and boosting algorithm for interactive segmentation based on texture features used to split processing ; based on texton scene segmentation using texton feature (textons) and Joint Boosting algorithm to split . Experimental results show that the texture feature - based discriminant learning method , can effectively distinguish the texture pattern in image recognition and segmentation and image blocks in various scenes images of different semantic categories . 3 , on the basis of the above study , further discussion of the three-dimensional scene to understand the problem . Image for the outdoor scenes and indoor scene images , the proposed algorithm based on the geometric characteristics of image segmentation and scene understanding . The experimental results show that even in a variety of complex scenes , the method was still able to get a better segmentation results .
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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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