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The Research Of Clustering And Curve Evolution Methods With Application To Agricultural Product Image Segmentation

Posted on:2009-07-13Degree:DoctorType:Dissertation
Country:ChinaCandidate:Z P XieFull Text:PDF
GTID:1118360272457084Subject:Light Industry Information Technology and Engineering
Abstract/Summary:PDF Full Text Request
Nondestructive detection of agricultural product is a basic technique for quality assurance, so it is an important component of agricultural modernization. Along with the development of computer vision techniques, now many corresponding nondestructive detection methods are introduced and some good effect are obtained. In these methods, a primary task is to gain the exact object images, i.e., image segmentation, with which the quantitative analyses on product quality might be done. However, image segmentation is one of the difficult problems in computer vision; to improve the power of corresponding nondestructive detection, the image segmentation problems on agricultural products deserve to further exploration. In this paper, two types of agricultural products are mainly examined including peach shade images and natural fruit images.Clustering and curve evolution (level set) techniques are two prominent image segmentation tools, which are also used to image segmentations of agricultural products in this paper. In chapter 2 and chapter 3, we focus on clustering based image segmentation methods, where two new clustering algorithms are presented including: a new divisive hierarchical clustering algorithm SHPDHC and an enhanced possibilistic clustering algorithm (EPCM). These two new image segmentation methods are all based on the soft hyperspheric partition strategies proposed in the chapter 2 of this paper. Compared with several popular clustering algorithms, SHPDHC and EPCM have the better (or equal) clustering robustness and the ability to label the outliers of the dataset. The peach shade images are mainly examined in these two chapters, and the experimental results demonstrate that the better segmentation results can be obtained by above two new algorithms than several classical clustering based image segmentation methods for the most of such type of images. However, for the complex natural fruit images, the satisfied results can not be obtained by above two algorithms. These cases are further researched in the next two chapters.In chapter 4, a new image segmentation model integrating the fuzzy c-means clustering into Mumford-Shah model (FCMMS) is presented. Next, a coupled level set method integrating with Gaussian mixture model (GMMLS) for image segmentation is proposed in chapter 5. Above two new models can be viewed as a combination of clustering and level set image segmentation methods from the viewpoint of model object functional. They can effectively inherit the merits of two original methods, while the respective shortcomings are weakened. The application performance of these two new methods on peach shade images and natural fruit images are very satisfied with our expectations. Furthermore, in many cases, GMMLS can get the better segmentation performance than FCMMS due to its theoretical maturity. Nevertheless, according to the same framework of FCMMS, many extended versions integrating with current fuzzy clustering algorithms for image segmentation might be put forward, which will improve its practical application worthiness.In chapter 6, some advanced techniques of two new image segmentation methods are discussed. In many cases, these advanced techniques might pay the crucial roles and improve the segmentation performance in great part.
Keywords/Search Tags:Image segmentation, agricultural product image, Gaussian mixture model, level set, fuzzy clustering, soft hyperspheric partition, divisive hierarchical clustering, enhanced possibilistic clustering method
PDF Full Text Request
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