Ceramic Microstructure Image Segmentation by Mean Shift

2013 ◽  
Vol 423-426 ◽  
pp. 2602-2605 ◽  
Author(s):  
Hua Hui Cai ◽  
Yan Cheng ◽  
Bing Xiang Liu

In order to effectively assist the researchers conduct quantitative analysis of ceramic microstructures, a segmentation algorithm based on mean shift is used for the ceramic microstructure image. Since the collection and transfer process of microscopic image will inevitably be subject to uneven distribution of light, electronic noise and other interference factors which make the image quality deterioration, it is necessary to reduce noises and enhance edges for ceramic microscopic image processing at first. Therefore, the median filter is used to remove the noises in the ceramic microstructure images. Then the component with similar feature is separated and merged by the mean shift segmentation algorithm. Experiments show the proposed algorithm of using median filter and mean shift clustering gives satisfactory results.

2014 ◽  
Vol 8 (S1) ◽  
pp. 1-3 ◽  
Author(s):  
Kivanc Kose ◽  
Rengul Cetin-Atalay ◽  
A. Enis Cetin

Author(s):  
Vinh-Thong Ta ◽  
Olivier Lézoray ◽  
Abderrahim Elmoataz

The authors present an overview of part of their work on graph-based regularization. Introduced first in order to smooth and filter images, the authors have extended these methods to address semi-supervised clustering and segmentation of any discrete domain that can be represented by a graph of arbitrary structure. This framework unifies, within a same formulation, methods from machine learning and image processing communities. In this chapter, the authors propose to show how these graph-based approaches can lead to a useful set of tools that can be combined altogether to address various image processing problems in pathology such as cytological and histological image filtering, segmentation and classification.


2008 ◽  
Vol 3 (Suppl 1) ◽  
pp. S18 ◽  
Author(s):  
Gloria Bueno ◽  
Roberto González ◽  
Oscar Déniz ◽  
Jesús González ◽  
Marcial García-Rojo

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