Human-guided fuzzy decision for image similarity analysis and classification based on information compression

2010 ◽  
Vol 26 (3) ◽  
pp. 246-261
Author(s):  
Gancho Vachkov
2017 ◽  
Vol 76 (23) ◽  
pp. 25477-25494
Author(s):  
Jae-Gu Lee ◽  
Kyung-Chan Choi ◽  
Seung-Ho Yeon ◽  
Jeong Won Kim ◽  
Young-Woong Ko

Author(s):  
Xiaoqiang Zhang ◽  
Shilong Ma ◽  
Guiliang Zhu ◽  
Weiping Wang ◽  
Mengmeng Wang

2006 ◽  
Vol 315-316 ◽  
pp. 628-631
Author(s):  
Yu Teng Liang ◽  
C.J. Lo ◽  
W.C. Chen

The purpose of this paper is to monitor the tool wear based on the image data of cutting tool in the face milling operation. The surface images of the different coated inserted blade cutters are captured using a machine vision system incorporating with the mutual information and image similarity analysis technique for processing the images. The milling test is designed by using Taguchi’s method. The experimental results indicate that the coating layer factor is recognized to make the most significant contribution to the over all performance. The TiAlN-surface multilayer coated inserted blade cutter has the least wear rate amongst these coated milling cutters and has the longest tool life in this experiment.


Author(s):  
Gancho Vachkov ◽  

The fuzzy similarity analysis we propose in this paper is used for unsupervised image classification. We introduce a special growing unsupervised learning algorithm for information compression (granulation) of the original “raw data” (the RGB pixels) of an image with a smaller number of neurons (information granules). Two important parameters are extracted from each image, namely the center of gravity (COG) and the model volume of the image, taken as the number of neurons obtained from information compression. These two features are used as inputs for special fuzzy inference for numerically calculating the degree of similarity between a pair of images. The fuzzy inference procedure can be tuned based on a predefined human preference (list of similar images), thus performing human-assisted similarity analysis. The choice of the optimization algorithm and the selection of the optimization criterion are questions open to the user to answer. The proposed computation scheme for similarity analysis is illustrated on a test example of 16 flower images and results are discussed.


2008 ◽  
Vol 72 (1) ◽  
pp. 186-193 ◽  
Author(s):  
Feng Chen ◽  
David E. Kissel ◽  
Larry T. West ◽  
W. Adkins ◽  
Doug Rickman ◽  
...  

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