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
Keyword(s):  
Image Similarity ◽  
Similarity Analysis ◽  
Fuzzy Decision ◽  
Information Compression

Enhanced image similarity analysis system in digital pathology

2017 ◽  
Vol 76 (23) ◽  
pp. 25477-25494
Author(s):  
Jae-Gu Lee ◽  
Kyung-Chan Choi ◽  
Seung-Ho Yeon ◽  
Jeong Won Kim ◽  
Young-Woong Ko
Keyword(s):  
Digital Pathology ◽  
Image Similarity ◽  
Similarity Analysis ◽  
Analysis System

Image similarity analysis on MIE encryption algorithm

2010 ◽  
Author(s):  
Xiaoqiang Zhang ◽  
Shilong Ma ◽  
Guiliang Zhu ◽  
Weiping Wang ◽  
Mengmeng Wang
Keyword(s):  
Image Similarity ◽  
Encryption Algorithm ◽  
Similarity Analysis

The Image Similarity Analysis Technique Applied to the Tool Wear Monitoring in Face Milling

2006 ◽  
Vol 315-316 ◽  
pp. 628-631
Author(s):  
Yu Teng Liang ◽  
C.J. Lo ◽  
W.C. Chen
Keyword(s):  
Tool Wear ◽  
Vision System ◽  
Image Data ◽  
Face Milling ◽  
Image Similarity ◽  
Similarity Analysis ◽  
Analysis Technique ◽  
Milling Operation ◽  
The Face ◽  
Wear Monitoring

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.

Human-Assisted Fuzzy Image Similarity Analysis Based on Information Compression

2009 ◽  
Vol 13 (3) ◽  
pp. 255-261 ◽  
Author(s):  
Gancho Vachkov ◽  
Keyword(s):  
Fuzzy Inference ◽  
Learning Algorithm ◽  
Optimization Criterion ◽  
Similarity Analysis ◽  
Information Compression ◽  
Inference Procedure ◽  
Fuzzy Similarity ◽  
Similar Images ◽  
Computation Scheme ◽  
Fuzzy Image

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.

Mapping Soil Organic Carbon Concentration for Multiple Fields with Image Similarity Analysis

2008 ◽  
Vol 72 (1) ◽  
pp. 186-193 ◽  
Author(s):  
Feng Chen ◽  
David E. Kissel ◽  
Larry T. West ◽  
W. Adkins ◽  
Doug Rickman ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Carbon Concentration ◽  
Image Similarity ◽  
Similarity Analysis ◽  
Organic Carbon Concentration
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