scholarly journals Local Contrast Enhancement using Local Standard Deviation

2012 ◽  
Vol 47 (15) ◽  
pp. 39-44 ◽  
Author(s):  
S. SomorjeetSingh ◽  
Th. Tangkeshwar Singh ◽  
H. Mamata Devi ◽  
Tejmani Sinam
Keyword(s):  
Standard Deviation ◽  
Contrast Enhancement ◽  
Local Contrast ◽  
Local Standard ◽  
Local Contrast Enhancement ◽  
Local Standard Deviation

scholarly journals Clinical evaluation of ‘local contrast enhancement’ for oral fluorescein angiograms

Eye ◽  
2000 ◽  
Vol 14 (3) ◽  
pp. 318-318 ◽  
Author(s):  
Richard S B Newsom ◽  
Chanjira Sinthanayothin ◽  
James Boyce ◽  
Anthony G Casswell ◽  
Tom H Williamson
Keyword(s):  
Contrast Enhancement ◽  
Clinical Evaluation ◽  
Local Contrast ◽  
Local Contrast Enhancement

Shape Defect Detection using Local Standard Deviation and Rule-Based Classifier for Bottle Quality Inspection

2017 ◽  
Vol 8 (1) ◽  
pp. 107
Author(s):  
Norhashimah Mohd Saad ◽  
Nor Nabilah Syazana Abdul Rahma ◽  
Abdul Rahim Abdullah ◽  
Mohd Juzaila Abd Latif
Keyword(s):  
Standard Deviation ◽  
Product Quality ◽  
Quality Inspection ◽  
Rule Based ◽  
Low Contrast ◽  
Contrast Gain ◽  
Local Standard ◽  
Local Standard Deviation ◽  
Rule Based Classifier ◽  
Ace Algorithm

This paper presents shape analysis using Local Standard Deviation (LSD) technique to detect shape defect of the bottle for product quality inspection. The proposed analysis framework includes segmentation, feature extraction, and classification. The shape of the bottle was segmented using LSD technique in order to obtain higher enhancement at the low contrast area and low enhancement at the high contrast area. <span lang="EN-MY">The contrast gain that was applied in Adaptive Contrast Enhancement (ACE) algorithm, was presented inversely proportional to LSD in order to detect and eliminate background noise at the bottle edge. After the segmentation process, the parameters of the bottle shape such as height, width, area, and extent were extracted and applied in classification stage. The rule-based classifier was used to classify the shape of the bottle either good or defect. The offline experimental results exhibit superior segmentation on performance with 100% accuracy for 100 sample images. This shows that the LSD could be an effective technique to monitor the product quality.</span>

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