Independent component analysis based filter design for defect detection in low-contrast textured images

2006 ◽  
Author(s):  
Du-Ming Tsai ◽  
Yan-Hsin Tseng ◽  
Shin-Min Chao ◽  
Chao-Hsuan Yen
Keyword(s):  
Independent Component Analysis ◽  
Defect Detection ◽  
Filter Design ◽  
Component Analysis ◽  
Independent Component ◽  
Low Contrast

An Independent Component Analysis Based Defect Detection for the OLED Display

2012 ◽  
Vol 605-607 ◽  
pp. 724-728 ◽  
Author(s):  
Zhi Liang Wang ◽  
Jian Gao ◽  
Chuan Xia Jian ◽  
Yao Cong Liang ◽  
Yu Cen
Keyword(s):  
Independent Component Analysis ◽  
Defect Detection ◽  
Detection System ◽  
Component Analysis ◽  
Rapid Identification ◽  
Independent Component ◽  
Display Device ◽  
Low Contrast ◽  
Light Emitting ◽  
Mixing Matrix

Organic Light Emitting Displays (OLED) is a new type of display device which has become increasingly attractive and popular. Due to the complex manufacturing process, various defects may exist on the OLED panel. These defects have the characteristics of fuzzy boundaries, irregular in shape, low contrast with background and they are mixed with the texture background increasing the difficulty of a rapid identification. In this paper, we proposed an approach to detect these defects based on the model of independent component analysis (ICA). The ICA model is applied to a perfect OLED image to determine the de-mixing matrix and its corresponding independent components (ICs). Through the choice of a proper ICi row vector, the new de-mixing matrix is generated which contains only uniform information and is used to reconstruct the OLED background image. The defect result can be obtained by the subtracting operation between the reconstructed background and the source images. The detection system is implemented in the Labview and the testing results show that the ICA based OLED defect detection method is feasible and effective.

scholarly journals Fractals and Independent Component Analysis for Defect Detection in Bridge Decks

2011 ◽  
Vol 2011 ◽  
pp. 1-14 ◽  
Author(s):  
Ikhlas Abdel-Qader ◽  
Fadi Abu-Amara ◽  
Osama Abudayyeh
Keyword(s):  
Feature Extraction ◽  
Independent Component Analysis ◽  
Defect Detection ◽  
Ground Penetrating Radar ◽  
Bridge Decks ◽  
Component Analysis ◽  
Independent Component ◽  
Extraction Algorithm ◽  
Ground Penetrating ◽  
Detection And Localization

We present in this paper a framework for the automatic detection and localization of defects inside bridge decks. Using Ground-Penetrating Radar (GPR) raw scans, this framework is composed of a feature extraction algorithm using fractals to detect defective regions and a deconvolution algorithm using banded-independent component analysis (ICA) to reduce overlapping between reflections and to estimate the radar waves travel time and depth of defects. Results indicate that the defects' estimated horizontal location and depth falling within 2 cm (76.92% accuracy) and 1 cm (84.62% accuracy) from their actual values.

Gray Matter Segmentation of Brain MRI Using Hybrid Enhanced Independent Component Analysis in Noisy and Noise Free Environment

2020 ◽  
Vol 47 ◽  
pp. 75-103
Author(s):  
Shaik Basheera ◽  
M. Satya Sai Ram
Keyword(s):  
Independent Component Analysis ◽  
Gray Matter ◽  
Brain Mri ◽  
Component Analysis ◽  
Independent Component ◽  
Noisy Environments ◽  
Low Contrast ◽  
Free Environment ◽  
Medical Segmentation ◽  
The Brain

Medical segmentation is the primary task performed to diagnosis the abnormalities in the human body. The brain is the complex organ and anatomical segmentation of brain tissues is a challenging task. In this paper, we used Enhanced Independent component analysis to perform the segmentation of gray matter. We used modified K means, Expected Maximization and Hidden Markov random field to provide better spatial correlation that overcomes in-homogeneity, noise and low contrast. Our objective is achieved in two steps (i) initially unwanted tissues are clipped from the MRI image using skull stripped Algorithm (ii) Enhanced Independent Component analysis is used to perform the segmentation of gray matter. We apply the proposed method on both T1w and T2w MRI to perform segmentation of gray matter at different noisy environments. We evaluate the the performance of our proposed system with Jaccard Index, Dice Coefficient and Accuracy. We further compared the proposed system performance with the existing frameworks. Our proposed method gives better segmentation of gray matter useful for diagnosis neurodegenerative disorders.

Gray Matter Segmentation of Brain MRI Using Hybrid Enhanced Independent Component Analysis

2021 ◽  
pp. 2150029
Author(s):  
Shaik Basheera ◽  
M. Satya Sai Ram
Keyword(s):  
Independent Component Analysis ◽  
Gray Matter ◽  
Medical Image Analysis ◽  
Brain Mri ◽  
Spatial Relation ◽  
Component Analysis ◽  
Independent Component ◽  
Morphological Operations ◽  
Low Contrast ◽  
Skull Stripping

One of the primary pre-processing tasks of medical image analysis is segmentation; it is used to diagnose the abnormalities in the tissues. As the brain is a complex organ, anatomical segmentation of brain tissues is a challenging task. Segmented gray matter is analyzed for early diagnosis of neurodegenerative disorders. In this endeavor, we used enhanced independent component analysis to perform segmentation of gray matter in noise-free and noisy environments. We used modified [Formula: see text]-means, expectation–maximization and hidden Markov random field to provide better spatial relation to overcome inhomogeneity, noise and low contrast. Our objective is achieved using the following two steps: (i) Irrelevant tissues are stripped from the MRI using skull stripping algorithm. In this algorithm, sequence of threshold, morphological operations and active contour are applied to strip the unwanted tissues. (ii) Enhanced independent component analysis is used to perform segmentation of gray matter. The proposed approach is applied on both T1w MRI and T2w MRI images at different noise environments such as salt and pepper noise, speckle noise and Rician noise. We evaluated the performance of the approach using Jaccard index, Dice coefficient and accuracy. The parameters are further compared with existing frameworks. This approach gives better segmentation of gray matter for the diagnosis of atrophy changes in brain MRI.

scholarly journals A Novel Method for Surface Defect Detection of Photovoltaic Module Based on Independent Component Analysis

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Xuewu Zhang ◽  
Hao Sun ◽  
Yun Zhou ◽  
Ji Xi ◽  
Min Li
Keyword(s):  
Independent Component Analysis ◽  
Defect Detection ◽  
Surface Defect ◽  
Reconstruction Algorithm ◽  
Component Analysis ◽  
Training Image ◽  
Independent Component ◽  
Photovoltaic Module ◽  
Surface Defect Detection ◽  
Mixing Matrix

This paper proposed a new method for surface defect detection of photovoltaic module based on independent component analysis (ICA) reconstruction algorithm. Firstly, a faultless image is used as the training image. The demixing matrix and corresponding ICs are obtained by applying the ICA in the training image. Then we reorder the ICs according to the range values and reform the de-mixing matrix. Then the reformed de-mixing matrix is used to reconstruct the defect image. The resulting image can remove the background structures and enhance the local anomalies. Experimental results have shown that the proposed method can effectively detect the presence of defects in periodically patterned surfaces.

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