Fault detection and feature analysis in interferometric fringe patterns by the application of wavelet filters in convolution processors

2000 ◽  
Author(s):  
Sven Krueger ◽  
Guenther K. Wernicke ◽  
Wolfgang Osten ◽  
Daniel Kayser ◽  
Nazif Demoli ◽  
...  
Keyword(s):  
Fault Detection ◽  
Feature Analysis ◽  
Wavelet Filters ◽  
Fringe Patterns

Application of wavelet filters for feature extraction in interferometric fringe patterns

1999 ◽  
Author(s):  
Daniel Kayser ◽  
Wolfgang Osten ◽  
Sven Krueger ◽  
Guenther K. Wernicke
Keyword(s):  
Feature Extraction ◽  
Wavelet Filters ◽  
Fringe Patterns

scholarly journals Machine Learning for Diabetic Retinopathy Detection using Image Processing

2021 ◽  
Vol 9 (5) ◽  
pp. 209-215
Author(s):  
Ujwala W. Wasekar ◽  
R. K. Bathla
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Early Stage ◽  
Haar Wavelet ◽  
Feature Analysis ◽  
Median Filtering ◽  
Wavelet Filters ◽  
Knn Classifier ◽  
Form Features ◽  
Processing Techniques

he disorder of Diabetic Retinopathy (DR), a complication of Diabetes that may lead to blindness if not treated at an early stage, is diagnosed by evaluating the retina images of eye. However, the manual grading of images for identifying the seriousness of DR disease requires many resources and it also takes a lot of time. Automated systems give accurate results along with saving time. Ophthalmologists may find it useful in reducing their workload. Proposed work presents the method to correctly identify the lesions and classify DR images efficiently. Blood leaking out of veins form features such as exudates, microaneurysms and haemorrhages, on retina. Image processing techniques assist in DR detection. Median filtering is used on gray scale converted image to reduce noise. The features of the pre-processed images are extracted by textural feature analysis. Optic disc (OD) segmentation methodology is implemented for the removal of OD. Blood vessels are extracted using haar wavelet filters. KNN classifier is applied for classifying retinal image into diseased or healthy .The proposed algorithm is executed in MATLAB software and analyze results with regard to certain parameters such as accuracy, sensitivity, and specificity. The outcomes prove the superiority of the new method with sensitivity of 92.6%, specificity of 87.56% and accuracy of 95% on Diaretdb1 database.

scholarly journals Fault Detection in Managed Pressure Drilling Using Slow Feature Analysis

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 34262-34271 ◽  
Author(s):  
Xiaoyong Gao ◽  
Haishou Li ◽  
Yuhong Wang ◽  
Tao Chen ◽  
Xin Zuo ◽  
...  
Keyword(s):  
Fault Detection ◽  
Feature Analysis ◽  
Slow Feature Analysis ◽  
Managed Pressure Drilling

Process fault detection based on dynamic kernel slow feature analysis

2015 ◽  
Vol 41 ◽  
pp. 9-17 ◽  
Author(s):  
Ni Zhang ◽  
Xuemin Tian ◽  
Lianfang Cai ◽  
Xiaogang Deng
Keyword(s):  
Fault Detection ◽  
Feature Analysis ◽  
Slow Feature Analysis
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