Edge Detection and Grey Level Co-Occurrence Matrix (GLCM) Algorithms for Fingerprint Identification

2021 ◽  
Author(s):  
Edy Winarno ◽  
Wiwien Hadikurniawati ◽  
Setyawan Wibisono ◽  
Anindita Septiarini
Keyword(s):  
Edge Detection ◽  
Fingerprint Identification ◽  
Grey Level ◽  
Occurrence Matrix

scholarly journals Skin Disease Recognition Method Based on Image Color and Texture Features

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Li-sheng Wei ◽  
Quan Gan ◽  
Tao Ji
Keyword(s):  
Skin Disease ◽  
Skin Diseases ◽  
Texture Features ◽  
Support Vector ◽  
Grey Level ◽  
Recognition Method ◽  
Svm Classification ◽  
Color Features ◽  
Automatic Methods ◽  
Occurrence Matrix

Skin diseases have a serious impact on people’s life and health. Current research proposes an efficient approach to identify singular type of skin diseases. It is necessary to develop automatic methods in order to increase the accuracy of diagnosis for multitype skin diseases. In this paper, three type skin diseases such as herpes, dermatitis, and psoriasis skin disease could be identified by a new recognition method. Initially, skin images were preprocessed to remove noise and irrelevant background by filtering and transformation. Then the method of grey-level co-occurrence matrix (GLCM) was introduced to segment images of skin disease. The texture and color features of different skin disease images could be obtained accurately. Finally, by using the support vector machine (SVM) classification method, three types of skin diseases were identified. The experimental results demonstrate the effectiveness and feasibility of the proposed method.

Edge detection in noisy images based on the co-occurrence matrix

1994 ◽  
Vol 27 (6) ◽  
pp. 765-775 ◽  
Author(s):  
Deok J. Park ◽  
Kwon M. Nam ◽  
Rae-Hong Park
Keyword(s):  
Edge Detection ◽  
Noisy Images ◽  
Occurrence Matrix

Machine Vision for Surface Roughness Assessment of Inclined Components

2010 ◽  
Vol 437 ◽  
pp. 141-144 ◽  
Author(s):  
P. Priya ◽  
B. Ramamoorthy
Keyword(s):  
Image Processing ◽  
Surface Roughness ◽  
Machine Vision ◽  
Roughness Parameters ◽  
Grey Level ◽  
Ideal Orientation ◽  
Roughness Assessment ◽  
Occurrence Matrix ◽  
Made In ◽  
The Ideal

Many researchers have so far used machine vision and digital image processing for grabbing images of machined surfaces, improving their quality by pre-processing and then analysed them for evaluation of surface finish with a reasonable success. An attempt has been made in this work to capture the images of the surfaces with varying inclinations covering both the sides. The ideal orientation of the surface (flat and horizontal) is found by observing the variation in optical roughness parameters estimated from the grey level co-occurrence matrix as the angle of inclination changes. It is observed that the variation of roughness parameters with respect to angle of inclination also depends on the surface roughness of the component. The optical roughness values obtained by machine vision approach are then subsequently compared with the conventional Ra as obtained by stylus method and the analysis is presented.

scholarly journals A Novel Technique for Copy Move Forgery Detection using Grey Level Co-occurrence Matrix

2019 ◽  
Vol 8 (9S) ◽  
pp. 581-586
Keyword(s):  
Feature Extraction ◽  
Grey Level ◽  
Forgery Detection ◽  
Novel Technique ◽  
Copy Move Forgery Detection ◽  
Occurrence Matrix

In this paper we have studied the GLCM approach as an improvement over SWT-DCT method for feature extraction for CMFD. We have carefully studied the previously used methods and also studied the SWT-DCT method for improvement in features. We have proposed a method for the use of GLCM instead of SWT-DCT method for feature extraction which will improve the results of CMFD method used in the base work framework.

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