scholarly journals A Study for Texture Feature Extraction of High-Resolution Satellite Images Based on a Direction Measure and Gray Level Co-Occurrence Matrix Fusion Algorithm

Sensors ◽  
2017 ◽  
Vol 17 (7) ◽  
pp. 1474 ◽  
Author(s):  
Xin Zhang ◽  
Jintian Cui ◽  
Weisheng Wang ◽  
Chao Lin
Keyword(s):  
Feature Extraction ◽  
High Resolution ◽  
Satellite Images ◽  
Texture Feature ◽  
Gray Level ◽  
Texture Feature Extraction ◽  
Fusion Algorithm ◽  
High Resolution Satellite Images ◽  
Matrix Fusion ◽  
Occurrence Matrix

scholarly journals Using Color and Texture Feature Extraction Technique to Retrieve Image

2019 ◽  
Vol 8 (11S) ◽  
pp. 915-917
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Texture Feature ◽  
Color Images ◽  
Color Histogram ◽  
Gray Level ◽  
Texture Feature Extraction ◽  
Color Feature ◽  
Occurrence Matrix ◽  
Feature Extraction Technique

This paper proposes a content image retrieval using the texture and the color feature of the images. Although for extraction of texture feature, the “gray level co-occurrence matrix (GLCM) algorithm” is used and for extracting color feature the color histogram is used. The presented system is tested on the WANG database that contains a thousand color images with ten different classes by the help of three various type of distances

scholarly journals Image Texture Feature Extraction Method Based on Regional Average Binary Gray Level Difference Co-occurrence Matrix

2011 ◽  
Vol 10 (3) ◽  
pp. 73-79 ◽  
Author(s):  
Jian Yang ◽  
Jingfeng Guo
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Texture Feature ◽  
Local Area ◽  
Image Texture ◽  
Gray Level ◽  
Texture Feature Extraction ◽  
Feature Extraction Method ◽  
Level Difference ◽  
Occurrence Matrix

Texture feature is a measure method about relationship among the pixels in local area, reflecting the changes of image space gray levels. This paper presents a texture feature extraction method based on regional average binary gray level difference co-occurrence matrix, which combined the texture structural analysis method with statistical method. Firstly, we calculate the average binary gray level difference of eight-neighbors of a pixel to get the average binary gray level difference image which expresses the variation pattern of the regional gray levels. Secondly, the regional co-occurrence matrix is constructed by using these average binary gray level differences. Finally, we extract the second-order statistic parameters reflecting the image texture feature from the regional co-occurrence matrix. Theoretical analysis and experimental results show that the image texture feature extraction method has certain accuracy and validity

scholarly journals Identification of Human Ovarian Adenocarcinoma Cells with Cisplatin-Resistance by Feature Extraction of Gray Level Co-Occurrence Matrix Using Optical Images

Diagnostics ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 389
Author(s):  
Chih-Ling Huang ◽  
Meng-Jia Lian ◽  
Yi-Hsuan Wu ◽  
Wei-Ming Chen ◽  
Wen-Tai Chiu
Keyword(s):  
Feature Extraction ◽  
Texture Feature ◽  
Neoplastic Disease ◽  
Gray Level ◽  
Texture Feature Extraction ◽  
Ovarian Adenocarcinoma ◽  
Optical Images ◽  
Adenocarcinoma Cells ◽  
Occurrence Matrix ◽  
Resistant Cells

Ovarian cancer is the most malignant of all gynecological cancers. A challenge that deteriorates with ovarian adenocarcinoma in neoplastic disease patients has been associated with the chemoresistance of cancer cells. Cisplatin (CP) belongs to the first-line chemotherapeutic agents and it would be beneficial to identify chemoresistance for ovarian adenocarcinoma cells, especially CP-resistance. Gray level co-occurrence matrix (GLCM) was characterized imaging from a numeric matrix and find its texture features. Serous type (OVCAR-4 and A2780), and clear cell type (IGROV1) ovarian carcinoma cell lines with CP-resistance were used to demonstrate GLCM texture feature extraction of images. Cells were cultured with cell density of 6 × 105 in a glass-bottom dish to form a uniform coverage of the glass slide to get the optical images by microscope and DVC camera. CP-resistant cells included OVCAR-4, A2780 and IGROV and had the higher contrast and entropy, lower energy, and homogeneity. Signal to noise ratio was used to evaluate the degree for chemoresistance of cell images based on GLCM texture feature extraction. The difference between wile type and CP-resistant cells was statistically significant in every case (p < 0.001). It is a promising model to achieve a rapid method with a more reliable diagnostic performance for identification of ovarian adenocarcinoma cells with CP-resistance by feature extraction of GLCM in vitro or ex vivo.

scholarly journals PENGENALAN TAS RANSEL PADA CITRA DIGITAL DENGAN EKSTRAKSI FITUR TEKSTUR MENGGUNAKAN METODE GRAY LEVEL CO-OCCURRENCE MATRIX

2018 ◽  
Vol 4 (4) ◽  
pp. 258
Author(s):  
Cahya Rahmad ◽  
Mungki Astiningrum ◽  
Ade Putra Lesmana
Keyword(s):  
Feature Extraction ◽  
Texture Feature ◽  
Gray Level ◽  
Significant Development ◽  
Average Percentage ◽  
The Road ◽  
The Third ◽  
Last Stage ◽  
On The Road ◽  
Occurrence Matrix

The Backpack is one type of bag that experienced significant development. Many people buy it for their needs. However, when assessing a backpack directly or on the road, he could not recognize the backpack. The generally people want to buy backpacks must look at the price, color, shape, features, and the main ingredients of manufacture. Therefore, in image processing, there is a feature extraction theory for the process of recognizing an object. The backpack itself has a different texture. So that the introduction of the object is better done texture feature extraction with the gray level Co-occurrence matrix method. After that, then get the uniqueness of the backpack image to the classification with the image of the backpack in the database. The last stage in this study the authors conducted trials in 3 conditions. The first condition is based on a backpack photo-taking background. The second condition is based on the pixel capacity of the camera to retrieve the backpack image. And the third condition is based on the brightness of the backpack image. Of these three conditions, a percentage of matching values was obtained in the first condition with an average percentage of 90%, the second condition with an average percentage of 80% and last on the third condition with an average percentage of 70%.

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