The Research of Leather Image Segmentation Using Texture Analysis Techniques

2014 ◽  
Vol 1030-1032 ◽  
pp. 1846-1850
Author(s):  
Hong Chen
Keyword(s):  
Image Segmentation ◽  
Surface Texture ◽  
Surface Defects ◽  
Spatial Information ◽  
Texture Feature ◽  
Gray Level ◽  
Analysis Techniques ◽  
Fuzzy C Means ◽  
Morphological Approach ◽  
Occurrence Matrix

The leather productions are produced rapidly in people’s living, the productions’ quality is required stricter. Leather must be detected include leather plainness; leather surface defects and the density of leather before they are produced to be productions.. The most important aspect is the surface defects; the defects’ location, size and quantity should be confirmed. One of the most important steps of leather defects detection is leather image segmentation so as to extract leather defects. Gray level co-occurrence matrix is used to extract a lot of leather surface texture feature, the method of optimized Fuzzy C-means is used to segment leather image in the article. The optimized Fuzzy C-means add the spatial information; the precision of segmentation is improved. The image needs to be treated use morphological approach after it is segmented. As a result, the defective areas are separated from non-defective areas successfully.

NIMG-56. USING RADIOMIC FEATURES FROM DAILY MAGNETIC RESONANCE IMAGING TO PREDICT RESPONSE TO RADIATION THERAPY IN GLIOBLASTOMA PATIENTS: A PILOT STUDY

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi142-vi142
Author(s):  
Kaylie Cullison ◽  
Garrett Simpson ◽  
Danilo Maziero ◽  
Kolton Jones ◽  
Radka Stoyanova ◽  
...  
Keyword(s):  
Radiation Therapy ◽  
Tumor Growth ◽  
Texture Feature ◽  
Texture Features ◽  
Gray Level ◽  
Difference Matrix ◽  
Patient Response ◽  
Significant Difference ◽  
Occurrence Matrix ◽  
Surrounding Areas

Abstract A dilemma in treating glioblastoma is that MRI after chemotherapy and radiation therapy (chemoRT) shows areas of presumed tumor growth in up to 50% of patients. These areas can represent true progression (TP), tumor growth with tumors non-responsive to treatment, or pseudoprogression (PP), edema and tumor necrosis with favorable treatment response. On imaging, TP and PP are usually not discernable. Patients in this study undergo six weeks of chemoRT on a combination MRI/RT device, receiving daily MRIs. The goal of this study is to explore the correlation of radiomics features with progression. The tumor lesion and surrounding areas of growth/edema were manually outlined as regions of interest (ROIs) for each daily T2-weighted MRI scan. The ROIs were used to calculate texture features: statistical features based on the gray-level co-occurrence matrix (GLCM), the gray-level zone size matrix (GLZSM), the gray-level run length matrix (GLRLM), and the neighborhood gray-tone difference matrix (NGTDM). Each of these matrix classes describe the probability of spatial relationships of gray levels occurring within the ROI. Daily texture features were averaged per week of treatment for each patient. Patient response was retrospectively defined as no progression (NP), TP, or PP. A Kruskal-Wallis test was performed to identify texture features that correlated most strongly with patient response. Forty texture features were calculated for 12 patients (19 treated, 7 excluded due to no T2 lesion or progression status unknown, 6 NP, 3 TP, 3 PP). There was a trend of more texture features correlating significantly with response in weeks 4-6 of treatment, compared to weeks 1-3. A particular texture feature, GLSZM Small Zone Low Gray-Level Emphasis, showed increasing difference between PP and TP over time, with significant difference during week 6 of treatment (p=0.0495). Future directions include correlating early outcomes with greater numbers of patients and daily multiparametric MRI.

Improved fuzzy C-means algorithm based on gray-level for image segmentation

2020 ◽  
Vol 35 (5) ◽  
pp. 499-507
Author(s):  
赵战民 ZHAO Zhan-min ◽  
朱占龙 ZHU Zhan-long ◽  
王军芬 WANG Jun-fen
Keyword(s):  
Image Segmentation ◽  
Gray Level ◽  
Fuzzy C Means ◽  
Fuzzy C Means Algorithm

Method of Detecting Sea State from Image Taken by Autonomous Surface Vehicle

2013 ◽  
Vol 316-317 ◽  
pp. 475-478
Author(s):  
Jian Hua Wang ◽  
Gang Li ◽  
Ya Zhou Xiong ◽  
Kang Ke Liu
Keyword(s):  
Water Surface ◽  
Texture Feature ◽  
Gray Level ◽  
Sea State ◽  
Autonomous Surface Vehicle ◽  
State Detection ◽  
Occurrence Matrix ◽  
Safe Approach ◽  
Dangerous Condition ◽  
Better Than

Autonomous surface vehicle provides a safe approach to monitor environment on water surface in dangerous condition. This paper presents a method of sea state detection from images taken by a camera fixed on an autonomous surface vehicle. Based on texture feature of images from water surface scene, gray level co-occurrence matrix is computed, and its features including energy, contrast, correlation and entropy are extracted. Experiments show that the contrast can differentiate the sea state levels better than the others. To improve discrimination at low sea state levels, a transform is proposed. Performance of the method at different light shining conditions is discussed, and the results validate the method.

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%.

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

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