The fractional differential enhancement of image texture features and its parallel processing optimization

2012 ◽  
Author(s):  
Jin Che ◽  
Yi-Shuai Shi ◽  
Yang Xiang ◽  
Yu-Tao Ma
Keyword(s):  
Parallel Processing ◽  
Texture Features ◽  
Image Texture ◽  
Fractional Differential ◽  
Processing Optimization

scholarly journals Research on college gymnastics teaching model based on multimedia image and image texture feature analysis

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Ying Wu ◽  
Jikun Liu
Keyword(s):  
Human Body ◽  
Rapid Development ◽  
Texture Feature ◽  
Texture Features ◽  
Dynamics Simulation ◽  
Image Texture ◽  
Whole Body ◽  
Database Modeling ◽  
Rigid Model ◽  
The Subject

AbstractWith the rapid development of gymnastics technology, novel movements are also emerging. Due to the emergence of various complicated new movements, higher requirements are put forward for college gymnastics teaching. Therefore, it is necessary to combine the multimedia simulation technology to construct the human body rigid model and combine the image texture features to display the simulation image in texture form. In the study, GeBOD morphological database modeling was used to provide the data needed for the modeling of the whole-body human body of the joint and used for dynamics simulation. Simultaneously, in order to analyze and summarize the technical essentials of the innovative action, this experiment compared and analyzed the hem stage of the cross-headstand movement of the subject and the hem stage of the 180° movement. Research shows that the method proposed in this paper has certain practical effects.

An Evaluation of the Effectiveness of Image-based Texture Features Extracted from Static B-mode Ultrasound Images in Distinguishing between Benign and Malignant Ovarian Masses

2021 ◽  
pp. 016173462199809
Author(s):  
Dhurgham Al-karawi ◽  
Hisham Al-Assam ◽  
Hongbo Du ◽  
Ahmad Sayasneh ◽  
Chiara Landolfo ◽  
...  
Keyword(s):  
Gabor Filter ◽  
Empirical Evaluation ◽  
Texture Features ◽  
Image Texture ◽  
Support Vector ◽  
Simple Majority ◽  
Ultrasound Scan ◽  
Histograms Of Oriented Gradients ◽  
The Individual ◽  
Ovarian Masses

Significant successes in machine learning approaches to image analysis for various applications have energized strong interest in automated diagnostic support systems for medical images. The evolving in-depth understanding of the way carcinogenesis changes the texture of cellular networks of a mass/tumor has been informing such diagnostics systems with use of more suitable image texture features and their extraction methods. Several texture features have been recently applied in discriminating malignant and benign ovarian masses by analysing B-mode images from ultrasound scan of the ovary with different levels of performance. However, comparative performance evaluation of these reported features using common sets of clinically approved images is lacking. This paper presents an empirical evaluation of seven commonly used texture features (histograms, moments of histogram, local binary patterns [256-bin and 59-bin], histograms of oriented gradients, fractal dimensions, and Gabor filter), using a collection of 242 ultrasound scan images of ovarian masses of various pathological characteristics. The evaluation examines not only the effectiveness of classification schemes based on the individual texture features but also the effectiveness of various combinations of these schemes using the simple majority-rule decision level fusion. Trained support vector machine classifiers on the individual texture features without any specific pre-processing, achieve levels of accuracy between 75% and 85% where the seven moments and the 256-bin LBP are at the lower end while the Gabor filter is at the upper end. Combining the classification results of the top k ( k = 3, 5, 7) best performing features further improve the overall accuracy to a level between 86% and 90%. These evaluation results demonstrate that each of the investigated image-based texture features provides informative support in distinguishing benign or malignant ovarian masses.

Fast Intra-Frame Encoding Algorithm Based on Image Texture Features for HEVC

2017 ◽  
Vol 54 (7) ◽  
pp. 071003
Author(s):  
孙学斌 Sun Xuebin ◽  
陈晓冬 Chen Xiaodong ◽  
肖禹泽 Xiao Yuze ◽  
汪 毅 Wang Yi ◽  
郁道银 Yu Daoyin
Keyword(s):  
Texture Features ◽  
Image Texture

A fabric defect detection algorithm via context-based local texture saliency analysis

2015 ◽  
Vol 27 (5) ◽  
pp. 738-750 ◽  
Author(s):  
Zhoufeng Liu ◽  
Chunlei Li ◽  
Quanjun Zhao ◽  
Liang Liao ◽  
Yan Dong
Keyword(s):  
Defect Detection ◽  
Texture Features ◽  
Detection Algorithm ◽  
Saliency Map ◽  
Image Texture ◽  
Content Type ◽  
Local Texture ◽  
Fabric Defect Detection ◽  
Fabric Defect ◽  
Saliency Analysis

Purpose – Fabric defect detection plays an important role in textile quality control. The purpose of this paper is to propose a fabric defect detection algorithm via context-based local texture saliency analysis. Design/methodology/approach – In the proposed algorithm, a target image is first divided into blocks, then the Local Binary Pattern (LBP) technique is used to extract the texture features of blocks. Second, for a given image block, several other blocks are randomly chosen for calculating the LBP contrast between a given block and the randomly chosen blocks. Based on the obtained contrast information, a saliency map is produced. Finally, saliency map is segmented by using an optimal threshold, which is obtained by an iterative approach. Findings – The experimental results show that the proposed algorithm, integrating local texture features and global image texture information, can detect texture defects effectively. Originality/value – In this paper, a novel fabric defect detection algorithm via context-based local texture saliency analysis is proposed.

A Secure One-Time Password Authentication Scheme Using Image Texture Features

2016 ◽  
pp. 283-294
Author(s):  
Maitreya Maity ◽  
Dhiraj Manohar Dhane ◽  
Tushar Mungle ◽  
Rupak Chakraborty ◽  
Vasant Deokamble ◽  
...  
Keyword(s):  
Texture Features ◽  
Authentication Scheme ◽  
Image Texture ◽  
Password Authentication ◽  
One Time Password
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