Adaptive window-size selection approach for feature extraction in texture analysis

2001 ◽  
Author(s):  
Wen Sheng ◽  
Chenxi Xu ◽  
Jianguo Liu
Keyword(s):  
Feature Extraction ◽  
Texture Analysis ◽  
Window Size ◽  
Size Selection ◽  
Adaptive Window ◽  
Selection Approach

Adaptive window based fall detection using anomaly identification in fog computing scenario

2021 ◽  
Vol 17 (1) ◽  
pp. 15-37
Author(s):  
Rashmi Shrivastava ◽  
Manju Pandey
Keyword(s):  
Feature Extraction ◽  
Assisted Living ◽  
Detection System ◽  
Fog Computing ◽  
Ambient Assisted Living ◽  
Window Size ◽  
Fall Detection ◽  
Detection Methods ◽  
Support Vector ◽  
Adaptive Window

Human fall detection is a subcategory of ambient assisted living. Falls are dangerous for old aged people especially those who are unaccompanied. Detection of falls as early as possible along with high accuracy is indispensable to save the person otherwise it may lead to physical disability even death also. The proposed fall detection system is implemented in the edge computing scenario. An adaptive window-based approach is proposed here for feature extraction because window size affects the performance of the classifier. For training and testing purposes two public datasets and our collected dataset have been used. Anomaly identification based on a support vector machine with an enhanced chi-square kernel is used here for the classification of Activities of Daily Living (ADL) and fall activities. Using the proposed approach 100% sensitivity and 98.08% specificity have been achieved which are better when compared with three recent research based on unsupervised learning. One of the important aspects of this study is that it is also validated on actual real fall data and got 100% accuracy. This complete fall detection model is implemented in the fog computing scenario. The proposed approach of adaptive window based feature extraction is better than static window based approaches and three recent fall detection methods.

Research on recognition of coal and gangue based on image processing

2015 ◽  
Vol 12 (3) ◽  
pp. 247-254 ◽  
Author(s):  
Lihong Li ◽  
Haijiang Wang ◽  
Lei An
Keyword(s):  
Image Processing ◽  
Texture Analysis ◽  
Spatial Information ◽  
Texture Feature ◽  
Window Size ◽  
Mean Value ◽  
Adaptive Window ◽  
Gray Histogram ◽  
New Ideas ◽  
Novel Method

In order to avoid the waste of water resources and environmental pollution caused by separating coal and gangue in the traditional methods, a novel method based on image processing is proposed in this paper. Firstly the image of coal or gangue is preprocessed. Then the mean value of gray histogram is extracted which serves as the statistical feature value to initially recognize coal and gangue. Then the textural feature is extracted from the image which is based on an adaptive window of texture analysis. The adaptive window size is determined by the contrast texture feature parameter. The adaptive window of texture analysis can improve the discriminability of coal and gangue. This method not only considers the image’s gray feature but also utilizes the image’s spatial information, so the recognition precision is improved. This method provides new ideas for dry separation technology.

Local transform-based image denoising with adaptive window-size selection

2001 ◽  
Author(s):  
Karen O. Egiazarian ◽  
Vladimir Katkovnik ◽  
Jaakko T. Astola
Keyword(s):  
Image Denoising ◽  
Window Size ◽  
Size Selection ◽  
Adaptive Window

scholarly journals An Adaptive Window Size Selection Method for Differentially Private Data Publishing over Infinite Trajectory Stream

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Geonhyoung Jo ◽  
Kangsoo Jung ◽  
Seog Park
Keyword(s):  
Differential Privacy ◽  
Window Size ◽  
Selection Method ◽  
Traffic Information ◽  
Sensor Technology ◽  
Traffic Data ◽  
Wireless Internet ◽  
Data Utility ◽  
Size Selection ◽  
Adaptive Window

Recently, various services based on user's location are emerging since the development of wireless Internet and sensor technology. VANET (vehicular ad hoc network), in which a large number of vehicles communicate using wireless communication, is also being highlighted as one of the services. VANET collects and analyzes the traffic data periodically to provide the traffic information service. The problem is that traffic data contains user’s sensitive location information that can lead to privacy violations. Differential privacy techniques are being used as a de facto standard to prevent such privacy violation caused by data analysis. However, applying differential privacy to traffic data stream which has infinite size over time makes data useless because too much noise is inserted to protect privacy. In order to overcome this limitation, existing researches set a certain range of windows and apply differential privacy to windowed data. However, previous researches have set a fixed window size do not consider a traffic data’s property such as road structure and time-based traffic variation. It may lead to insufficient privacy protection and unnecessary data utility degradation. In this paper, we propose an adaptive window size selection method that consider the correlation between road networks and time-based traffic variation to solve a fixed window size problem. And we suggest an adjustable privacy budget allocation technique for corresponding to the adaptive window size selection. We show that the proposed method improves the data utility, while satisfying the equal level of differential privacy as compared with the existing method through experiments that is designed based on real-world road network.

Sign in / Sign up

Export Citation Format

Share Document