Statistical feature bag based background subtraction for local change detection

2016 ◽  
Vol 366 ◽  
pp. 31-47 ◽  
Author(s):  
Badri Narayan Subudhi ◽  
Susmita Ghosh ◽  
Simon C.K. Shiu ◽  
Ashish Ghosh
Keyword(s):  
Change Detection ◽  
Background Subtraction ◽  
Local Change ◽  
Statistical Feature

scholarly journals Which way is up? Global and local change detection in the hemispheres.

2016 ◽  
Vol 16 (12) ◽  
pp. 47
Author(s):  
Bonnie Angelone ◽  
Jessica Marcoux
Keyword(s):  
Change Detection ◽  
Local Change ◽  
Global And Local

Spatio-contextual Gaussian mixture model for local change detection in underwater video

2018 ◽  
Vol 97 ◽  
pp. 117-136 ◽  
Author(s):  
Deepak Kumar Rout ◽  
Badri Narayan Subudhi ◽  
T. Veerakumar ◽  
Santanu Chaudhury
Keyword(s):  
Change Detection ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Local Change ◽  
Underwater Video

Improved Algorithms for Motion Detection of Intelligent Video Surveillance System

2012 ◽  
Vol 505 ◽  
pp. 367-372
Author(s):  
Yan Ling Wang ◽  
Xiao Li Wang ◽  
Guang Lun Li
Keyword(s):  
Change Detection ◽  
Motion Detection ◽  
Background Subtraction ◽  
Moving Objects ◽  
Detection Algorithm ◽  
Reference Image ◽  
Lighting Conditions ◽  
Detection Approach ◽  
Time Segmentation ◽  
Temporal Differencing

Real-time segmentation of moving regions in image sequences is a fundamental step in video monitoring systems. This paper presents an improved motion detection algorithm in a dynamic scene based on change detection. The algorithm integrates the temporal differencing method and background subtraction method to achieve better performance. Background subtraction is a typical change detection approach to segment foreground, but the continuous or abrupt variations of lighting conditions that cause unexpected changes in intensities on the background reference image. So we combine the background subtraction’s result with temporal difference’s result. The foreground mask is segmented by both the methods of background subtraction and temporal differencing. Finally, a post-processing is applied on the obtained object mask to reduce regions and smooth the moving region boundary. Experimental results demonstrate that the proposed method can update the background exactly and quickly along with the variation of illumination, and the moving objects can be extracted effectively.

Kernelized Fuzzy Modal Variation for Local Change Detection From Video Scenes

2020 ◽  
Vol 22 (4) ◽  
pp. 912-920
Author(s):  
Badri Narayan Subudhi ◽  
Thangaraj Veerakumar ◽  
S. Esakkirajan ◽  
Ashish Ghosh
Keyword(s):  
Change Detection ◽  
Local Change ◽  
Modal Variation

scholarly journals Detection of Moving Objects with Fuzzy Color Coherence Vector

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Yu-Long Qiao ◽  
Kai-Long Yuan ◽  
Chun-Yan Song ◽  
Xue-Zhi Xiang
Keyword(s):  
Background Subtraction ◽  
Moving Objects ◽  
Spatial Relationship ◽  
Experimental Results ◽  
Fuzzy Membership ◽  
Subtraction Method ◽  
Color Distribution ◽  
Popular Method ◽  
Statistical Feature ◽  
Advanced Applications

Background subtraction is a popular method for detecting foreground that is widely adopted as the fundamental processing for advanced applications such as tracking and surveillance. Color coherence vector (CCV) includes both the color distribution information (histogram) and the local spatial relationship information of colors. So it overcomes the weakness of the conventional color histogram for the representation of an object. In this paper, we introduce a fuzzy color coherence vector (FCCV) based background subtraction method. After applying the fuzzyc-means clustering to color coherence subvectors and color incoherence subvectors, we develop a region-based fuzzy statistical feature for each pixel based on the fuzzy membership matrices. The features are extracted from consecutive frames to build the background model and detect the moving objects. The experimental results demonstrate the effectiveness of the proposed approach.

Quantitative EPMA of nitrogen : A tricky element in the electron-probe microanalyzer

1992 ◽  
Vol 50 (2) ◽  
pp. 1622-1623
Author(s):  
G.F. Bastin ◽  
H.J.M. Heijligers
Keyword(s):  
Background Subtraction ◽  
Electron Probe ◽  
Detection System ◽  
Higher Order ◽  
Light Elements ◽  
Strong Suppression ◽  
Electron Probe Microanalyzer ◽  
Quantitative Epma ◽  
Peak Count ◽  
Lead Stearate

Among the ultra-light elements B, C, N, and O nitrogen is the most difficult element to deal with in the electron probe microanalyzer. This is mainly caused by the severe absorption that N-Kα radiation suffers in carbon which is abundantly present in the detection system (lead-stearate crystal, carbonaceous counter window). As a result the peak-to-background ratios for N-Kα measured with a conventional lead-stearate crystal can attain values well below unity in many binary nitrides . An additional complication can be caused by the presence of interfering higher-order reflections from the metal partner in the nitride specimen; notorious examples are elements such as Zr and Nb. In nitrides containing these elements is is virtually impossible to carry out an accurate background subtraction which becomes increasingly important with lower and lower peak-to-background ratios. The use of a synthetic multilayer crystal such as W/Si (2d-spacing 59.8 Å) can bring significant improvements in terms of both higher peak count rates as well as a strong suppression of higher-order reflections.

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