Global, local, and stochastic background modeling for target detection in mixed pixels

2010 ◽  
Author(s):  
Marin S. Halper
Keyword(s):  
Target Detection ◽  
Background Modeling ◽  
Stochastic Background ◽  
Mixed Pixels

scholarly journals Dim and Small Target Detection Based on Local Energy Aggregation Degree of Sequence Images

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Fan Xiangsuo ◽  
Xu Zhiyong
Keyword(s):  
Target Detection ◽  
Detection Algorithm ◽  
Background Modeling ◽  
Small Target ◽  
Local Energy ◽  
Dynamic Scenes ◽  
Detection Ability ◽  
Small Target Detection ◽  
The Difference ◽  
Small Targets

In order to improve the detection ability of dim and small targets in dynamic scenes, this paper first proposes an anisotropic gradient background modeling method combined with spatial and temporal information and then uses the multidirectional gradient maximum of neighborhood blocks to segment the difference maps. On the basis of previous background modeling and segmentation extraction candidate targets, a dim small target detection algorithm for local energy aggregation degree of sequence images is proposed. Experiments show that compared with the traditional algorithm, this method can eliminate the interference of noise to the target and improve the detection ability of the system effectively.

scholarly journals Matched filter stochastic background characterization for hyperspectral target detection

2005 ◽  
Author(s):  
Jason E. West ◽  
David W. Messinger ◽  
Emmett J. Ientilucci ◽  
John P. Kerekes ◽  
John R. Schott
Keyword(s):  
Target Detection ◽  
Matched Filter ◽  
Stochastic Background

Eight-Neighborhood Based Background Modeling

2012 ◽  
Vol 532-533 ◽  
pp. 743-747
Author(s):  
Yan Lu ◽  
Ming Dai ◽  
Lei Jiang
Keyword(s):  
Target Detection ◽  
Kernel Density ◽  
Detection Algorithm ◽  
Background Modeling ◽  
Modeling Method ◽  
Sample Space ◽  
Experimental Results ◽  
Gaussian Kernel ◽  
Background Interference ◽  
Sample Set

In order to solve the Gaussian kernel density-based background modeling, we propose a background modeling method based on an 8-neighborhood pixels sample set. In this method, we use the target pixel and its surrounding 8-neighborhood pixels to analyze whether it is included in the background or in the foreground sample space. Experimental results show that the method can judge the object as the background interference caused by the cyclical movement. By practical verification, the algorithm of the background modeling can full meet the requirements of the target detection algorithm.

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