New Small Target Detection Algorithm via Zernike-Facet Model and the Total Least Squares

2011 ◽  
Vol 30 (1) ◽  
pp. 194-197
Author(s):  
Mou-fa Hu ◽  
Zeng-ping Chen
Keyword(s):  
Least Squares ◽  
Target Detection ◽  
Detection Algorithm ◽  
Total Least Squares ◽  
Small Target ◽  
Small Target Detection ◽  
Facet Model

Infrared dim small target detection algorithm based on NSCT and SVD

2015 ◽  
Author(s):  
Ying Zhao ◽  
Gang Liu ◽  
Huixin Zhou ◽  
Hanlin Qin ◽  
Xiao Li ◽  
...  
Keyword(s):  
Target Detection ◽  
Detection Algorithm ◽  
Small Target ◽  
Small Target Detection

INFRARED SMALL TARGET DETECTION ALGORITHM BASED ON FEATURE SALIENCE AND MULTI-FEATURES FUSION

2011 ◽  
Vol 25 (02) ◽  
pp. 299-308 ◽  
Author(s):  
ZHEN-XUE CHEN ◽  
CHENG-YUN LIU ◽  
FA-LIANG CHANG
Keyword(s):  
Target Detection ◽  
Detection Algorithm ◽  
Probability Of Detection ◽  
Absolute Difference ◽  
Small Target ◽  
Probability Of Error ◽  
Small Target Detection ◽  
Low Snr ◽  
Complex Background ◽  
Small Targets

It is an important and challenging problem to detect small targets in clutter scene and low SNR (Signal Noise Ratio) in infrared (IR) images. In order to solve this problem, a method based on feature salience is proposed for automatic detection of targets in complex background. Firstly, in this paper, the method utilizes the average absolute difference maximum (AADM) as the dissimilarity measurement between targets and background region to enhance targets. Secondly, minimum probability of error was used to build the model of feature salience. Finally, by computing the realistic degree of features, this method solves the problem of multi-feather fusion. Experimental results show that the algorithm proposed shows better performance with respect to the probability of detection. It is an effective and valuable small target detection algorithm under a complex background.

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