scholarly journals Robust Feature Detection Using 2D Wavelet Transform under Low Light Environment

10.5772/4970 ◽  
2007 ◽  
Author(s):  
Youngouk Kim ◽  
Jihoon Lee ◽  
Woon Cho ◽  
Changwoo Park ◽  
Changhan Park ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Feature Detection ◽  
Light Environment ◽  
Low Light

Low-light image enhancement based on Retinex theory and dual-tree complex wavelet transform

2018 ◽  
Vol 14 (6) ◽  
pp. 470-475 ◽  
Author(s):  
Mao-xiang Yang ◽  
Gui-jin Tang ◽  
Xiao-hua Liu ◽  
Li-qian Wang ◽  
Zi-guan Cui ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Image Enhancement ◽  
Complex Wavelet Transform ◽  
Low Light ◽  
Light Image ◽  
Retinex Theory ◽  
Complex Wavelet

Learning to Decompose and Restore Low-light Images with Wavelet Transform

2021 ◽  
Author(s):  
Pengju Zhang ◽  
Chaofan Zhang ◽  
Zheng Rong ◽  
Yihong Wu
Keyword(s):  
Wavelet Transform ◽  
Low Light

scholarly journals Low-Light Environment Neural Surveillance

2020 ◽  
Author(s):  
Michael Potter ◽  
Henry Gridley ◽  
Noah Lichtenstein ◽  
Kevin Hines ◽  
John Nguyen ◽  
...  
Keyword(s):  
Light Environment ◽  
Low Light

Activity Recognition via Feature Decomposition

2011 ◽  
Vol 243-249 ◽  
pp. 6221-6224
Author(s):  
Qing Wei ◽  
Hao Zhang ◽  
Zhi Jing Liu
Keyword(s):  
Wavelet Transform ◽  
Feature Detection ◽  
Markov Models ◽  
Hidden Markov ◽  
Haar Wavelet ◽  
Human Motion ◽  
Feature Points ◽  
Recognition Method ◽  
Haar Wavelet Transform ◽  
Coupled Hidden Markov Models

This paper presents a new recognition method for human motion, which is represented by Haar wavelet transform and recognized by Coupled Hidden Markov Model. We tackle the challenge of detecting the feature points by Haar wavelet transform to improve the accuracy. We extract binary silhouette after creating the background model. Then the low-level features are detected by Haar wavelet and principal vectors in two subspaces are obtained. We utilize Coupled Hidden Markov Models to model and recognize them, and demonstrate their usability. Compared with others, our approach is simple and effective in feature detection, strength in robustness. Therefore, the video surveillance based on our method is practicable in (but not limited to) many scenarios where the background is known.

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