scholarly journals Gaze Prediction Improvement by Adding a Face Feature to a Saliency Model

10.5772/7445 ◽  
2009 ◽  
Author(s):  
Marat Sophie ◽  
Guyader Nathalie ◽  
Pellerin Denis
Keyword(s):  
Saliency Model ◽  
Gaze Prediction

Single-shot Weakly-supervised Object Detection Guided by Empirical Saliency Model

2021 ◽  
Author(s):  
Danpei Zhao ◽  
Zhichao Yuan ◽  
Zhenwei Shi ◽  
Fengying Xie
Keyword(s):  
Object Detection ◽  
Single Shot ◽  
Saliency Model ◽  
Weakly Supervised

scholarly journals A Novel Driving Behavior Learning and Visualization Method With Natural Gaze Prediction

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 18560-18568
Author(s):  
Guoliang Yuan ◽  
Yafei Wang ◽  
Jinjia Peng ◽  
Xianping Fu
Keyword(s):  
Driving Behavior ◽  
Visualization Method ◽  
Behavior Learning ◽  
Gaze Prediction

scholarly journals Visual Saliency Model-Based Image Watermarking with Laplacian Distribution

Information ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 239
Author(s):  
Hongmei Liu ◽  
Jinhua Liu ◽  
Mingfeng Zhao
Keyword(s):  
Simulation Analysis ◽  
Image Watermarking ◽  
Additive White Gaussian Noise ◽  
Visual Saliency ◽  
High Energy ◽  
Distribution Model ◽  
Saliency Model ◽  
Detection Approach ◽  
Laplacian Distribution ◽  
Visual Saliency Model

To improve the invisibility and robustness of the multiplicative watermarking algorithm, an adaptive image watermarking algorithm is proposed based on the visual saliency model and Laplacian distribution in the wavelet domain. The algorithm designs an adaptive multiplicative watermark strength factor by utilizing the energy aggregation of the high-frequency wavelet sub-band, texture masking and visual saliency characteristics. Then, the image blocks with high-energy are selected as the watermark embedding space to implement the imperceptibility of the watermark. In terms of watermark detection, the Laplacian distribution model is used to model the wavelet coefficients, and a blind watermark detection approach is exploited based on the maximum likelihood scheme. Finally, this paper performs the simulation analysis and comparison of the performance of the proposed algorithm. Experimental results show that the proposed algorithm is robust against additive white Gaussian noise, JPEG compression, median filtering, scaling, rotation attack and other attacks.

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