The fusion of infrared and visible images via decomposition-based structure transfer and local saliency detection

2022 ◽  
Vol 149 ◽  
pp. 107787
Author(s):  
Xu Chen ◽  
Lei Liu ◽  
Xiangyu Kong
IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 20676-20688 ◽  
Author(s):  
Jing Li ◽  
Hongtao Huo ◽  
Chenhong Sui ◽  
Chenchen Jiang ◽  
Chang Li

2019 ◽  
Vol 101 ◽  
pp. 45-55 ◽  
Author(s):  
Song Minghui ◽  
Liu Lu ◽  
Peng Yuanxi ◽  
Jiang Tian ◽  
Li Jun

Author(s):  
Han Liu ◽  
Bo Li ◽  
Tao Zheng ◽  
Jiaxu Yao
Keyword(s):  

Author(s):  
M. N. Favorskaya ◽  
L. C. Jain

Introduction:Saliency detection is a fundamental task of computer vision. Its ultimate aim is to localize the objects of interest that grab human visual attention with respect to the rest of the image. A great variety of saliency models based on different approaches was developed since 1990s. In recent years, the saliency detection has become one of actively studied topic in the theory of Convolutional Neural Network (CNN). Many original decisions using CNNs were proposed for salient object detection and, even, event detection.Purpose:A detailed survey of saliency detection methods in deep learning era allows to understand the current possibilities of CNN approach for visual analysis conducted by the human eyes’ tracking and digital image processing.Results:A survey reflects the recent advances in saliency detection using CNNs. Different models available in literature, such as static and dynamic 2D CNNs for salient object detection and 3D CNNs for salient event detection are discussed in the chronological order. It is worth noting that automatic salient event detection in durable videos became possible using the recently appeared 3D CNN combining with 2D CNN for salient audio detection. Also in this article, we have presented a short description of public image and video datasets with annotated salient objects or events, as well as the often used metrics for the results’ evaluation.Practical relevance:This survey is considered as a contribution in the study of rapidly developed deep learning methods with respect to the saliency detection in the images and videos.


2019 ◽  
Vol 31 (5) ◽  
pp. 761
Author(s):  
Xiao Lin ◽  
Zuxiang Liu ◽  
Xiaomei Zheng ◽  
Jifeng Huang ◽  
Lizhuang Ma

2011 ◽  
Vol 33 (7) ◽  
pp. 1625-1631 ◽  
Author(s):  
Lin Lian ◽  
Guo-hui Li ◽  
Hai-tao Wang ◽  
hao Tian ◽  
Shu-kui Xu

2019 ◽  
Vol 5 (6) ◽  
pp. 57 ◽  
Author(s):  
Gang Wang ◽  
Bernard De Baets

Superpixel segmentation can benefit from the use of an appropriate method to measure edge strength. In this paper, we present such a method based on the first derivative of anisotropic Gaussian kernels. The kernels can capture the position, direction, prominence, and scale of the edge to be detected. We incorporate the anisotropic edge strength into the distance measure between neighboring superpixels, thereby improving the performance of an existing graph-based superpixel segmentation method. Experimental results validate the superiority of our method in generating superpixels over the competing methods. It is also illustrated that the proposed superpixel segmentation method can facilitate subsequent saliency detection.


2021 ◽  
pp. 1-1
Author(s):  
Xiaoliang Qian ◽  
Xi Cheng ◽  
Gong Cheng ◽  
Xiwen Yao ◽  
Liying Jiang
Keyword(s):  

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