Saliency detection based on salient edges and remarkable discriminating for superpixel pairs

2017 ◽  
Vol 77 (5) ◽  
pp. 5949-5968
Author(s):  
Zhengping Hu ◽  
Zhenbin Zhang ◽  
Zhe Sun ◽  
Shuhuan Zhao
Keyword(s):  
Saliency Detection ◽  
Salient Edges

scholarly journals High-definition human visual attention mapping using wavelets

2021 ◽  
Author(s):  
Yusuf Saber
Keyword(s):  
Visual Attention ◽  
Saliency Detection ◽  
High Definition ◽  
Biological Inspiration ◽  
Saliency Maps ◽  
Spectral Residual ◽  
Salient Regions ◽  
Novel Approaches ◽  
Salient Edges ◽  
Human Visual Attention

In this work, three novel approaches to detecting visual attention in images are presented. The idea behind detecting areas within images or video that naturally attract a viewer’s attention is based on the concept of generating pre-attentive saliency maps. Saliency, in and of itself, relates to some measure of “conspicuity” in the visual field and is believed to be an important precursor for many tasks in computer vision. One of the proposed methods in this thesis detects salient regions, while the other two detect salient edges. The classical approach to saliency detection proposed by Itti is extended by introducing wavelets as a lossless resizing tool while maintaining the aspect of biological inspiration. In addition to this, the spectral residual method and the frequency tuned method are modified using wavelets to allow for salient edge detection. Tests show that the proposed methods yield results that are not only comparable to leading,cutting-edge methods, but also exceed them in terms of correct and complete object detection as well as noise reduction.

scholarly journals High-definition human visual attention mapping using wavelets

2021 ◽  
Author(s):  
Yusuf Saber
Keyword(s):  
Visual Attention ◽  
Saliency Detection ◽  
High Definition ◽  
Biological Inspiration ◽  
Saliency Maps ◽  
Spectral Residual ◽  
Salient Regions ◽  
Novel Approaches ◽  
Salient Edges ◽  
Human Visual Attention

In this work, three novel approaches to detecting visual attention in images are presented. The idea behind detecting areas within images or video that naturally attract a viewer’s attention is based on the concept of generating pre-attentive saliency maps. Saliency, in and of itself, relates to some measure of “conspicuity” in the visual field and is believed to be an important precursor for many tasks in computer vision. One of the proposed methods in this thesis detects salient regions, while the other two detect salient edges. The classical approach to saliency detection proposed by Itti is extended by introducing wavelets as a lossless resizing tool while maintaining the aspect of biological inspiration. In addition to this, the spectral residual method and the frequency tuned method are modified using wavelets to allow for salient edge detection. Tests show that the proposed methods yield results that are not only comparable to leading,cutting-edge methods, but also exceed them in terms of correct and complete object detection as well as noise reduction.

A Universal Descent Model for Saliency Detection

2019 ◽  
Author(s):  
Han Liu ◽  
Bo Li ◽  
Tao Zheng ◽  
Jiaxu Yao
Keyword(s):  
Saliency Detection

Saliency detection in deep learning era: trends of development

2019 ◽  
pp. 10-36 ◽  
Author(s):  
M. N. Favorskaya ◽  
L. C. Jain
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Event Detection ◽  
Visual Analysis ◽  
Saliency Detection ◽  
Salient Object Detection ◽  
Public Image ◽  
Detection Methods ◽  
Salient Object ◽  
Salient Event

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.

Saliency Detection Based on Improved Manifold Ranking via Convex Hull

2019 ◽  
Vol 31 (5) ◽  
pp. 761
Author(s):  
Xiao Lin ◽  
Zuxiang Liu ◽  
Xiaomei Zheng ◽  
Jifeng Huang ◽  
Lizhuang Ma
Keyword(s):  
Convex Hull ◽  
Saliency Detection ◽  
Manifold Ranking

scholarly journals Superpixel Segmentation Based on Anisotropic Edge Strength

2019 ◽  
Vol 5 (6) ◽  
pp. 57 ◽  
Author(s):  
Gang Wang ◽  
Bernard De Baets
Keyword(s):  
Distance Measure ◽  
Saliency Detection ◽  
Experimental Results ◽  
Segmentation Method ◽  
Superpixel Segmentation ◽  
Gaussian Kernels ◽  
First Derivative ◽  
Edge Strength

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.

Two-stream Encoder GAN with Progressive Training for Co-Saliency Detection

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