Multi-Feature Fusion Network for Salient Region Detection

2019 ◽  
Vol E102.A (6) ◽  
pp. 834-841
Author(s):  
Zheng FANG ◽  
Tieyong CAO ◽  
Jibin YANG ◽  
Meng SUN
Keyword(s):  
Feature Fusion ◽  
Salient Region Detection ◽  
Salient Region ◽  
Region Detection

scholarly journals Salient Region Detection with Multi-Feature Fusion and Edge Constraint

2020 ◽  
Vol E103.D (4) ◽  
pp. 910-913
Author(s):  
Cheng XU ◽  
Wei HAN ◽  
Dongzhen WANG ◽  
Daqing HUANG
Keyword(s):  
Feature Fusion ◽  
Salient Region Detection ◽  
Salient Region ◽  
Region Detection

scholarly journals Military Object Real-Time Detection Technology Combined with Visual Salience and Psychology

Electronics ◽  
2018 ◽  
Vol 7 (10) ◽  
pp. 216 ◽  
Author(s):  
Xia Hua ◽  
Xinqing Wang ◽  
Dong Wang ◽  
Jie Huang ◽  
Xiaodong Hu
Keyword(s):  
Visual Information ◽  
Feature Fusion ◽  
Visual Salience ◽  
Salient Region Detection ◽  
Salient Region ◽  
Detection Model ◽  
Region Detection ◽  
Accurate Detection ◽  
Detection Technology ◽  
Salient Regions

This paper presents a method of military object detection through the combination of human visual salience and visual psychology, so as to achieve rapid and accurate detection of military objects on the vast and complex battlefield. Inspired by the process of human visual information processing, this paper establishes a salient region detection model based on double channel and feature fusion. In this model the pre-attention channel is to process information on the position and contrast of images, and the sub-attention channel is to integrate information on primary visual features first and then merges results of the two channels to determine the salient region. The main theory of Gestalt visual psychology is then used as the constraint condition to integrate the candidate salient regions and to obtain the object figure with overall perception. After that, the efficient sub-window search method is used to detect and filter the object in order to determine the location and range of objects. The experimental results show that, when compared with the existing algorithms, the algorithm proposed in this paper has prominent advantages in precision, effectiveness, and simplicity, which not only significantly reduces the effectiveness of battlefield camouflage and deception but also achieves the rapid and accurate detection of military objects, thus promoting its application prospect.

Efficient 3D mesh salient region detection using local homogeneity measure

2018 ◽  
Vol 12 (9) ◽  
pp. 1663-1672 ◽  
Author(s):  
Abdul Rahman El Sayed ◽  
Abdallah El Chakik ◽  
Hassan Alabboud ◽  
Adnan Yassine
Keyword(s):  
Salient Region Detection ◽  
3D Mesh ◽  
Local Homogeneity ◽  
Salient Region ◽  
Region Detection

Exploiting Surroundedness and Superpixel cues for salient region detection

2020 ◽  
Vol 79 (15-16) ◽  
pp. 10935-10951
Author(s):  
Yifeng Jiang ◽  
Shan Chang ◽  
Enxing Zheng ◽  
Linna Hu ◽  
Ranran Liu
Keyword(s):  
Salient Region Detection ◽  
Salient Region ◽  
Region Detection

Multi-Resolution Salient Region Detection in Frequency Domain

2014 ◽  
Vol 6 (1) ◽  
pp. 60-70
Author(s):  
Yingchun Guo ◽  
Yanhong Feng ◽  
Gang Yan ◽  
Shuo Shi
Keyword(s):  
Frequency Domain ◽  
Euclidean Distance ◽  
Detection Method ◽  
Saliency Map ◽  
Normal Distribution Function ◽  
Salient Region Detection ◽  
Salient Region ◽  
Region Detection ◽  
Proposed Model ◽  
Salient Regions

Salient region detection is a challenge problem in computer vision, which is useful in image segmentation, region-based image retrieval, and so on. In this paper we present a multi-resolution salient region detection method in frequency domain which can highlight salient regions with well-defined boundaries of object. The original image is sub-sampled into three multi-resolution layers, and for each layer the luminance and color salient features are extracted in frequency domain. Then, the significant values are calculated by using invariant laws of Euclidean distance in Lab space and the normal distribution function is used to specify the salient map in each layer in order to remove noise and enhance the correlation among the vicinity pixels. The final saliency map is obtained by normalizing and merging the multi-resolution salient maps. Experimental evaluation depicts the promising results from the proposed model by outperforming the state-of-art frequency-tuned model.

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