A focused target segmentation paradigm

1996 ◽  
pp. 579-588 ◽  
Author(s):  
Dinesh Nair ◽  
J. K. Aggarwal
Keyword(s):  
Target Segmentation

Infrared weak target segmentation under complicated background based on mountain clustering

2010 ◽  
Vol 30 (2) ◽  
pp. 367-369
Author(s):  
Tao LI ◽  
Kai-bin LEI ◽  
Jian LIU ◽  
Jian-ying CHEN
Keyword(s):  
Target Segmentation ◽  
Weak Target

scholarly journals Convolutional Neural Network for the Semantic Segmentation of Remote Sensing Images

2021 ◽  
Vol 26 (1) ◽  
pp. 200-215
Author(s):  
Muhammad Alam ◽  
Jian-Feng Wang ◽  
Cong Guangpei ◽  
LV Yunrong ◽  
Yuanfang Chen
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Neural Networks ◽  
Image Processing ◽  
Deep Learning ◽  
Semantic Segmentation ◽  
Natural Scene ◽  
Remote Sensing Images ◽  
Advantages And Disadvantages ◽  
Target Segmentation

AbstractIn recent years, the success of deep learning in natural scene image processing boosted its application in the analysis of remote sensing images. In this paper, we applied Convolutional Neural Networks (CNN) on the semantic segmentation of remote sensing images. We improve the Encoder- Decoder CNN structure SegNet with index pooling and U-net to make them suitable for multi-targets semantic segmentation of remote sensing images. The results show that these two models have their own advantages and disadvantages on the segmentation of different objects. In addition, we propose an integrated algorithm that integrates these two models. Experimental results show that the presented integrated algorithm can exploite the advantages of both the models for multi-target segmentation and achieve a better segmentation compared to these two models.

Infrared Small Target Segmentation with Multiscale Feature Representation

2021 ◽  
pp. 103755
Author(s):  
Lian Huang ◽  
Shaosheng Dai ◽  
Tao Huang ◽  
Xiangkang Huang ◽  
Hailing Wang
Keyword(s):  
Feature Representation ◽  
Small Target ◽  
Target Segmentation ◽  
Infrared Small Target

Moving Target Detection Based on Principle of Connectivity and Texture Gradient

2012 ◽  
Vol 605-607 ◽  
pp. 2117-2120
Author(s):  
Min Huang ◽  
Yang Zhang ◽  
Gang Chen ◽  
Guo Feng Yang
Keyword(s):  
Target Detection ◽  
Detection Algorithm ◽  
Experimental Results ◽  
Texture Gradient ◽  
Moving Target ◽  
Moving Target Detection ◽  
Gradient Information ◽  
Target Segmentation ◽  
Target Profile

In target detection, “hole” phenomenon is present in the detection result, and the shadow is difficult to remove. To solve these problems, we propose a target detection algorithm based on principle of connectivity and texture gradient. Firstly, we use the connectivity principle to find the largest target prospects connection area to get a complete target contour, secondly we use target texture gradient information to further remove the shadow of the target. At last, the experimental results show that the algorithm can obtain a clear target profile and improve the accuracy of the moving target segmentation.

Target Segmentation for Hyperspectral Imagery Based on FastICA

2010 ◽  
Vol 39 (6) ◽  
pp. 1003-1009 ◽  
Author(s):  
粘永健 NIAN Yong-jian ◽  
张志 ZHANG Zhi ◽  
王力宝 WANG Li-bao ◽  
万建伟 WAN Jian-wei
Keyword(s):  
Hyperspectral Imagery ◽  
Target Segmentation

scholarly journals Accuracy of metabolic volume and total glycolysis among six threshold-based target segmentation algorithms

2020 ◽  
Vol 34 (8) ◽  
pp. 583-594
Author(s):  
Tetsu Nakaichi ◽  
Shozo Yamashita ◽  
Wataru Kawakami ◽  
Haruki Yamamoto ◽  
Masayuki Sasaki ◽  
...  
Keyword(s):  
Target Segmentation ◽  
Segmentation Algorithms ◽  
Metabolic Volume
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