Results 1 to 10 of about 58,990 (298)
Semantic Segmentation Algorithm Combined with Edge Detection [PDF]
Jisuanji gongcheng, 2021 To address the fuzzy and inaccurate target edges in semantic segmentation,a semantic segmentation network combined with edge detection is proposed.The network consists of an edge detection network and a semantic segmentation network.The edge detection ...
WANG Nan, HOU Zhiqiang, ZHAO Mengqi, YU Wangsheng, MA Sugang
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SEMANTIC SEGMENTATION OF INDOOR 3D POINT CLOUDS BY JOINT OPTIMIZATION OF GEOMETRIC FEATURES AND NEURAL NETWORKS [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2022 Indoor navigation, indoor robotics, and other deep applications of interior space can be realized through semantic segmentation of 3D point clouds. We propose a semantic segmentation method for point clouds that uses geometric features of point clouds ...
M. M. Yao +9 more
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Research Review of Image Semantic Segmentation Method in High-Resolution Remote Sensing Image Interpretation [PDF]
Jisuanji kexue yu tansuo, 2023 Rapid acquisition of remote sensing information has important research significance for the development of image semantic segmentation methods in remote sensing image interpretation applications.
MA Yan, Gulimila·Kezierbieke
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Segmenter: Transformer for Semantic Segmentation [PDF]
2021 IEEE/CVF International Conference on Computer Vision (ICCV), 2021 Image segmentation is often ambiguous at the level of individual image patches and requires contextual information to reach label consensus. In this paper we introduce Segmenter, a transformer model for semantic segmentation. In contrast to convolution-based methods, our approach allows to model global context already at the first layer and throughout ...
Strudel, Robin +3 more
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Survey of Research in Image Semantic Segmentation Based on Deep Neural Network [PDF]
Jisuanji gongcheng, 2020 With the rapid development of deep learning and its widespread applications in semantic segmentation,the quality of semantic segmentation has been significantly improved.This paper reviews and analyzes the mainstream deep neural network-based methods in ...
JING Zhuangwei, GUAN Haiyan, PENG Daifeng, YU Yongtao
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Survey of Image Semantic Segmentation Methods Based on Deep Neural Network
Jisuanji kexue yu tansuo, 2021 Image semantic segmentation is a hot research topic in the field of computer vision in recent years. With the rise of deep learning technology, image semantic segmentation and deep learning technology are integrated and developed, which has made ...
XU Hui, ZHU Yuhua, ZHEN Tong, LI Zhihui
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2D Semantic Segmentation: Recent Developments and Future Directions
Future Internet, 2023 Semantic segmentation is a critical task in computer vision that aims to assign each pixel in an image a corresponding label on the basis of its semantic content.
Yu Guo +3 more
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Journal of Information Technology Research, 2023 Semantic segmentation was traditionally performed using primitive methods; however, in recent times, a significant growth in the advancement of deep learning techniques for the same is observed. In this paper, an extensive study and review of the existing deep learning (DL)-based techniques used for the purpose of semantic segmentation is carried out ...
null Aakanksha +2 more
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YUTO SEMANTIC: A LARGE SCALE AERIAL LIDAR DATASET FOR SEMANTIC SEGMENTATION [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2023 Creating virtual duplicates of the real world has garnered significant attention due to its applications in areas such as autonomous driving, urban planning, and urban mapping.
S. Yoo, C. Ko, G. Sohn, H. Lee
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A TWO-STAGE APPROACH FOR RARE CLASS SEGMENTATION IN LARGE-SCALE URBAN POINT CLOUDS [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2022 Although deep learning has greatly improved the semantic segmentation accuracy of point clouds, the segmentation of rare classes in large-scale urban scenes has not been targeted in available methods.
X. Zhang, R. Xue, R. Xue, U. Soergel
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