Results 31 to 40 of about 30,915 (267)
Semantic Image Segmentation Using Transformers
, 2023 Abstract Since the development of deep learning, FCNNs—particularly "U-shaped" encoder-decoder architectures—have excelled at various medical semantic segmentation tasks. For the bulk of medical picture segmentation applications during the past ten years, Fully Convolutional Neural Networks (FCNNs) with contracting and expanding paths have ...
SAI KRISHNA DOPPALAPUDI, SUPREETHI K. P
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Semantic Image Segmentation: Two Decades of Research
Foundations and Trends® in Computer Graphics and Vision, 2022 Semantic image segmentation (SiS) plays a fundamental role in a broad variety of computer vision applications, providing key information for the global understanding of an image. This survey is an effort to summarize two decades of research in the field of SiS, where we propose a literature review of solutions starting from early historical methods ...
Csurka, Gabriela +2 more
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Image semantic segmentation method based on GAN network and ERFNet model
The Journal of Engineering, 2021 This article addresses the problems of traditional methods in image semantic segmentation, such as insufficient segmentation of small‐scale targets and weak anti‐noise ability.
Chaoxian Dong
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Remote Sensing, 2023 The wide application and rapid development of satellite remote sensing technology have put higher requirements on remote sensing image segmentation methods.
Zheng Zhou +6 more
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Towards Open-Set Semantic Segmentation Of Aerial Images [PDF]
2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS), 2020 Abstract. Classical and more recently deep computer vision methods are optimized for visible spectrum images, commonly encoded in grayscale or RGB colorspaces acquired from smartphones or cameras. A more uncommon source of images exploited in the remote sensing field are satellite and aerial images.
C. C. V. da Silva +4 more
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Remote Sensing, 2022 With the development of deep learning algorithms, more and more deep learning algorithms are being applied to remote sensing image classification, detection, and semantic segmentation.
Yongxiu Zhou +5 more
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SEMANTIC SEGMENTATION OF BUILDING IN AIRBORNE IMAGES [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2019 Abstract. Building is a key component to the reconstructing of LoD3 city modelling. Compared to terrestrial view, airborne datasets have more occlusions at street level but can cover larger area in the urban areas. With the popularity of the Deep Learning, many tasks in the field of computer vision can be solved in easier and efficiency way.
S. Huang, F. Nex, Y. Lin, M. Y. Yang
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Weakly Supervised Deep Semantic Segmentation Using CNN and ELM with Semantic Candidate Regions
Complexity, 2019 The task of semantic segmentation is to obtain strong pixel-level annotations for each pixel in the image. For fully supervised semantic segmentation, the task is achieved by a segmentation model trained using pixel-level annotations.
Xinying Xu +4 more
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Semantic Segmentation of 3D Medical Images with 3D Convolutional Neural Networks
CLEI Electronic Journal, 2020 A neural network is a mathematical model that is able to perform a task automatically or semi-automatically after learning the human knowledge that we provided.
Alejandra Márquez Herrera +2 more
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Semantic Image Segmentation with Contextual Hierarchical Models [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016 Semantic segmentation is the problem of assigning an object label to each pixel. It unifies the image segmentation and object recognition problems. The importance of using contextual information in semantic segmentation frameworks has been widely realized in the field. We propose a contextual framework, called contextual hierarchical model (CHM), which
Mojtaba, Seyedhosseini, Tolga, Tasdizen
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