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A Lightweight Semantic Segmentation Model for Underwater Images Based on DeepLabv3. [PDF]
J ImagingXiao C, Zhou Z, Hu Y.
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Averse Deep Semantic Segmentation
2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019Semantic segmentation consists in predicting whether any given pixel is part of the object of interest or not. Two types of errors are therefore possible: false positives and false negatives. For visualization and emphasis purposes, we might want to put special effort into reducing one type of error in detriment of the other.
Ricardo, Cruz +2 more
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Semantic Segmentation without Annotating Segments
2013 IEEE International Conference on Computer Vision, 2013Numerous existing object segmentation frameworks commonly utilize the object bounding box as a prior. In this paper, we address semantic segmentation assuming that object bounding boxes are provided by object detectors, but no training data with annotated segments are available.
Wei Xia +4 more
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ACM Transactions on Graphics, 2018
Accurate representation of soft transitions between image regions is essential for high-quality image editing and compositing. Current techniques for generating such representations depend heavily on interaction by a skilled visual artist, as creating such accurate object selections is a tedious task. In this work, we introduce
Yağız Aksoy +4 more
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Accurate representation of soft transitions between image regions is essential for high-quality image editing and compositing. Current techniques for generating such representations depend heavily on interaction by a skilled visual artist, as creating such accurate object selections is a tedious task. In this work, we introduce
Yağız Aksoy +4 more
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2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015
The availability of GIS (Geographical Information System) databases for many urban areas, provides a valuable source of information for improving the performance of many computer vision tasks. In this paper, we propose a method which leverages information acquired from GIS databases to perform semantic segmentation of the image alongside with geo ...
Ardeshir, Shervin +2 more
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The availability of GIS (Geographical Information System) databases for many urban areas, provides a valuable source of information for improving the performance of many computer vision tasks. In this paper, we propose a method which leverages information acquired from GIS databases to perform semantic segmentation of the image alongside with geo ...
Ardeshir, Shervin +2 more
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Covariance Attention for Semantic Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020The dependency between global and local information can provide important contextual cues for semantic segmentation. Existing attention methods capture this dependency by calculating the pixel wise correlation between the learnt feature maps, which is of high space and time complexity.
Yazhou, Liu +3 more
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Prototype-Based Semantic Segmentation
IEEE Transactions on Pattern Analysis and Machine IntelligenceDeep learning based semantic segmentation solutions have yielded compelling results over the preceding decade. They encompass diverse network architectures (FCN based or attention based), along with various mask decoding schemes (parametric softmax based or pixel-query based).
Tianfei Zhou, Wenguan Wang
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Semantic Customers’ Segmentation
2019Many approaches have been proposed to allow customers’ segmentation in retail sector. However, very few contributions exploit the existing semantics links that may exist between objects and resulting groups. The aim of this paper is to overcome this drawback by using semantic similarity measures (SSM) in customers’ segmentation to provide clusters ...
Jocelyn Poncelet +3 more
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Semantic segmentation based on semantic edge optimization
2021 International Conference on Electronic Information Engineering and Computer Science (EIECS), 2021Semantic segmentation based on deep learning is to extract semantic features by convolution, and then classify each pixel. In the process of feature extraction, the subsampled operation used to expand the receptive field will cause a large amount of detail information loss, resulting in the loss of small-scale objects in the image and blurred ...
Hao Hu +3 more
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Semantic Segmentation For Aerial Images
International Journal of Research Publication and ReviewsSemantic segmentation of aerial imagery plays a critical role in modern urban planning, environmental monitoring, and the development of smart cities. This project presents an interactive webbased application that performs semantic segmentation on high-resolution aerial images using a deep learning-based U-Net model.
Dr. B. Harika, M. Bharath, K. Himneesh
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