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Deep Semantic Segmentation of Natural and Medical Images: A Review
The (medical) image semantic segmentation task consists of classifying e...
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A Survey on Deep Learning Methods for Semantic Image Segmentation in Real-Time
Semantic image segmentation is one of fastest growing areas in computer ...
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PaddleSeg: A High-Efficient Development Toolkit for Image Segmentation
Image Segmentation plays an essential role in computer vision and image ...
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Multi-Atlas Segmentation of Biomedical Images: A Survey
Multi-atlas segmentation (MAS), first introduced and popularized by the ...
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Beyond Pixels: A Comprehensive Survey from Bottom-up to Semantic Image Segmentation and Cosegmentation
Image segmentation refers to the process to divide an image into nonover...
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Greenery Segmentation In Urban Images By Deep Learning
Vegetation is a relevant feature in the urban scenery and its awareness ...
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Bladder segmentation based on deep learning approaches: current limitations and lessons
Precise determination and assessment of bladder cancer (BC) extent of mu...
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Image Segmentation Using Deep Learning: A Survey
Image segmentation is a key topic in image processing and computer vision with applications such as scene understanding, medical image analysis, robotic perception, video surveillance, augmented reality, and image compression, among many others. Various algorithms for image segmentation have been developed in the literature. Recently, due to the success of deep learning models in a wide range of vision applications, there has been a substantial amount of works aimed at developing image segmentation approaches using deep learning models. In this survey, we provide a comprehensive review of the literature at the time of this writing, covering a broad spectrum of pioneering works for semantic and instance-level segmentation, including fully convolutional pixel-labeling networks, encoder-decoder architectures, multi-scale and pyramid based approaches, recurrent networks, visual attention models, and generative models in adversarial settings. We investigate the similarity, strengths and challenges of these deep learning models, examine the most widely used datasets, report performances, and discuss promising future research directions in this area.
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