Image Segmentation Using Deep Learning: A Survey
- URL: http://arxiv.org/abs/2001.05566v5
- Date: Sun, 15 Nov 2020 04:51:11 GMT
- Title: Image Segmentation Using Deep Learning: A Survey
- Authors: Shervin Minaee, Yuri Boykov, Fatih Porikli, Antonio Plaza, Nasser
Kehtarnavaz, and Demetri Terzopoulos
- Abstract summary: Image segmentation is a key topic in image processing and computer vision.
There has been a substantial amount of works aimed at developing image segmentation approaches using deep learning models.
- Score: 58.37211170954998
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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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