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Open Access Exploring Variants of Fully Convolutional Networks with Local and Global Contexts in Semantic Segmentation Problem

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Recently, the semantic inference from images is widely used for various applications, such as augmented reality, autonomous robots, and indoor navigation. As a pioneering work for semantic segmentation, the fully convolutional networks (FCN) was introduced and outperformed traditional methods. However, since FCN only takes account of the local contextual dependency, it does not reflect the global contextual dependency. In this paper, we explore variants of FCN with local and global contextual dependencies in the semantic segmentation problem. In addition, we tried to improve the performance of semantic segmentation with extra depth information from a commercial RGBD camera. Our experiment result indicates that exploiting the global contextual dependencies and the additional depth information improves the quality of semantic segmentation
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Keywords: Deep learning; RGBD; Semantic segmentation

Document Type: Research Article

Publication date: January 13, 2019

This article was made available online on January 13, 2019 as a Fast Track article with title: "Exploring variants of fully convolutional networks with local and global contexts in semantic segmentation problem".

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  • For more than 30 years, the Electronic Imaging Symposium has been serving those in the broad community - from academia and industry - who work on imaging science and digital technologies. The breadth of the Symposium covers the entire imaging science ecosystem, from capture (sensors, camera) through image processing (image quality, color and appearance) to how we and our surrogate machines see and interpret images. Applications covered include augmented reality, autonomous vehicles, machine vision, data analysis, digital and mobile photography, security, virtual reality, and human vision. IS&T began sole sponsorship of the meeting in 2016. All papers presented at EIs 20+ conferences are open access.

    Please note: For purposes of its Digital Library content, IS&T defines Open Access as papers that will be downloadable in their entirety for free in perpetuity. Copyright restrictions on papers vary; see individual paper for details.

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