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2018 | OriginalPaper | Buchkapitel

Deep Neural Network for Foreground Object Segmentation: An Unsupervised Approach

verfasst von : Avishek Majumder, R. Venkatesh Babu

Erschienen in: Computer Vision, Pattern Recognition, Image Processing, and Graphics

Verlag: Springer Singapore

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Abstract

Saliency plays a key role in various computer vision tasks. Extracting salient regions from images and videos have been a well established problem of computer vision. While segmenting salient objects from images depend only on static information, temporal information in a video can make non salient objects be salient due to movement. Besides the temporal information, there are other challenges involved with video segmentation, such as 3D parallax, camera shake, motion blur, etc. In this work, we propose a novel unsupervised end to end trainable, fully convolutional deep neural network for object segmentation. Our model is robust and scalable across scenes, as it is tested unsupervisedly and can easily infer which objects constitute the foreground of the image. We run various tests on two well established benchmarks of video object segmentation, DAVIS and FBMS-59 datasets. We report our results and compare them against the state of the art methods.

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Vorheriges Kapitel Dataset Augmentation with Synthetic Images Improves Semantic Segmentation

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Titel: Deep Neural Network for Foreground Object Segmentation: An Unsupervised Approach
verfasst von: Avishek Majumder
R. Venkatesh Babu
Verlag: Springer Singapore
Buch: Computer Vision, Pattern Recognition, Image Processing, and Graphics
Print ISBN: 978-981-13-0019-6

Electronic ISBN: 978-981-13-0020-2

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-981-13-0020-2_32

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