NDLI: Widening residual skipped network for semantic segmentation

Content Provider	IET Digital Library
Author	Su, Wen Wang, Zengfu
Abstract	Over the past two years deep convolutional neural networks have pushed the performance of computer vision systems to soaring heights on semantic segmentation. In this study, the authors present a novel semantic segmentation method of using a deep fully convolutional neural network to achieve image segmentation results with more precise boundary localisation. The above segmentation engine is trainable, and consists of an encoder network with widening residual skipped connections and a decoder network with a pixel-wise classification layer. Here the encoder network with widening residual skipped connections allows the combination of shallow layer features and deep layer semantic features, and the decoder network with classification layer maps the low-resolution encoder features to full resolution image with pixel-wise classification. The experimental results on PASCAL VOC 2012 semantic segmentation dataset and Cityscapes dataset show that the proposed method is effective and competitive.
Starting Page	880
Ending Page	887
Page Count	8
ISSN	17519659
Volume Number	11
e-ISSN	17519667
Issue Number	Issue 10, Oct (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/11/10
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.0070
Journal	IET Image Processing
Publisher Date	2017-05-18
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Cityscapes Dataset Classification Layer Maps Computer Vision Computer Vision And Image Processing Technique Computer Vision System Decoder Network Deep Convolutional Neural Network Deep Layer Semantic Features Encoder Network Image And Video Coding Image Classification Image Coding Image Resolution Image Segmentation Low-resolution Encoder Neural Computing Technique Neural Nets PASCAL VOC 2012 Semantic Segmentation Dataset Pixel-wise Classification Layer Precise Boundary Localisation Residual Skipped Connections Residual Skipped Network Resolution Image
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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