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Using Stacked Sparse Auto-Encoder and Superpixel CRF for Long-Term Visual Scene Understanding of UGVs

Qiu, Z and Zhuang, Y and Hu, H and Wang, W (2017) 'Using Stacked Sparse Auto-Encoder and Superpixel CRF for Long-Term Visual Scene Understanding of UGVs.' IEEE Transactions on Systems Man and Cybernetics: Systems. ISSN 2168-2216

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Abstract

Multiple images have been widely used for scene understanding and navigation of unmanned ground vehicles in long term operations. However, as the amount of visual data in multiple images is huge, the cumulative error in many cases becomes untenable. This paper proposes a novel method that can extract features from a large dataset of multiple images efficiently. Then the membership K-means clustering is used for high dimensional features, and the large dataset is divided into N subdatasets to train N conditional random field (CRF) models based on superpixel. A Softmax subdataset selector is used to decide which one of the N CRF models is chosen as the prediction model for labeling images. Furthermore, some experiments are conducted to evaluate the feasibility and performance of the proposed approach.

Item Type: Article
Uncontrolled Keywords: Conditional random field (CRF), long term navigation, scene understanding, stacked sparse auto-encoder
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 21 Sep 2017 11:52
Last Modified: 25 Jan 2019 16:15
URI: http://repository.essex.ac.uk/id/eprint/20396

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