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- W2164393095 abstract "This paper describes a vision-based ground-plane classification system for autonomous indoor mobile-robot that takes advantage of the synergy in combining together multiple visual-cues. A priori knowledge of the environment is important in many biological systems, in parallel with their reactive systems. As such, a learning model approach is taken here for the classification of the ground/object space, initialised through a new Distributed-Fusion (D-Fusion) method that captures colour and textural data using Superpixels. A Markov Random Field (MRF) network is then used to classify, regularise, employ a priori constraints, and merge additional ground/object information provided by other visual cues (such as motion) to improve classification images. The developed system can classify indoor test-set ground-plane surfaces with an average true-positive to false-positive rate of 90.92% to 7.78% respectively on test-set data. The system has been designed in mind to fuse a variety of different visual-cues. Consequently it can be customised to fit different situations and/or sensory architectures accordingly." @default.
- W2164393095 created "2016-06-24" @default.
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- W2164393095 date "2010-12-01" @default.
- W2164393095 modified "2023-09-23" @default.
- W2164393095 title "Ground-plane classification for robot navigation: Combining multiple cues toward a visual-based learning system" @default.
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- W2164393095 doi "https://doi.org/10.1109/icarcv.2010.5707289" @default.
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