DocumentCode :
722695
Title :
A Perceptual Depth Shape-based CRF Model for Deformable Surface Labeling
Author :
Gang Hu ; Reilly, Derek ; Qigang Gao ; Bastos, Arthur ; Nhu Loan Truong
Author_Institution :
Fac. of Comput. Sci., Dalhousie Univ., Halifax, NS, Canada
fYear :
2015
fDate :
3-5 June 2015
Firstpage :
184
Lastpage :
191
Abstract :
Real-time deformable scene understanding is a challenging task. In this paper, we address this problem by using Conditional random fields (CRFs) framework and perceptual shape salience occupancy patterns. CRF is a powerful probabilistic model that has been widely used for labelling image segments. It is particularly well-suited to modelling local interactions and global consistency among bottom-up regions (e.g. super pixels). However, its capacity could be limited if the underlying feature potentials are not well reflecting the scene properties. We propose a depth shape-based CRF model for deformable surface (sand in our case) labelling by utilizing expressive novel shape salience occupancy patterns (SOP). Experimental results demonstrate the effectiveness and robustness of the method on recorded video datasets. While our work has concentrated on sand surface labelling, the approach can be applied to other surface materials (e.g. snow, mud), and extended to non-planar surfaces as well (e.g. sculpting blocks).
Keywords :
feature extraction; object tracking; video signal processing; CRF framework; SOP; conditional random field model; deformable scene understanding; deformable surface labeling; image segment labelling; perceptual depth shape-based CRF model; perceptual shape salience occupancy pattern; salience occupancy patterns; video datasets; Cameras; Computational modeling; Image color analysis; Image segmentation; Labeling; Shape; Three-dimensional displays; CRF; deformable surface; depth data; mixed reality games; perceptual shape features; sand tracking; scene understanding;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer and Robot Vision (CRV), 2015 12th Conference on
Conference_Location :
Halifax, NS
Type :
conf
DOI :
10.1109/CRV.2015.31
Filename :
7158338
Link To Document :
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