Wearable mobility aid for low vision using scene classification in a Markov random field model framework

被引:20
作者
Everingham, MR
Thomas, BT
Troscianko, T
机构
[1] Univ Oxford, Dept Engn Sci, Oxford OX1 3PJ, England
[2] Univ Bristol, Dept Comp Sci, Bristol BS8 1TH, Avon, England
[3] Univ Oxford, Perceptual Syst Res Ctr, Oxford OX1 3PJ, England
关键词
D O I
10.1207/S15327590IJHC1502_3
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
This article describes work on a novel approach to vision enhancement for people with severe visual impairments. This approach utilizes computer vision techniques to classify scene content so that visual enhancement of the scene can identify semantically important concepts. The mediated view of a scene presented to the user is in the form of a highly-saturated color image in which distinct colors represent important object types in the scene. The effectiveness of this scheme was demonstrated in a pilot study participated in by people with a range of visual impairments. The scene classification technique uses an artificial neural network classifier Within the framework of a Markov random field model, and the accuracy and robustness of this technique using low quality video images from a hand-held camera is demonstrated.
引用
收藏
页码:231 / 244
页数:14
相关论文
共 17 条
[1]  
BESAG J, 1986, J R STAT SOC B, V48, P259
[2]  
Bishop C. M., 1995, NEURAL NETWORKS PATT
[3]  
Bridle J. S., 1990, Neurocomputing, Algorithms, Architectures and Applications. Proceedings of the NATO Advanced Research Workshop, P227
[4]   Interpreting image databases by region classification [J].
Campbell, NW ;
MacKeown, WPJ ;
Thomas, BT ;
Troscianko, T .
PATTERN RECOGNITION, 1997, 30 (04) :555-563
[5]  
EVERINGHAM MR, 1999, LOW VISION MOBILITY
[6]  
Everingham MR, 1999, INT J VIRTUAL REALIT, V3, P3
[7]   STOCHASTIC RELAXATION, GIBBS DISTRIBUTIONS, AND THE BAYESIAN RESTORATION OF IMAGES [J].
GEMAN, S ;
GEMAN, D .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1984, 6 (06) :721-741
[8]  
Goodrich G., 1995, LOW VIS SHAR INT GRO
[9]   UNSUPERVISED TEXTURE SEGMENTATION USING GABOR FILTERS [J].
JAIN, AK ;
FARROKHNIA, F .
PATTERN RECOGNITION, 1991, 24 (12) :1167-1186
[10]  
Mackeown W.P.J., 1994, THESIS U BRISTOL UK