Wearable mobility aid for low vision using scene classification in a Markov random field model framework
Wearable mobility aid for low vision using scene classification in a Markov random field model framework
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DOI：
10.1207/s15327590ijhc1502_3
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发表时间：
2003-01-01
影响因子：
4.7
通讯作者：
Troscianko, T
中科院分区：
文献类型：
--
作者：
Everingham, MR;Thomas, BT;Troscianko, T
本文介绍了一种新的方法来增强视力的人有严重的视觉障碍的工作。该方法利用计算机视觉技术对场景内容进行分类，以便场景的视觉增强可以识别语义上重要的概念。呈现给用户的场景的介导视图是高度饱和的彩色图像的形式，其中不同的颜色表示场景中的重要对象类型。这项计划的有效性已在一项有一系列视力障碍的人参加的试点研究中得到证明。场景分类技术使用人工神经网络分类器的马尔可夫随机场模型的框架内，这种技术使用低质量的视频图像从手持摄像机的准确性和鲁棒性证明。
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.