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CLASSIFICATION OF VISUAL SENSATIONS GENERATED ELECTRICALLY IN THE VISUAL FIELD OF THE BLIND

机译:盲区视场中电气产生的视觉感应的分类

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摘要

Within the framework of the OPTIVIP project, an optic nerve based visual prosthesis is being developed in order to restore partial vision to the blind. In this paper, we concentrate on the classification problem of visual sensations generated by multiple electrical stimulations. We propose to use probabilistic neural networks in order to perform Bayesian classification. Statistical sampling techniques are utilized in order to reduce the bias on the estimated performances and assess the sensitivity of the method. In noisy environment the Parzen window estimator seems more reliable than finite Gaussian mixtures.
机译:在OPTIVIP项目的框架内,正在开发一种基于视神经的视觉假体,以恢复盲人的部分视力。在本文中,我们集中于由多个电刺激产生的视觉感觉的分类问题。我们建议使用概率神经网络来执行贝叶斯分类。为了减少对估计性能的偏见并评估该方法的敏感性,使用了统计采样技术。在嘈杂的环境中,Parzen窗估计器似乎比有限的高斯混合可靠。

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