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Image identification with a 3-D charge simulation retina

机译：使用3D电荷模拟视网膜进行图像识别

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摘要

Studies were conducted To establish the feasibility for a charge simulation retina model to identify shape and size. The retina consisted of sensory cells which detected image features generated by different 3-D objects that were arbitrarilylocated in the retina's field of view. The image features were compressed by a charge simulation method algorithm by computing output signals at work cells located in the retina. With these signals, neural networks were used to classify each image, toidentify shape and size. Classification rates above 75% for both shape and size were obtained, showing that it is feasible for the retina to identify shape and size. Since object displacement affected the performance of the retina, to minimisemisclassification, it is necessary that the centre of area of each object is kept within a circle whose radius is one - tenth that of the retina and is measured from the centre of the retina base.
机译：进行研究以建立电荷模拟视网膜模型以识别形状和大小的可行性。视网膜由感觉细胞组成，该感觉细胞检测由任意3D对象生成的图像特征，这些3D对象在视网膜的视野中任意放置。通过计算位于视网膜上的工作单元处的输出信号，通过电荷模拟方法算法对图像特征进行压缩。利用这些信号，使用神经网络对每个图像进行分类，以识别形状和大小。形状和大小的分类率均高于75％，这表明视网膜识别形状和大小是可行的。由于物体的位移影响了视网膜的性能，因此，为了实现最小限度的分类，必须将每个物体的区域中心保持在一个半径为视网膜半径的十分之一的圆内，该半径是从视网膜底部的中心开始测量的。

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