一种使用neocognitron的汽车检测系统

S. Yamaguchi, H. Itakura
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引用次数: 5

摘要

介绍了一种基于新认知器的汽车图像检测系统。该系统可以在不考虑车辆种类差异和位置变化影响的情况下成功识别汽车图像。通过主动引入新认知器需要识别的模式特征,可以减少细胞平面的数量。新认知器使用垂直和水平线以及组合作为训练模式。因此,细胞平面数量的增加可以被控制住。虽然在训练过程中,除了在输出层中,没有直接使用汽车图像,但该系统可以熟练地检测到汽车。因此,使用适当的输入模式特征,新认知器获得足够的识别能力。
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A car detection system using the neocognitron
A car image detection system using the neocognitron is described. The system can recognize car images successfully without regard to influences of the differences of kinds of cars and shifts in position. The number of cell planes can be reduced by actively introducing features of patterns to be recognized by the neocognitron. The neocognitron uses vertical and horizontal lines and combinations as training patterns. The increase of the number of cell planes can thus be held down. Although car images are not directly used in the training process except in the output layer, the system can detect cars skilfully. Thus, using appropriate features of input patterns, the neocognitron obtains sufficient recognition capability.<>
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