This system is made up of image capture unit, 3 parallel neural network recognition units, mobile characteristics calculation unit, syncretism logic, and corresponding software.
该系统由图像捕获单元，3个并行工作的神经网络识别单元、运动特征计算单元、融合逻辑和相应的软件构成。
In order to improve the pattern recognition precision, using the neural network recognition and optimal method, a new pattern recognition method of shape signal was established.
为了提高带材板形识别的精度，应用神经网络和最优化方法，构建了一种新的板形信号模式识别方法。
Ground objects can be effectively recognized by gray co-occurrence vector and gray co - dimension feature vector with BP neural network and Bayesian network, recognition rate of 70%.
利用灰度共生纹理特征向量和灰度共生-差分维数联合特征向量结合BP神经网络和朴素贝叶斯网络都能对地物进行有效识别，识别率在70%以上。
Ground objects can be effectively recognized by gray co-occurrence vector and gray co - dimension feature vector with BP neural network and Bayesian network, recognition rate of 70%.
利用灰度共生纹理特征向量和灰度共生-差分维数联合特征向量结合BP神经网络和朴素贝叶斯网络都能对地物进行有效识别，识别率在70%以上。
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