基于级联神经网络的实用型三维复合不变性多目标识别

以三 飞机模型作为待识别目标，模型真实场景，对用于多目标分类识别的级联神经网络重新进行了研究。实验发现畜产品上降的主要原因是实际采集的目标发生的复杂畸变与计算机模拟产生的效果并不一样。用采集得到的目标图像作为训练样本，对网络重新构造和训练，取得了好的实验结果。分析了其中涉及到目标定位、图像分割等图像预处理问题，提出了一种基于二值图像开矿学腐蚀运算的快速目标检测位法，可快速有效地对目标进行检测定 。

Real-Time Recognition of Multi-Targets with Complicated Distortion Based on Cascaded Neural Networks

Further work was done on the cascaded neural network system for muti target recognition. Three plane models were used as targets to be recognized to imitate the real scene. It is found that the recognizing rate is lowered dramatically mostly due to complex distortions occuring in applications, which are very different with that stimulated by computers. Utilizing images of the models as training samples, the network was reconstructed and retrained. Good results were obtained. And to approach image preprocessing problems, such as location and segmentation, an efficient method of quick target detection and location is proposed based on binary morphological erosion algorithm.