基于方面图技术的三维运动目标识别

三维目标在不同的视点下呈现不同的姿态 ,所得的二维视图也不尽相同 ,因此三维目标识别是一个较为复杂的问题 .为此提出了通过图象序列和图象序列之间的转移关系 ,根据胜者为王的原则来识别三维目标的方法 .该方法采用极指数栅格技术和傅立叶变换相结合得到目标的轮廓不变量 ;用神经网络结合方面图技术 ,通过识别运动目标图象序列来识别三维运动目标 ,实现了一个目标识别系统 .实验结果证明 ,此方法可以有效地用于三维运动目标的识别

3D Moving Object Recognition Based on Aspect Graphs

The poses of 3D object in different viewpoints are different, so the 2D digital images in different viewpoints are different. Therefore, the 3D object recognition is difficult. In this paper, the approach to recognize 3D moving object by image sequence and the transition relationship of the images in terms of the principle of "the winner is the king" is produced. Polar-exponential grid technique maps the image in Cartesian coordinate into polar-log coordinate. The profile of object in polar-log coordinate has translation, rotation and scale invariant by Fourier transform and it can be used as invariant to recognize object. In this paper, the profile invariant is obtained by polar-exponential grid technique and Fourier transform, and 3D moving object is recognized by neural network and aspect graphs. An object recognition system is carried out. The experimental results show that this method is effective to recognize 3D moving object.