Li W J, Lee T
Computer Vision and Image Processing Laboratory, Department of Electronic Engineering, The Chinese University of Hong Kong, Shatin, New Territory, Hong Kong.
IEEE Trans Neural Netw. 2001;12(6):1400-10. doi: 10.1109/72.963776.
The affine transformation, which consists of rotation, translation, scaling, and shearing transformations, can be considered as an approximation to the perspective transformation. Therefore, it is very important to find an effective means for establishing point correspondences under affine transformation in many applications. In this paper, we consider the point correspondence problem as a subgraph matching problem and develop an energy formulation for affine invariant matching by a Hopfield type neural network. The fourth-order network is investigated first, then order reduction is done by incorporating the neighborhood information in the data. Thus we can use the second-order Hopfield network to perform subgraph isomorphism invariant to affine transformation, which can be applied to an affine invariant shape recognition problem. Experimental results show the effectiveness and efficiency of the proposed method.
仿射变换由旋转、平移、缩放和剪切变换组成，可被视为透视变换的一种近似。因此，在许多应用中找到一种在仿射变换下建立点对应关系的有效方法非常重要。在本文中，我们将点对应问题视为子图匹配问题，并通过霍普菲尔德型神经网络开发了一种用于仿射不变匹配的能量公式。首先研究了四阶网络，然后通过将邻域信息纳入数据进行降阶。这样我们就可以使用二阶霍普菲尔德网络来执行对仿射变换不变的子图同构，它可应用于仿射不变形状识别问题。实验结果表明了所提方法的有效性和高效性。
