Description: This paper presents a method based on kernel resolution of the next scene, as the other matching algorithms. Attracted by charge simulation model for computing the data ranging from high-dimensional similarity dimension SNN kernel function. The image feature points mapped to the radial basis vector (Radial basis vector, RBV) space, calculated using SNN kernel similarity of two feature point sets and the transition matrix. permutation test module to enhance the stability of the nuclear SNN to ensure the reliability of the output solution. Experiments show that SNN-based scene matching algorithm kernel image distortion, noise and signal loss are highly robust, and can ensure high precision and high real time.
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基于SNN 核的景象匹配算法.pdf
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