基于相似性度量和反向传播神经网络的图像定位算法

Jun Yan, Hongliu Zhu
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摘要

本文提出了一种基于相似性测量和反向传播神经网络的图像定位技术。首先,采用双线性插值方法对得到的图像进行尺寸归一化处理。然后,计算相对距离作为每个采集位置的标签。选取余弦、Hist和SSIM相似度等图像相似度度量作为采集位置的指纹。最后利用bp神经网络进行回归学习,得到基于距离的回归函数。现场测试表明,该算法比现有的基于图像的定位方法能获得更精确的位置估计。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Image Based Localization Algorithm Using Similarity Measurements and Back-Propagation Neural Network
In this paper, an image based localization technique is proposed by similarity measurements and back-propagation neural network (BPNN). First, the bilinear interpolation method is used for size normalization of the obtained images. Then, the relative distance is calculated as the label of each collection position. The image similarity measurements, such as cosine, Hist and SSIM similarity are chosen as the fingerprint of the collection position. At last, the BPNN is utilized for regression learning and obtain the distance based regression function. Field tests show that the proposed algorithm can obtain more accurate position estimation than other existing image based localization approaches.
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