意大利里拉按LVQ分类

S. Omatu, T. Fujinaka, T. Kosaka, H. Yanagimoto, M. Yoshioka
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引用次数: 11

摘要

本文提出了一种基于学习向量量化(LVQ)的意大利里拉分类新方法。使用1000、2000、5000、10000、50000(新)、50000(旧)、100000(新)、100000(旧)里拉8种,A、B、C、D四个方向的意大利里拉,其中A、B表示正方向和倒立方向,C、D表示A、B的反方向。在交易机器上观察到128 × 64像素的原始图像,其中包含旋转和移位。在对这些影响进行校正后,我们选择一个合适的区域来显示账单图像,并将64 × 15像素的图像馈送给神经网络。虽然LVQ类型的神经网络可以对输入数据的任意维序进行处理,但是越小越好,收敛速度越快。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Italian Lira classification by LVQ
In this paper, a new method to classify the Italian Liras by using the learning vector quantization (LVQ) is proposed. The Italian Liras of 8 kinds, 1000, 2000, 5000, 10000, 50000 (new), 50000 (old), 100000 (new), 100000 (old) Liras with four directions A,B,C, and D are used, where A and B mean the normal direction and the upside down direction and C and D mean the reverse version of A and B. The original image with 128 by 64 pixels is observed at the transaction machine in which rotation and shift are included. After correction of these effects, we select a suitable area which shows the bill image and feed the image with 64 by 15 pixels to a neural network. Although the neural network of the LVQ type can process in any order of the dimension of the input data, the smaller size is better to achieve a faster convergence.
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