用于验证硬币的可变分辨率图像处理

O. Martens, T. Saar, A. Gavrijaseva, A. Molder
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引用次数: 9

摘要

本文针对高速运动的欧元硬币验证问题,提出了基于相关性的图像匹配方法,并对其进行了评价。图像处理(结合其他电磁、光学和声学传感器)是硬币识别和验证的有效方法。从图像处理的角度来看,一种通用的方法是通过将硬币图像与预期(参考)图像相互关联来查找和检查硬币图像。相关性作为一种有效的相似性度量在匹配任务中得到了广泛的应用。然而,传统的基于相关性的匹配方法受到各种因素的限制。这里的主要挑战(当然,除了对比较图像进行“预处理”之外)是计算量的显著减少,这是在二维空间中寻找相互关联值和所有可能的旋转值所需要的。在目前的工作中,已经提出并评估了可变速率下采样(像素块)参考图像的使用。考虑了一种特殊情况,其中大多数图像块以零(无)或一个采样值采样,而图像的几个块(一些到几十个)以完全精度采样(例如240 - 240像素,在所使用的示例中)。用于选择参考图像的“特定”(唯一)块的标准是样本块(例如20−20或30−30像素)对同一硬币图像的任何其他(移位或旋转)块的最大相互关系的最小值。因此,移位和旋转时“次级”相关峰值较高的块不被认为是“好的或唯一的”块。为此,本文提出了各种欧元硬币的子采样参考图像,并对相应的算法进行了评价。另外,使用图像的一组特征点,作为相互关联的参考,已经评估了各种欧洲硬币(与相应的确定合理的特征点,为这些硬币)。或者,使用基于局部最大和最小差分的感兴趣像素块作为相互关联的参考块已经被讨论过。这些方法已经在真实的欧元硬币上进行了测试,结果在论文的最后给出。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Variable-resolution image processing for validation of coins
In this paper correlation-based matching solutions of images for the case of the validation of the eurocoins with relatively high-speed motion have been developed and evaluated. Image processing (combined with other — electromagnetic, optical and acoustical sensorics) is an efficient method for coin recognition and validation. From the image processing viewpoint — an universal method is finding and checking the coin image by cross-correlating it with expected (reference) image(s). Correlation is widely used as an effective similarity measure in matching tasks. However, traditional correlation based matching methods are limited by various ways. Main challenge here (additionally to "pre-conditioning" of compared images, of course) is the significant reduction of the computations, needed for finding cross-correlation values over 2-D space and for all possible rotation values. In current work usage of variable-rate subsampled (by pixel blocks) reference images has been proposed and evaluated. A special case has been considered, where most of the image blocks are sampled with zero (none) or one sample-values, while a few blocks (some to some tens) of the image has been sampled at the full accuracy (eg 240−240 pixels, in the used examples). The criteria, used for selection of the “specific” (unique) blocks of the reference image has been the minimum value of the maximum cross-correlation of an block of samples (eg 20−20 or 30−30 pixels) against any other (shifted or rotated) block of the same coin image. So, blocks with relatively high value of “secondary “correlation peaks at shifting and rotating are not considered as “good or unique” ones. So, sub-sampled reference images for various eurocoins has been proposed, and corresponding algorithms has been evaluated. Alternatively, using of set of feature-points of the images, as reference for cross-correlation, has been evaluated, for various eurocoins (with corresponding determination of reasonable feature points, for these coins). Alternatively, using local maximum and minimum difference based interesting pixel blocks as reference blocks for cross-correlation has been discussed. These methods have been tested on real eurocoins, the results are presented at the end of paper.
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