Low-complexity arrays of patch signature for efficient ancient coin retrieval

IF 3.7 4区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pattern Analysis and Applications Pub Date : 2024-06-19 DOI:10.1007/s10044-024-01284-x
Florian Lardeux, Petra Gomez-Krämer, Sylvain Marchand
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Abstract

We present a new recognition framework for ancient coins struck from the same die. It is called Low-complexity Arrays of Patch Signatures. To overcome the problem of illumination conditions we use multi-light energy maps which are a light-independent, 2.5D representation of the coin. The coin recognition is based on a local texture analysis of the energy maps. Descriptors of patches, tailored to coin images via the properties provided by the energy map, are matched against a database using a system of associative arrays. The system of associative arrays used for the matching is a generalization of the Low-complexity Arrays of Contour Signatures. Hence, the matching is very efficient and nearly at constant time. Due to the lack of available data, we present two new data sets of artificial and real ancient coins respectively. Theoretical insights for the framework are discussed and various experiments demonstrate the promising efficiency of our method.

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用于高效古钱币检索的低复杂度补丁签名阵列
我们提出了一种新的古钱币识别框架。它被称为低复杂度补丁签名阵列。为了克服光照条件的问题,我们使用了多光能量图,这是一种不受光照影响的钱币 2.5D 表示法。硬币识别基于对能量图的局部纹理分析。通过能量图提供的属性为硬币图像量身定制的斑块描述符,使用关联阵列系统与数据库进行匹配。用于匹配的关联阵列系统是低复杂度轮廓特征阵列的一般化。因此,匹配效率非常高,而且时间几乎不变。由于缺乏可用数据,我们提出了两个新的数据集,分别是人工古钱币和真实古钱币。我们讨论了该框架的理论见解,各种实验证明了我们方法的高效性。
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来源期刊
Pattern Analysis and Applications
Pattern Analysis and Applications 工程技术-计算机:人工智能
CiteScore
7.40
自引率
2.60%
发文量
76
审稿时长
13.5 months
期刊介绍: The journal publishes high quality articles in areas of fundamental research in intelligent pattern analysis and applications in computer science and engineering. It aims to provide a forum for original research which describes novel pattern analysis techniques and industrial applications of the current technology. In addition, the journal will also publish articles on pattern analysis applications in medical imaging. The journal solicits articles that detail new technology and methods for pattern recognition and analysis in applied domains including, but not limited to, computer vision and image processing, speech analysis, robotics, multimedia, document analysis, character recognition, knowledge engineering for pattern recognition, fractal analysis, and intelligent control. The journal publishes articles on the use of advanced pattern recognition and analysis methods including statistical techniques, neural networks, genetic algorithms, fuzzy pattern recognition, machine learning, and hardware implementations which are either relevant to the development of pattern analysis as a research area or detail novel pattern analysis applications. Papers proposing new classifier systems or their development, pattern analysis systems for real-time applications, fuzzy and temporal pattern recognition and uncertainty management in applied pattern recognition are particularly solicited.
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