Image binarization method for markers tracking in extreme light conditions

IF 5.8 2区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Integrated Computer-Aided Engineering Pub Date : 2022-01-01 DOI:10.3233/ica-210674

M. Ćurković, A. Curkovic, D. Vucina

引用次数: 3

Abstract

Image binarization is one of the fundamental methods in image processing and it is mainly used as a preprocessing for other methods in image processing. We present an image binarization method with the primary purpose to find markers such as those used in mobile 3D scanning systems. Handling a mobile 3D scanning system often includes bad conditions such as light reflection and non-uniform illumination. As the basic part of the scanning process, the proposed binarization method successfully overcomes the above problems and does it successfully. Due to the trend of increasing image size and real-time image processing we were able to achieve the required small algorithmic complexity. The paper outlines a comparison with several other methods with a focus on objects with markers including the calibration system plane of the 3D scanning system. Although it is obvious that no binarization algorithm is best for all types of images, we also give the results of the proposed method applied to historical documents.

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极端光照条件下标记物跟踪的图像二值化方法

图像二值化是图像处理的基本方法之一，主要用作图像处理中其他方法的预处理。我们提出了一种图像二值化方法，其主要目的是寻找诸如移动3D扫描系统中使用的标记。处理移动3D扫描系统通常包括光线反射和不均匀照明等恶劣条件。作为扫描过程的基础部分，本文提出的二值化方法成功地克服了上述问题，并取得了成功。由于图像尺寸和实时图像处理的趋势，我们能够实现所需的小算法复杂度。本文重点对三维扫描系统的标定系统平面等有标记的物体进行了比较。虽然很明显，没有一种二值化算法对所有类型的图像都是最好的，但我们也给出了将所提出的方法应用于历史文档的结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Integrated Computer-Aided Engineering 工程技术-工程：综合

CiteScore

9.90

自引率

21.50%

发文量

审稿时长

>12 weeks

期刊介绍： Integrated Computer-Aided Engineering (ICAE) was founded in 1993. "Based on the premise that interdisciplinary thinking and synergistic collaboration of disciplines can solve complex problems, open new frontiers, and lead to true innovations and breakthroughs, the cornerstone of industrial competitiveness and advancement of the society" as noted in the inaugural issue of the journal. The focus of ICAE is the integration of leading edge and emerging computer and information technologies for innovative solution of engineering problems. The journal fosters interdisciplinary research and presents a unique forum for innovative computer-aided engineering. It also publishes novel industrial applications of CAE, thus helping to bring new computational paradigms from research labs and classrooms to reality. Areas covered by the journal include (but are not limited to) artificial intelligence, advanced signal processing, biologically inspired computing, cognitive modeling, concurrent engineering, database management, distributed computing, evolutionary computing, fuzzy logic, genetic algorithms, geometric modeling, intelligent and adaptive systems, internet-based technologies, knowledge discovery and engineering, machine learning, mechatronics, mobile computing, multimedia technologies, networking, neural network computing, object-oriented systems, optimization and search, parallel processing, robotics virtual reality, and visualization techniques.