Advanced Computer Vision Techniques for Accurate Measurement in Unmanned Mobile Robots

For years, researchers have been studying computer vision, i.e. the ability of artificial intelligence (AI) systems to perceive and interpret visual data like humans. This study is gaining increasing attention as researchers aim to develop tools that automate visual tasks and replicate human visual...

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Bibliographic Details
Published inMeasurement science review Vol. 24; no. 5; pp. 188 - 192
Main Authors Bharathi, V, Natraj, N A, Gopinath, S, Kiruthikaa, R
Format Journal Article
LanguageEnglish
Published Bratislava Sciendo 01.10.2024
De Gruyter Poland
Subjects
Algorithms
Artificial intelligence
Automation
Computer vision
data extraction
Digital computers
digital image processing
Digital imaging
Image processing
image recognition
Task complexity
Technology assessment
Visual observation
Visual tasks
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Summary:For years, researchers have been studying computer vision, i.e. the ability of artificial intelligence (AI) systems to perceive and interpret visual data like humans. This study is gaining increasing attention as researchers aim to develop tools that automate visual tasks and replicate human visual awareness. However, the interpretation of images is very complex due to the vast amount of multi-resolution information they contain, making the development of AI technologies for visual recognition particularly challenging. This article provides an overview of digital image processing, highlighting the main concepts and introducing key algorithms. These methods are designed to capture, process, and interpret digital images and enable the extraction of important data from real-world environments. We conduct rigorous image processing tests and compare AI-driven recognition systems with human analysis. The results show that computer vision technology significantly outperforms human observation in terms of accuracy and consistency. These results highlight the potential of computer vision to revolutionize various industries by automating complex visual tasks and offer promising future applications in areas such as healthcare, security, and manufacturing. The paper provides valuable insights into current advances in digital image processing and the role of AI in improving visual recognition capabilities, paving the way for further innovation in this area.
ISSN:1335-8871
1335-8871
DOI:10.2478/msr-2024-0025