preprints.org > computer science and mathematics > computer science > doi: 10.20944/preprints202410.2006.v1

Preprint Article Version 1 This version is not peer-reviewed

Version 1 : Received: 24 October 2024 / Approved: 25 October 2024 / Online: 25 October 2024 (11:05:02 CEST)

In recent years, the demand for efficient and accurate measurement techniques has surged across various industries, driving the development of non-invasive methods that leverage computer vision technology. This paper explores the principles and applications of non-invasive measurement techniques utilizing computer vision, emphasizing their ability to deliver precise measurements without the need for physical contact or specialized sensors. We delve into the underlying algorithms, including image processing, machine learning, and deep learning methods, that enable the extraction of dimensional data from visual inputs. Case studies across diverse fields such as manufacturing, healthcare, and logistics illustrate the effectiveness of these techniques in enhancing operational efficiency and reducing measurement errors. Furthermore, we discuss the challenges and limitations of implementing computer vision-based measurement systems, including the need for robust algorithms capable of functioning in varied environmental conditions. By highlighting the transformative potential of non-invasive measurement techniques, this paper aims to provide insights into future developments in computer vision and its role in advancing measurement practices across multiple sectors.

Computer Vision; Non-Invasive Measurement; Image  Analysis; Optical Sensing; Depth Estimation; Machine Learning; Object Detection; Pattern Recognition; Feature Extraction; 3D Reconstruction

Computer Science and Mathematics, Computer Science

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