Kenneth Chen, Niall Williams, Colin Groth, Jenna Kang, Alexandre Chapiro, Qi Sun, Aaron Hertzmann, Laurie Wilcox, Richard Zhang, Rafał K. Mantiuk
Nikoleta Manakitsa, George S. Maraslidis, Lazaros Moysis, George F. Fragulis
Machine vision, an interdisciplinary field that aims to replicate human visual perception in computers, has experienced rapid progress and significant contributions. This paper traces the origins of machine vision, from early image processing algorithms to its convergence with co…
Maroš Krupáš, Ľubomír Urblík, Iveta Zolotová
Recent advances in multimodal large language models (MLLMs)—particularly vision– language models (VLMs)—introduce new possibilities for integrating visual perception with natural-language understanding in human–machine collaboration (HMC). Unmanned aerial vehicles (UAVs) are incr…
Zhike Zhao, Linman Song, Songying Li, Ruihao Xue, Peng Li
Traditional acupoint localization methods rely heavily on manual operation, resulting in high subjectivity and limited accuracy. To improve the precision and stability of acupoint detection, this study integrates machine vision technology with in situ projection to achieve automa…
Marian Marcel Abagiu, Dorian Cojocaru, Florin Manta, Alexandru Mariniuc
This paper describes the implementation of a solution for detecting the machining defects from an engine block, in the piston chamber. The solution was developed for an automotive manufacturer and the main goal of the implementation is the replacement of the visual inspection per…
Dong-Han Mo, Chuen-Lin Tien, Yu-Ling Yeh, Yi-Ru Guo, Chern-Sheng Lin, Chih-Chin Chen, et al.
In this study, the design of a Digital-twin human-machine interface sensor (DT-HMIS) is proposed. This is a digital-twin sensor (DT-Sensor) that can meet the demands of human-machine automation collaboration in Industry 5.0. The DT-HMIS allows users/patients to add, modify, delet…
Bushra Mughal, Fernando B. Duarte, Tiago Cunha Reis, Carlos Jorge Dos Santos Limão Sebastiã
Automated detection of suspicious human activities in complex and crowded environments remains a critical challenge in modern surveillance systems due to high false-positive rates, poor contrast and generalization across diverse scenes. We propose a GM_CNN3D Model for the classif…