Daniel Skomedal Breland

Papers

Total Citations

136

H-Index

About

Daniel Skomedal Breland is a leading researcher at the intersection of computer vision, deep learning, and edge computing, with a primary focus on advancing hand gesture and sign language recognition systems. His most significant contributions lie in pioneering the use of thermal imaging as a robust alternative to traditional RGB cameras, addressing critical performance limitations in low-light and challenging environmental conditions. Breland’s work has demonstrated that deep convolutional neural networks (CNNs) can achieve high-accuracy recognition of sign language digits and hand gestures from thermal data, with his foundational 2021 paper on "Deep Learning-Based Sign Language Digits Recognition From Thermal Images With Edge Computing System" accumulating 70 citations. This was complemented by his 2021 study on "Robust Hand Gestures Recognition Using a Deep CNN and Thermal Images," which has garnered 62 citations, establishing his reputation in the field. His most recent 2024 work extends this research to high-resolution thermal imaging, further refining the technology’s applicability in automotive interfaces, human-computer interaction, and industrial robotics. By enabling reliable, contactless communication systems that function effectively regardless of lighting conditions, Breland’s research is paving the way for more inclusive and resilient assistive technologies and human-machine interfaces.