Overview of key technologies of human-computer interaction
DOI:
https://doi.org/10.54691/bcpbm.v18i.579Keywords:
Human-computer interaction, Cognitive psychology, Flexible screen, Object Detection and ClassificationAbstract
In the 21st century, computers are developing faster and faster. Human-computer interaction(HCL) is defined as a multidisciplinary field of study which focuses on the interaction between humans and computer technology. The advancement of HCL makes a great contribution to nowadays’ society as human beings gradually perceive themselves in a technological world that is in a time of high-speed development.The situation of human-computer interaction is becoming more and more common, and computers have more and more intelligent factors. The latest computer technology has been applied to various disciplines, which have developed rapidly and achieved remarkable research results. In this paper, we summarizes the core technologies involved in human-computer interaction (HCI), mainly including human-computer interaction psychology, flexible screen technology, target recognition and classification, and expounds the current situation and development trend of these technologies.
Downloads
References
Elhai J D, Rozgonjuk D. Editorial overview: Cyberpsychology: reviews of research on the intersection between computer technology use and human behavior [J]. Current Opinion in Psychology, 2020, 36: iv-vii.
Xing L, Chen W, Wang J, et al. Long Range MLRS Fire Control Model's Design Based on Human-computer Intersection Primitive [J]. Ship Electronic Engineering, 2013.
Majid A . Human Olfaction at the Intersection of Language, Culture, and Biology[J]. Trends in Cognitive Sciences, 2020.
Sawantdesai M. Human Computer Interaction based on Psychology and Interactive system design. 2014.
Lai C C, Tsai W H. Human-Computer Interaction Using Eye-Glasses Shape Information by 3-D Computer Vision Techniques. 1999.
Ren S, He K, Girshick R, et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks [J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2017, 39(6):1137-1149.
Ren S, He K, Girshick R, et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks [J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2017, 39(6):1137-1149.
[Girshick R, Donahue J, Darrell T, et al. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation [J]. IEEE Computer Society, 2013.
Related S, Related S. Gradient-based learning applied to document recognition [61].
Technicolor T, Related S, Technicolor T, et al. ImageNet Classification with Deep Convolutional Neural Networks [50].
Simonyan K, Zisserman A. Very Deep Convolutional Networks for Large-Scale Image Recognition [J]. Computer Science, 2014.
Downloads
Published
Issue
Section
