Abstract
Computer vision systems aim to analyze data collected from the environment and derive an interpretation from completing a specified task. Vision system tasks are object recognition, low-level processing, representation, model construction, and matching subtasks. One of the classical problems addressed by machine vision is object recognition determining whether or not the image data contains some specific object, feature, or activity. Object recognition or object classification goal is to recognize pre-specified or learning objects or object classes. The application of object recognition provides the ability to recognize objects with their 2D positions in the image or 3D poses in the scene, while the recognition ability of computer vision can also be used for identifying a person’s face or fingerprint, handwritten digits, or a specific vehicle (e.g., Google Goggles).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Bibliography
Alex, K., Ilya, S., & Geoffrey, E. (2017). Hinton Communications of the ACM. 60(6):84–90. https://doi.org/10.1145/3065386
Alzubaidi, L., Zhang, J., & Humaidi, A. J. et al. (2021). Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J Big Data 8, 53. https://doi.org/10.1186/s40537-021-00444-8
Baker, S. (1998). Design and evaluation of feature detectors. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.39.9490
Jiao, L., Zhang, F., Liu, F., Yang, S., Li, L., Feng, Z., & Qu, R. (2019). A Survey of Deep Learning-Based Object Detection. IEEE Access, 7, 128837–128868.
Mahony, N. O., Campbell, S., Carvalho, A., Harapanahalli, S., Velasco-Hernández, G.A., Krpalkova, L., Riordan, D., & Walsh, J. (2019). Deep Learning vs. Traditional Computer Vision. CVC.
Sun, S., Cao, Z., Zhu, H., & Zhao, J. (2020). A Survey of Optimization Methods From a Machine Learning Perspective. IEEE Transactions on Cybernetics, 50, 3668–3681.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Namdari, H., Nanda, D., Yuan, X. (2022). Progress in Computer Vision: Object Recognition. In: Albert, M.V., Lin, L., Spector, M.J., Dunn, L.S. (eds) Bridging Human Intelligence and Artificial Intelligence. Educational Communications and Technology: Issues and Innovations. Springer, Cham. https://doi.org/10.1007/978-3-030-84729-6_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-84729-6_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-84728-9
Online ISBN: 978-3-030-84729-6
eBook Packages: EducationEducation (R0)