The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.
Computer Vision and Image Processing: A Paper Review
Open accessAbstract
Computer vision has been studied from many persective. It expands from raw data recording into techniques and ideas combining digital image processing, pattern recognition, machine learning and computer graphics. The wide usage has attracted many scholars to integrate with many disciplines and fields. This paper provide a survey of the recent technologies and theoretical concept explaining the development of computer vision especially related to image processing using different areas of their field application. Computer vision helps scholars to analyze images and video to obtain necessary information, understand information on events or descriptions, and scenic pattern. It used method of multi-range application domain with massive data analysis. This paper provides contribution of recent development on reviews related to computer vision, image processing, and their related studies. We categorized the computer vision mainstream into four group e.g., image processing, object recognition, and machine learning. We also provide brief explanation on the up-to-date information about the techniques and their performance.
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Cites
1 retracted
Yang, H., Zhu, M. Q., Niu, Y., et al. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7298670 · Cited by 7
RetractedGoing deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834
A Computational Approach to Edge Detection
Canny, J. (1986) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1986.4767851 · Cited by 30,004
Scale-space and edge detection using anisotropic diffusion
Perona, P., & Malik, J. (1990) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.56205 · Cited by 12,119
Unsupervised texture segmentation using Gabor filters
Jain, A. K., & Farrokhnia, F. (1991) · Pattern Recognition · doi:10.1016/0031-3203(91)90143-s · Cited by 2,182
Face detection in color images
Hsu, R.-L., Abdel-Mottaleb, M., & Jain, A. K. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.1000242 · Cited by 1,843
Vision based hand gesture recognition for human computer interaction: a survey
Rautaray, S. S., & Agrawal, A. (2015) · Artificial Intelligence Review · doi:10.1007/s10462-012-9356-9 · Cited by 1,526
Multichannel texture analysis using localized spatial filters
Bovik, A. C., Clark, M., & Geisler, W. S. (1990) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.41384 · Cited by 1,382
Salient Object Detection: A Benchmark
Borji, A., Cheng, M.-M., Jiang, H., & Li, J. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2487833 · Cited by 1,364
Cited by
Recent Advances in Artificial Intelligence Sensors
Zhang, Z., Wang, L., & Lee, C. (2023) · Advanced Sensor Research · doi:10.1002/adsr.202200072 · Cited by 107
Metadata
- Authors
- Wiley, V., & Lucas, T.
- Venue
- International Journal of Artificial Intelligence Research
- Publisher
- STMIK Dharma Wacana
- Published
- 2018-06-01
- Volume
- 2
- Issue
- 1
- Pages
- 22
- ISSN
- 2579-7298 (ISSN-L)
- OpenAlex
- W2793959159
- Cited by
- 298
- References
- 45
- FWCI
- 5.73
- Type
- journal article
- Language
- en
- Open access
- diamond
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
