Pattern Application in Computer Vision and Image Transmission

Pattern Application in Computer Vision and Image Transmission

Authors

  • Xiaohua Yang School of Art University of Sanya, Sanya 572022, Hainan, China

DOI:

https://doi.org/10.53469/wjimt.2023.06(06).07

Keywords:

Computer Vision, Image Transmission, Pattern Application, Cultural and Artistic Works

Abstract

In today's society with rapid digital development, image transmission has become a ubiquitous part of daily life, playing a vital role in spreading information and conveying emotions. However, with the continuous advancement of technology, image transmission also faces various challenges, such as ensuring image quality, accurate transmission of information, and how to effectively utilize new technologies. As an emerging technology, computer vision is gradually attracting people's attention for its application in image transmission. This article will explore the application and potential value of computer vision in image transmission, using a combination of qualitative and quantitative research methods, and analyzing the applications of image processing and image generation through experiments and theory. Research results show that computer vision technology can ensure high quality in the process of image transmission, and also provides a new vision and tools for artistic creation.

References

Alhayani B, Abdallah A A. Manufacturing intelligent Corvus corone module for a secured two way image transmission under WSN[J]. Engineering Computations, 2021, 38(4): 1751-1788.

Ismail N, Malik O A. Real-time visual inspection system for grading fruits using computer vision and deep learning techniques[J]. Information Processing in Agriculture, 2022, 9(1): 24-37.

Guo M H, Xu T X, Liu J J, et al. Attention mechanisms in computer vision: A survey[J]. Computational visual media, 2022, 8(3): 331-368.

Monga V, Li Y, Eldar Y C. Algorithm unrolling: Interpretable, efficient deep learning for signal and image processing[J]. IEEE Signal Processing Magazine, 2021, 38(2): 18-44.

Wu Yufeng, Li Yiming, Zhao Yuanyang, et al. Review of research on body condition scoring of dairy cows based on computer vision [J]. Journal of Agricultural Machinery, 2021, 52(S1): 268-275.

Fan G, Hua Z, Li J. Multi-scale depth information fusion network for image dehazing[J]. Applied Intelligence, 2021, 51(10): 7262-7280.

Harakannanavar S S, Rudagi J M, Puranikmath V I, et al. Plant leaf disease detection using computer vision and machine learning algorithms[J]. Global Transitions Proceedings, 2022, 3(1): 305-310.

Nalbant K G, UYANIK Ş. Computer vision in the metaverse[J]. Journal of Metaverse, 2021, 1(1): 9-12.

Saleh A, Sheaves M, Rahimi Azghadi M. Computer vision and deep learning for fish classification in underwater habitats: A survey[J]. Fish and Fisheries, 2022, 23(4): 977-999.

Yan L, Cengiz K, Sharma A. An improved image processing algorithm for automatic defect inspection in TFT-LCD TCON[J]. Nonlinear Engineering, 2021, 10(1): 293-303.

Gui J, Sun Z, Wen Y, et al. A review on generative adversarial networks: Algorithms, theory, and applications[J]. IEEE transactions on knowledge and data engineering, 2021, 35(4): 3313-3332.

Lindsay G W. Convolutional neural networks as a model of the visual system: Past, present, and future[J]. Journal of cognitive neuroscience, 2021, 33(10): 2017-2031.

Veerasingam S, Ranjani M, Venkatachalapathy R, et al. Contributions of Fourier transform infrared spectroscopy in microplastic pollution research: A review[J]. Critical Reviews in Environmental Science and Technology, 2021, 51(22): 2681-2743.

Kurani A, Doshi P, Vakharia A, et al. A comprehensive comparative study of artificial neural network (ANN) and support vector machines (SVM) on stock forecasting[J]. Annals of Data Science, 2023, 10(1): 183-208.

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Published

2023-12-29
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