Multi-scale Wavelet Transform Based Optimization of Edge Detection Algorithm for Package Design Process

被引：0

作者：

Zhao M. ^{[1
]}

Tian H. ^{[1
]}

机构：

[1] Department of Art and Design, Shanxi Technology And Business College, Taiyuan

来源：

Computer-Aided Design and Applications | 2024年 / 21卷 / S12期

关键词：

Computer Aided Design; Edge Detection; Package Design; Virtual Reality;

D O I：

10.14733/cadaps.2024.S12.236-250

中图分类号：

学科分类号：

摘要：

In order to promote the application of computer-aided design (CAD) and virtual reality (VR) in package design, this article puts forward an edge detection algorithm for packaging images based on multi-scale wavelet transform (WT), and uses the multi-scale method of wavelet local modulus maxima to detect edges, so as to optimize the package design process. According to the design requirements of the best edge filter, the selection criteria of wavelet bases for edge detection are determined. In the process of using local maximum of wavelet modulus, aiming at the selection of local maximum, the modulus maximum is calculated along the gradient direction. The results show that the WT algorithm in this article shows high efficiency in optimizing packaging images. It can effectively control the noise of the image, enhance the definition of the image outline and improve the contrast of the image. Moreover, compared with the traditional algorithm, this algorithm has significantly improved the recall and accuracy of product packaging image feature detection. The method in this article has obvious advantages in realizing VR presentation and user experience of package design, mainly in the aspects of good image processing effect, strong interactivity and immersion, and high algorithm efficiency. © 2024 U-turn Press LLC.

引用

页码：236 / 250

页数：14

共 19 条

[1]

Berni A., Borgianni Y., Applications of virtual reality in engineering and product design: why, what, how, when and where, Electronics, 9, 7, (2020)

[2]

Bhatti U.-A., Zhou M., Huo Q., Ali S., Hussain A., Yan Y., Yu Z., Yuan L., Nawaz S.-A., Advanced color edge detection using clifford algebra in satellite images, IEEE Photonics Journal, 13, 2, pp. 1-21, (2021)

[3]

Chen G., Zhang G., Yang Z., Liu W., Multi-scale patch-GAN with edge detection for image inpainting, Applied Intelligence, 53, 4, pp. 3917-3932, (2023)

[4]

Ding Y., Nan X., On edge detection algorithms for water-repellent images of insulators taking into account efficient approaches, Symmetry, 15, 7, (2023)

[5]

Faheem Z.-B., Ishaq A., Rustam F., Torre D.-I., Gavilanes D., Vergara M.-M., Ashraf I., Image watermarking using least significant bit and Canny edge detection, Sensors, 23, 3, (2023)

[6]

Han Z., Shang M., Liu Z., SeqViews2seqlabels: learning 3d global features via aggregating sequential views by RNN with attention, IEEE Transactions on Image Processing, 28, 2, pp. 658-672, (2019)

[7]

Li C., Qu Z., Review of image edge detection algorithms based on deep learning, Journal of Computer Applications, 40, 11, pp. 3280-3288, (2020)

[8]

Liow K.-M., Ng P., Eaw H.-C., Jommachinelearning: bringing artwork nearer with designlab, International Journal of Business Strategy and Automation, 2, 2, pp. 54-71, (2021)

[9]

Liu D., Pu G., Wu X., Quaternion‐based improved cuckoo algorithm for colour UAV image edge detection, IET Image Processing, 16, 3, pp. 926-935, (2022)

[10]

Liu Y., Dong Z., Feng Y., Chen Y., Yang L., Edge detection method for high-resolution remote sensing imagery by combining superpixels with dual-threshold edge tracking, Photogrammetric Engineering & Remote Sensing, 89, 8, pp. 469-476, (2023)

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