<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>To address the problem of poor visual expression due to traditional methods’ poor image enhancement effect, this paper proposes an image processing model for visual communication of the human-computer interaction interface based on hierarchical analysis. The edge contour feature extraction method is used to extract the convenient information features of the HCI interface, and the cross-sectional information fusion technique is combined to realize the visual communication information enhancement processing of the HCI interface, extract the spatial multi-resolution feature amount of the visual communication image of the HCI interface, and realize the visual communication optimization design of the HCI interface. In the image processing model proposed in this paper, the traditional PCANet and computer vision methods are compared to test the effectiveness of the three image recognition methods. The recognition accuracy of the method proposed in this paper is 82.55%, 85.28%, 87.06%, and 85.87% for the four types of images, respectively; the recognition accuracy of PCANet is 75.53%, 76.48%, 81.26%, and 74.74%, respectively; and the recognition accuracy of computer vision method is 73.79%, 72.83%, 79.36%, and 73.71%. Comparing the data, it can be seen that the model in this paper can improve the fidelity of visual communication and the discriminative ability of interface output, which can greatly improve the performance of the graphical human-computer interaction interface.</p>
</abstract>

To address the problem of poor visual expression due to traditional methods’ poor image enhancement effect, this paper proposes an image processing model for visual communication of the human-computer interaction interface based on hierarchical analysis. The edge contour feature extraction method is used to extract the convenient information features of the HCI interface, and the cross-sectional information fusion technique is combined to realize the visual communication information enhancement processing of the HCI interface, extract the spatial multi-resolution feature amount of the visual communication image of the HCI interface, and realize the visual communication optimization design of the HCI interface. In the image processing model proposed in this paper, the traditional PCANet and computer vision methods are compared to test the effectiveness of the three image recognition methods. The recognition accuracy of the method proposed in this paper is 82.55%, 85.28%, 87.06%, and 85.87% for the four types of images, respectively; the recognition accuracy of PCANet is 75.53%, 76.48%, 81.26%, and 74.74%, respectively; and the recognition accuracy of computer vision method is 73.79%, 72.83%, 79.36%, and 73.71%. Comparing the data, it can be seen that the model in this paper can improve the fidelity of visual communication and the discriminative ability of interface output, which can greatly improve the performance of the graphical human-computer interaction interface.

Application of image processing technology based on hierarchical analysis in visual communication system

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

To address the problem of poor visual expression due to traditional methods’ poor image enhancement effect, this paper proposes an image processing model for...

Application of image processing technology based on hierarchical analysis in visual communication system

Online veröffentlicht: 26. Aug. 2023

Seitenbereich: -

Eingereicht: 24. Okt. 2022

Akzeptiert: 26. März 2022

DOI: https://doi.org/10.2478/amns.2023.2.00239

Schlüsselwörter
Hierarchical analysis, Image processing, Visual communication, Human-computer interaction, Information fusion

© 2023 Yi Cheng, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Application of image processing technology based on hierarchical analysis in visual communication system

Online veröffentlicht: 26. Aug. 2023

Seitenbereich: -

Eingereicht: 24. Okt. 2022

Akzeptiert: 26. März 2022

DOI: https://doi.org/10.2478/amns.2023.2.00239

SchlüsselwörterHierarchical analysis, Image processing, Visual communication, Human-computer interaction, Information fusion

© 2023 Yi Cheng, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Hierarchical analysis, Image processing, Visual communication, Human-computer interaction, Information fusion