Analysis of Graphic Design Based on AI Interaction Technology.

Area of Science:

• Computer Vision
• Human-Computer Interaction
• Image Processing

Background:

• Effective visual communication in human-computer interfaces (HCI) is crucial for user experience.
• Existing image enhancement techniques may not adequately address the specific visual perception needs of interactive graphical interfaces.
• Optimizing graphical elements for visual clarity and impact remains an ongoing challenge.

Purpose of the Study:

• To design and develop a color enhancement system for plane visual images within graphical human-machine interfaces (HMI).
• To improve the local contrast and overall visual perception of graphical elements.
• To meet the specific requirements of human visual perception for interactive displays.

Main Methods:

• Utilized the naive Bayes classification algorithm to determine plane feature vectors.
• Applied a color image enhancement algorithm based on visual characteristics to improve local contrast.
• Established a hierarchical optimization model for graphical HMI, incorporating visual perception intensity and element importance.
• Solved the optimization model using a genetic algorithm.

Main Results:

• Demonstrated high efficiency in image color processing.
• Achieved superior image enhancement effects compared to existing methods.
• Confirmed that the system effectively meets the requirements of human visual perception.
• Validated the system's ability to improve the visual effect of plane visual image color.

Conclusions:

• The developed human-computer interface plane visual image color enhancement system significantly improves visual effects.
• The integration of naive Bayes classification and visual characteristic-based enhancement offers an effective approach for graphical HMI.
• The system's optimization model and genetic algorithm solution contribute to enhanced visual perception in interactive displays.