Visual Search in 3D: Effects of Monoscopic and Stereoscopic Cues to Depth on the Validity of Feature Integration Theory and Perceptual Load Theory

Optimization and Application of Information Visualization Design Based on Image Symbol under the Guidance of Feature Integration Theory

Increasingly, today's businesses rely on data visualization to aid in the outcome that is directly linked to the bulk of their earnings. Due to the enormous volume, speed, and accuracy requirements of data management, database professionals are becoming increasingly necessary to aid in the effective visualization of data. Assuming the information to be depicted is free of ambiguity, most visualization approaches were developed. However, this is a rare occurrence. There has been a recent upsurge in visualizations that attempt to convey a sense of unpredictability. When it comes to visual optimization, we present a novel cognitive fuzzy logic-based particle swarm optimization (CFLPSO) to optimize the data visualizations. Initially, the datasets are gathered as images as well as are denoised and enhanced by employing the bilateral three-dimensional fairing median filter (B-3D-FMF) and contrast illuminate histogram equalization (CIHE), correspondingly. Principal component analysis (PCA) is utilized in the feature extraction stage to extract the features from the enhanced data. Then, the feature integration theory is applied to the extracted features, and also a fast rectangle-packing algorithm is applied to the data visualization. And the proposed approach is employed for visual optimization. The performance of the proposed technique is examined and compared with other existing techniques to obtain the proposed technique with the greatest effectiveness of visual optimization. The findings are depicted by utilizing the Origin tool.