In the era of Big Data, augmented reality (AR)-based 3D visualization technology is gradually becoming an essential tool of effective information dissemination. However, 3D data visualization inevitably faces a series of challenges, particularly when visualizing large datasets within limited physical spaces. This often results in high spatial density, characterized by smaller target objects and occlusion of data objects, which greatly complicate object selection and subsequent interaction. To address these issues, this paper proposes a multimodal progressive target selection technique-StarPicker, which integrates the SpotLight metaphor and Clock metaphor. By leveraging wrist rotation and gesture interactions, StarPicker facilitates a coarse-to-fine semantic disambiguation and precise selection of target objects. To validate the effectiveness of StarPicker, a comparative user study was conducted against the state-of-the-art techniques (GridWall and FlowerCone) under high-density (up to 360 objects) and small-target (1cm) conditions. Experimental results demonstrate that StarPicker significantly outperforms the baselines in terms of target selection accuracy and user satisfaction, while achieving comparable or better completion times, especially in denser scenes. This work offers a novel approach to target selection and interaction technology in the field of AR-based 3D Big Data visualization.

The purpose of this study was to implement principal component analysis (PCA) on videocapsule endoscopy (VE) images to develop a new computerized tool for celiac disease recognition. Three PCA algorithms were implemented for feature extraction and sparse representation. A novel strip PCA (SPCA) with nongreedy L1-norm maximization is proposed for VE image analysis. The extracted principal components were interpreted by a non-parametric k-nearest neighbor (k-NN) method for automated celiac disease classification. A benchmark dataset of 460 images (240 from celiac disease patients with small intestinal villous atrophy versus 220 control patients lacking villous atrophy) was constructed from the clinical VE series. It was found that the newly developed SPCA with nongreedy L1-norm maximization was most efficient for computerized celiac disease recognition, having a robust performance with an average recognition accuracy of 93.9 percent. Furthermore, SPCA also has a reduced computation time as compared with other methods. Therefore, it is likely that SPCA will be a helpful adjunct for the diagnosis of celiac disease.