We present a novel web-based framework, named Pixel-Based Edge Bundling (PBEB), for effectively and interactively visualizing large graphs. Our framework combines an image-based edge-bundling method and a parallel texture-based processing scheme, allowing us to effectively and efficiently compute edge similarities using kernel density estimation and subsequently group these edges into bundles based on their similarities. We discuss several challenges related to developing large-graph visualization on web-based platforms. To accelerate the edge bundling process and enable interactivity in web-based environments, we leverage texture-based parallel processing, a standard feature of WebGL. Our framework optimizes an end-to-end process, from bundling to rendering, enabling practical and interactive visualization of large graphs in a web-based setting. We demonstrate the superior performance of our framework by conducting comparisons with existing web-based and CUDA-based edge-bundling methods using various standard graphics cards on different devices.

Studying the growth dynamics of developing plants is of critical importance in plant sciences. The traditional methods rely on either manual measurement, which involves tedious labor work, or 2D image-based approaches, which cannot fully characterize plants in 3D. Given the advances of scanners and 3D reconstruction methods, scientists begin to pay more attention to 3D models to improve accuracy. However, existing methods mostly focus on the growth of a whole plant rather than its detailed substructures. In this paper, we have developed an end-to-end pipeline to detect the key events on both the whole plant and the specific components. Our method is achieved by building 3D models from images, segmenting individual components, and capturing traits. We implement an experiment on maizes for evaluation and successfully detect events in the process of growth.

Nowadays, floating-point temporal-spatial datasets are routinely generated from scientific observational apparatuses or computer simulations at an unprecedented pace. The sheer amount of these large volumetric datasets on the order of terabytes or petabytes consume massive resources in terms of bandwidth, storage and computational power. On the other hand, scientists, equipped with low-end post-analysis machines, often find it impossible to visualize and analyze these massive datasets with such limited resources in hand, not to mention their ultimate goal of real time analysis and visualization. To solve this discrepancy, a compact data representation has to be generated and a trade-off between resource consumption and analytical precision has to be found. There are many existing volumetric representation generating methods, almost all of which adopts some kind of hand-engineered heuristics to extract the effective portion of the datasets. However, the trade-off between resource consumption and analytical quality could not be well established due to the introduction of hand-engineered heuristics. In this paper, we present a deep learning based method that can adaptively capture the inherently complicated dynamics of temporal-spatial volumetric datasets without introducing any hand engineered features. We train an autoencoder based neural network with quantization and adaptation. Compared with existing methods, our method could learn data representation at a much lower compressed/uncompressed rate while preserving the details of original datasets. Also, our method could adapt with different data distribution and conduct compression and decompression in real time. Through extensive experiments, we show the effectiveness and efficiency of our approach over existing methods.

Analysis of hyperspectral images is of great importance in many scientific disciplines. Obtaining the spectral and spatial information simultaneously from time-varying hyperspectral images is a challenging task due to their high dimensionality. In this paper, we design an interface that allows users to study hyperspectral images interactively and obtain spectral features and enhanced images at the same time. The image fusion results change dynamically with the regions of interest selected by users and convey both the spatial and spectral information. We show the usefulness of our approach using time-varying hyperspectral plant images. We compare our method with existing hyperspectral image analysis techniques. Our evaluation indicates that our interface can help users determine important bands, identify regions of interest, and generate image fusion results for time-varying hyperspectral plant images.