Fast Color Space Transformations Using Minimax Approximations

Color space transformations are frequently used in image processing, graphics, and visualization applications. In many cases, these transformations are complex nonlinear functions, which prohibits their use in time-critical applications. In this paper, we present a new approach called Minimax Approximations for Color-space Transformations (MACT).We demonstrate MACT on three commonly used color space transformations. Extensive experiments on a large and diverse image set and comparisons with well-known multidimensional lookup table interpolation methods show that MACT achieves an excellent balance among four criteria: ease of implementation, memory usage, accuracy, and computational speed

Visual Knowledge Discovery and Machine Learning for Investment Strategy

Knowledge discovery is an important aspect of human cognition. The advantage of the visual approach is in opportunity to substitute some complex cognitive tasks by easier perceptual tasks. However for cognitive tasks such as financial investment decision making this opportunity faces the challenge that financial data are abstract multidimensional and multivariate, i.e., outside of traditional visual perception in 2D or 3D world. This paper presents an approach to find an investment strategy based on pattern discovery in multidimensional space of specifically prepared time series. Visualization based on the lossless Collocated Paired Coordinates (CPC) plays an important role in this approach for building the criteria in the multidimensional space for finding an efficient investment strategy. Criteria generated with the CPC approach allow reducing/compressing space using simple directed graphs with beginnings and the ends located in different time points. The dedicated subspaces constructed for time series include characteristics such as Bollinger Band, difference between moving averages, changes in volume etc. Extensive simulation studies have been performed in learning/testing context. Effective relations were found for one-hour EURUSD pair for recent and historical data. Also the method has been explored for one-day EURUSD time series n 2D and 3D visualization spaces. The main positive result is finding the effective split of a normalized 3D space on 4x4x4 cubes in the visualization space that leads to a profitable investment decision (long, short position or nothing). The strategy is ready for implementation in algotrading mode

MetricsVis: A Visual Analytics Tool for Evaluating Multidimensional Data

Visualization for multidimensional data is a popular topic and many methods have been created to visualize this type of data. We developed a visual analytics tool to visualize multidimensional data for two distinct fields: resource allocation in law enforcement departments and phenotype traits of sorghum crops. For law enforcement departments, we designed a visualization tool to measure and compare police officer’s experience in different types of crimes. Our tool supports the analysis of the amount of experience each officer has in each crime category. Meanwhile, the field crop modeling project requires the visualization of the measured value of multiple traits of each sorghum category. In general, our visualization tool is now able to represent these multidimensional data in multiple graphs and charts, with a rich interaction set of selecting, grouping, and filtering. MetricsVis has been expanded this summer with the addition of 6 new graphs, the ability to use the sorghum crops dataset, and more data manipulation features. By being able to explore the data through several graphs and charts at the same time, this allows the user to easily query the data or find peculiarities in the data that they would have otherwise missed. We describe several case studies to validate the importance of our tool in analyzing the data in both projects. In the future, we would like to expand our tool for other similar datasets

IMAGE-IN: Interactive web-based multidimensional 3D visualizer for multi-modal microscopy images

Advances in microscopy hardware and storage capabilities lead to increasingly larger multidimensional datasets. The multiple dimensions are commonly associated with space, time, and color channels. Since “seeing is believing”, it is important to have easy access to user-friendly visualization software. Here we present IMAGE-IN, an interactive web-based multidimensional (N-D) viewer designed specifically for confocal laser scanning microscopy (CLSM) and focused ion beam scanning electron microscopy (FIB-SEM) data, with the goal of assisting biologists in their visualization and analysis tasks and promoting digital work-flows. This new visualization platform includes intuitive multidimensional opacity fine-tuning, shading on/off, multiple blending modes for volume viewers, and the ability to handle multichannel volumetric data in volume and surface views. The software accepts a sequence of image files or stacked 3D images as input and offers a variety of viewing options ranging from 3D volume/surface rendering to multiplanar reconstruction approaches. We evaluate the performance by comparing the loading and rendering timings of a heterogeneous dataset of multichannel CLSM and FIB-SEM images on two devices with installed graphic cards, as well as comparing rendered image quality between ClearVolume (the ImageJ open-source desktop viewer), Napari (the Python desktop viewer), Imaris (the closed-source desktop viewer), and our proposed IMAGE-IN web viewer

OWLAP - using OLAP approach in anomaly detection

OWLAP (Operative Workbench for Large-scale Analytics and Presentation) is a visual analytics tool that allows the user to browse and drill down the multidimensional data on-line with the possibility to export result into a zooming presentation framework. We address the challenges of multidimensional visualization by aiding the cognitively hard task of understanding attributes, finding patterns and outliers. We successfully solved the challenge of real time Big Data OLAP reporting by a home developed multithreaded inmemory database manager. Our additional focus is the automatic management of summary preparation that we aid by scripting the presentation framework of Prezi Inc