Visual exploration of multivariate data typically requires projection onto lower-dimensional representations. The number of possible representations grows rapidly with the number of dimensions, and manual exploration quickly becomes ineffective or even uoJeasible. This paper proposes automatic analysis methods to extract potentially relevant visual structures from a set of candidate visualizations. Based on features, the visualizations are ranked in accordance with a specified user task. The user is provided with a manageable number of potentially useful candidate visualizations, which can be u ed as a starting point for interactive data analysis. This can effectively t:ase the task of finding truly useful visualizations and potcntially speed up the data exploration task. In this paper, we present ranking measures for class-based as well as non class-based Scatterplots and Parallel Coordinates visualizations. The proposed analysis methods are evaluated on different datasets.

Visual exploration of multivariate data typically requires projection onto lower dimensional representations. The number of possible representations grows rapidly with the number of dimensions, and manual exploration quickly becomes ineffective or even unfeasible. This paper proposes automatic analysis methods to extract potentially relevant visual structures from a set of candidate visualizations. Based on features, the visualizations are ranked in accordance with a specified user task. The user is provided with a manageable number of potentially useful candidate visualizations, which can be used as a starting point for interactive data analysis. This can effectively ease the task of finding truly useful visualizations and potentially speed up the data exploration task. In this paper, we present ranking measures for class-based as well as non-class-based scatterplots and parallel coordinates visualizations. The proposed analysis methods are evaluated on different data sets.

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The ability to interpolate between images taken at different time and viewpoints directly in image space opens up new possiblities. The goal of our work is to create plausible in-between images in real time without the need for an intermediate 3D reconstruction. This enables us to also interpolate between images recorded with uncalibrated and unsynchronized cameras. In our approach we use a novel discontiniuity preserving image deformation model to robustly estimate dense correspondences based on local homographies. Once correspondences have been computed we are able to render plausible in-between images in real time while properly handling occlusions. We discuss the relation of our approach to human motion perception and other image interpolation techniques.

The scatterplot matrix (SPLOM) is a well-established

Abstract-Generation of synthetic datasets is a common practice in many research areas. Such data is often generated to meet specific needs or certain conditions that may not be easily found in the original, real data. The nature of the data varies according to the application area and includes text, graphs, social or weather data, among many others. The common process to create such synthetic datasets is to implement small scripts or programs, restricted to small problems or to a specific application. In this paper we propose a framework designed to generate high dimensional datasets. Users can interactively create and navigate through multi dimensional datasets using a suitable graphical user-interface. The data creation is driven by statistical distributions based on a few user-defined parameters. First, a grounding dataset is created according to given inputs, and then structures and trends are included in selected dimensions and orthogonal projection planes. Furthermore, our framework supports the creation of complex non-orthogonal trends and classified datasets. It can successfully be used to create synthetic datasets simulating important trends as multidimensional clusters, correlations and outliers.

This work aimed to determine the effect of citric and malic acids on the food safety, nutritional and physicochemical quality of sheep buchada. For this purpose, several microbiological (thermotolerant coliforms, coagulase positive Staphylococcus, and Salmonella sp.), nutritional (lipids, proteins, ashes) physicochemical (acidity, pH, water activity, and TBARS test), and sensorial analyses (acceptance test and purchase intent) were carried out. The sheep buchada with organic acids added, as well as the control samples, did not show any microbial growth during storage. The addition of organic acids in sheep buchada resulted in the reduction of the pH and moisture as well as a significant increase in the acidity of the samples, being these changes recognized as barriers to microbial growth. The addition of citric acid resulted in lower values in the TBARS test, which could attributed to its antioxidant action. Moreover, the addition of acids did not negatively interfere with the sensory attributes. Practical applications The use of edible by‐products from the slaughter of sheep in the formulation of blood sausage is viable because it uses low‐cost raw materials; furthermore, the utilization of these by‐products can generate income for producers, allowing them to offer a meat product of high nutritional and sensory quality.