We take a new, scenario-based look at evaluation in information visualization. Our seven scenarios, evaluating visual data analysis and reasoning, evaluating user performance, evaluating user experience, evaluating environments and work practices, evaluating communication through visualization, evaluating visualization algorithms, and evaluating collaborative data analysis were derived through an extensive literature review of over 800 visualization publications. These scenarios distinguish different study goals and types of research questions and are illustrated through example studies. Through this broad survey and the distillation of these scenarios, we make two contributions. One, we encapsulate the current practices in the information visualization research community and, two, we provide a different approach to reaching decisions about what might be the most effective evaluation of a given information visualization. Scenarios can be used to choose appropriate research questions and goals and the provided examples can be consulted for guidance on how to design one's own study.

/pdf/empirical-studies-in-information-visualization-seven-3li5yz6tt6.pdf

Empirical Studies in Information Visualization: Seven Scenarios

Massive simulations and arrays of sensing devices, in combination with increasing computing resources, have generated large, complex, high-dimensional datasets used to study phenomena across numerous fields of study. Visualization plays an important role in exploring such datasets. We provide a comprehensive survey of advances in high-dimensional data visualization that focuses on the past decade. We aim at providing guidance for data practitioners to navigate through a modular view of the recent advances, inspiring the creation of new visualizations along the enriched visualization pipeline, and identifying future opportunities for visualization research.

/pdf/visualizing-high-dimensional-data-advances-in-the-past-4rinck46n7.pdf

Visualizing High-Dimensional Data: Advances in the Past Decade

This paper presents a novel convolutional neural network (CNN) -based method for high-accuracy real-time car license plate detection. Many contemporary methods for car license plate detection are reasonably effective under the specific conditions or strong assumptions only. However, they exhibit poor performance when the assessed car license plate images have a degree of rotation, as a result of manual capture by traffic police or deviation of the camera. Therefore, we propose the a CNN-based MD-YOLO framework for multi-directional car license plate detection. Using accurate rotation angle prediction and a fast intersection-over-union evaluation strategy, our proposed method can elegantly manage rotational problems in real-time scenarios. A series of experiments have been carried out to establish that the proposed method outperforms over other existing state-of-the-art methods in terms of better accuracy and lower computational cost.

A New CNN-Based Method for Multi-Directional Car License Plate Detection

The use of multivariate information visualization techniques is intrinsically difficult because the multidimensional nature of data cannot be effectively presented and understood on real-world displays, which have limited dimensionalities. However, the necessity to use these techniques in daily life is increasing as the amount and complexity of data grows explosively in the information age. Thus, multivariate information visualization techniques that are easier to understand and more accessible are needed for the general population. In order to meet this need, the present paper proposes Dust & Magnet, a multivariate information visualization technique using a magnet metaphor and various interactive techniques. The intuitive magnet metaphor and subsequent interactions facilitate the ease of learning this multivariate information visualization technique. A visualization tool such as Dust & Magnet has the potential to increase the acceptance of and utility for multivariate information by a broader population of users who are not necessarily knowledgeable about multivariate information visualization techniques.

https://www.cc.gatech.edu/~john.stasko/papers/iv05-dnm.pdf

Dust & magnet: multivariate information visualization using a magnet metaphor

Parallel coordinates have been widely applied to visualize high-dimensional and multivariate data, discerning patterns within the data through visual clustering. However, the effectiveness of this technique on large data is reduced by edge clutter. In this paper, we present a novel framework to reduce edge clutter, consequently improving the effectiveness of visual clustering. We exploit curved edges and optimize the arrangement of these curved edges by minimizing their curvature and maximizing the parallelism of adjacent edges. The overall visual clustering is improved by adjusting the shape of the edges while keeping their relative order. The experiments on several representative datasets demonstrate the effectiveness of our approach.

/pdf/visual-clustering-in-parallel-coordinates-317swkxutt.pdf

Visual clustering in parallel coordinates

The one-to-one strategy of mapping each single data item into a graphical marker adopted in many visualization techniques has limited usefulness when the number of records and/or the dimensionality of the data set are very high. In this situation, the strong overlapping of graphical markers severely hampers the user's ability to identify patterns in the data from its visual representation. We tackle this problem here with a strategy that computes frequency or density information from the data set, and uses such information in parallel coordinates visualizations to filter out the information to be presented to the user, thus reducing visual clutter and allowing the analyst to observe relevant patterns in the data. The algorithms to construct such visualizations, and the interaction mechanisms supported, inspired by traditional image processing techniques such as grayscale manipulation and thresholding are also presented. We also illustrate how such algorithms can assist users to effectively identify clusters in very noisy large data sets

/pdf/uncovering-clusters-in-crowded-parallel-coordinates-4i8jqm8uxa.pdf

Uncovering Clusters in Crowded Parallel Coordinates Visualizations

We propose a multidimensional visualization technique, named Viz3D, that creates a 3D representation of n-dimensional data that may be interactively manipulated by users to handle visual cluttering and object occlusion. The projection performed in Viz3D is comparable in quality with the 3D projections obtained with well-known dimensionality reduction techniques, at a lower complexity cost. While a 3D projection conveys more information, giving the user more control of the visual representation and an additional dimension, as compared to 2D, visual cluttering and object occlusion are still a problem in handling large multidimensional data sets. To produce more effective visualizations, two strategies are introduced. Dimensionality is handled with a similarity clustering of attributes prior to projection. Data set size is handled with a new strategy of visualizing data densities, rather than individual data records. Both the direct and density Viz3D visualizations provide the basis for a user driven visual clustering approach applicable to high-dimensional data sets that is very simple, intuitive and effective.

Viz3D: effective exploratory visualization of large multidimensional data sets

Researchers and users are well aware of the difficulties related to finding an appropriate configuration of the axes mapping attributes in multidimensional visualization techniques, particularly in visualizations that show a large number of attributes simultaneously. We address this problem with a simple strategy that offers both dimension ordering and dimension reduction. Dimension ordering is based on attribute similarity heuristics, and the basic rationale is extended to support dimension reduction. We discuss the performance of our algorithms and present some results of their application to several data sets. The algorithms improve the capability of visualization techniques to segregate clusters present in the data and reduce the visual clutter aggravated by arbitrary distributions of the axes.

Enhanced High Dimensional Data Visualization through Dimension Reduction and Attribute Arrangement

The aim of this paper is to present results achieved with a 3D terrestrial calibration field, designed for calibrating digital cameras and omnidirectional sensors. This terrestrial calibration field is composed of 139 ARUCO coded targets. Some experiments were performed using a Nikon D3100 digital camera with 8mm Samyang Bower fisheye lens. The camera was calibrated in this terrestrial test field using a conventional bundle adjustment with the Collinearity and mathematical models specially designed for fisheye lenses. The CMC software (Calibration with Multiple Cameras), developed in-house, was used for the calibration trials. This software was modified to use fisheye models to which the Conrady-Brown distortion equations were added. The target identification and image measurements of its four corners were performed automatically with a public software. Several experiments were performed with 16 images and the results were presented and compared. Besides the calibration of fish-eye cameras, the field was designed for calibration of a catadrioptic system and brief informations on the calibration of this unit will be provided in the paper.

/pdf/calibration-of-panoramic-cameras-with-coded-targets-and-a-3d-1a1ybifssv.pdf

Calibration of panoramic cameras with coded targets and a 3d calibration field

This paper presents a new approach for the automatic license plate recognition, which includes the SIFT algorithm in step to locate the plate in the input image. In this new approach, besides the comparison of the features obtained with the SIFT algorithm, the correspondence between the spatial orientations and the positioning associated with the keypoints is also observed. Afterwards, an algorithm is used for the character recognition of the plates, very fast, which makes it possible its application in real time. The results obtained with the proposed approach presented very good success rates, so much for locating the characters in the input image, as for their recognition.

/pdf/alprs-a-new-approach-for-license-plate-recognition-using-the-3kizcuehvb.pdf

Almir Olivette Artero

Papers

Uncovering Clusters in Crowded Parallel Coordinates Visualizations

Viz3D: effective exploratory visualization of large multidimensional data sets

Enhanced High Dimensional Data Visualization through Dimension Reduction and Attribute Arrangement

Calibration of panoramic cameras with coded targets and a 3d calibration field

ALPRS - A New Approach for License Plate Recognition using the Sift Algorithm