Set Of Visual Attributes Research Articles

PyVT: A python-based open-source software for visualization and graphic analysis of fluid dynamics datasets

We developed a Python-based open-source software for scientific visualization of fluid dynamics datasets. This software is enabled by the VTK graphic rendering library and equipped with an interactive user interface built upon PyQt5. The concept of “scene module” is proposed to construct the rendered scene for graphic analysis. In the context of fluid dynamics, scene module is defined by associating a manifested geometry, such as a cut plane, an iso-surface, a set of streamlines and a vector field, with a set of visual attributes, including rendering scalar, colormap, and opacity. In the proposed framework, each scene module is processed by one single VTK pipeline, and a number of modules can be rendered simultaneously for multivariate graphic analysis. Beyond the VTK built-in functionality, additional implementation efforts are taken to enhance the annotating capability, improve the display of coordinate axes, and load input data of various formats. Two case studies are presented to demonstrate the capability of PyVT, especially the visualization of multivariate fluid dynamic datasets that typically possess complex and interdependent flow physics.

MED-NODE: A computer-assisted melanoma diagnosis system using non-dermoscopic images

Melanoma is one of the most aggressive types of skin cancer and in many cases it is difficult to differentiate from benign naevi. In this contribution we present a decision support (expert) system, which we call MED-NODE, able to assist physicians with this challenging task. The proposed system makes use of non-dermoscopic digital images of lesions from which it automatically extracts the lesion regions and then computes descriptors regarding the color and texture. In addition, a set of visual attributes is provided by the examining physician. The automatically extracted descriptors and the attributes provided by the physician are separately used for automatic prediction. Final classification is achieved by a majority vote of all predictions. The proposed system achieves high diagnostic accuracy results (81%) and performs comparably to state-of-the-art methods that are using dermoscopic images, though such images contain more detailed information and are subject to less noise and illumination effects. The simple input requirements and the robustness of its descriptors allow MED-NODE to be an effective tool within the diagnostic process for melanoma. In addition, the modular nature of the system allows for it to be easily extended.

Reflections on the Pedagogy of Place in Planning and Urban Design

This article revisits the concept of place in planning and urban design and proposes a conceptual framework consisting of four ontological constructs of place as a set of visual attributes, product, process, and meaning. The article discusses the theoretical underpinnings of each concept and explores the advantages of a continuum between these terms to help bridge the gap between policy planning and physical design and to move toward a meaningful urban design. The study also examines the application of the proposed constructs of place to a sophomore-planning studio, focusing on the University of Cincinnati campus, which was offered at the School of Planning during the fall quarter of 2002.

A Strategy and Architecture for the Visualization of Complex Geographical Datasets

The use of computer visualization as a means to analyze complex geographic datasets is discussed. Visualization is a valuable tool for conducting exploratory data analysis on geographical data; making good use of the human eye's unparalleled ability to recognize structure and relationships that may be inherent within the data. Traditional GIS are extremely poor at visualization, being limited to a very restricted set of visual attributes with which to convey information (position, size, color). The use of a more sophisticated approach is discussed in detail. Specifically, a system to visualise complex environmental datasets is described, which makes use of knowledge concerning the problem domain as well as knowledge concerning human cognition. In the realizations produced, the most salient attributes in the data, for a particular task, are assigned to the most striking visual attributes. Assignments are controlled by heuristics that may be changed to alter system behavior. Results are presented showing the application of this approach on datasets involving several multi-dimensional thematic layers of environmental data, used in mineral exploration.