Category Archives: Prize

VAST Best Paper Award

Congratulations to Gary Tam and co-authors for their Best Paper Award at VAST 2016: An Analysis of Machine- and Human-Analytics in Classification, Gary K. L. Tam, Vivek Kothari, Min Chen, http://dx.doi.org/10.1109/TVCG.2016.2598829.

Abstract
machine-and-human-classification-analysisIn this work, we present a study that traces the technical and cognitive processes in two visual analytics applications to a common theoretic model of soft knowledge that may be added into a visual analytics process for constructing a decision-tree model. Both case studies involved the development of classification models based on the “bag of features” approach. Both compared a visual analytics approach using parallel coordinates with a machine-learning approach using information theory. Both found that the visual analytics approach had some advantages over the machine learning approach, especially when sparse datasets were used as the ground truth. We examine various possible factors that may have contributed to such advantages, and collect empirical evidence for supporting the observation and reasoning of these factors. We propose an information-theoretic model as a common theoretic basis to explain the phenomena exhibited in these two case studies. Together we provide interconnected empirical and theoretical evidence to support the usefulness of visual analytics.

Best of 2013 for Photon Parameterisation for Robust Relaxation Constraints

Best of 2013Photon Parameterisation for Robust Relaxation Constraints has been selected as a notable article in computing in 2013. Computing Reviews’ Best of 2013 list consists of book and article nominations from reviewers, CR category editors, the editors in chief of journals, and others in the computing community. The complete list is here. The paper also won best paper at Eurographics 2013.

Eurographics 2013 Best Paper Award

splitprismWe are pleased to announce our award of Best Paper at Eurographics 2013 for our paper Photon Parameterisation for Robust Relaxation Constraints, Ben Spencer and Mark W. Jones. [Link to paper]

The paper introduces a technique that augments each photon with information about its origin trajectory. Using this, lighting is separable during density estimation queries. Additionally, we spot fine edge detail using PCA allowing us to employ photon relaxation without detrimental effects. This results in high qualify photon maps that reduce variance and can be rendered with very low bandwidth kernels reducing bias. [Link to news on EG.org]