76,671 research outputs found
Vision systems with the human in the loop
The emerging cognitive vision paradigm deals with vision systems that apply machine learning and automatic reasoning in order to learn from what they perceive. Cognitive vision systems can rate the relevance and consistency of newly acquired knowledge, they can adapt to their environment and thus will exhibit high robustness. This contribution presents vision systems that aim at flexibility and robustness. One is tailored for content-based image retrieval, the others are cognitive vision systems that constitute prototypes of visual active memories which evaluate, gather, and integrate contextual knowledge for visual analysis. All three systems are designed to interact with human users. After we will have discussed adaptive content-based image retrieval and object and action recognition in an office environment, the issue of assessing cognitive systems will be raised. Experiences from psychologically evaluated human-machine interactions will be reported and the promising potential of psychologically-based usability experiments will be stressed
Visual Integration of Data and Model Space in Ensemble Learning
Ensembles of classifier models typically deliver superior performance and can
outperform single classifier models given a dataset and classification task at
hand. However, the gain in performance comes together with the lack in
comprehensibility, posing a challenge to understand how each model affects the
classification outputs and where the errors come from. We propose a tight
visual integration of the data and the model space for exploring and combining
classifier models. We introduce a workflow that builds upon the visual
integration and enables the effective exploration of classification outputs and
models. We then present a use case in which we start with an ensemble
automatically selected by a standard ensemble selection algorithm, and show how
we can manipulate models and alternative combinations.Comment: 8 pages, 7 picture
Usability testing for improving interactive geovisualization techniques
Usability describes a productâs fitness for use according to a set of predefined criteria.
Whatever the aim of the product, it should facilitate usersâ tasks or enhance their performance
by providing appropriate analysis tools. In both cases, the main interest is to satisfy users in
terms of providing relevant functionality which they find fit for purpose. âTesting usability
means making sure that people can find and work with [a productâs] functions to meet their
needsâ (Dumas and Redish, 1999: 4). It is therefore concerned with establishing whether
people can use a product to complete their tasks with ease and at the same time help them
complete their jobs more effectively.
This document describes the findings of a usability study carried out on DecisionSite Map
Interaction Services (Map IS). DecisionSite, a product of Spotfire, Inc.,1 is an interactive
system for the visual and dynamic exploration of data designed for supporting decisionmaking.
The system was coupled to ArcExplorer (forming DecisionSite Map IS) to provide
limited GIS functionality (simple user interface, basic tools, and data management) and
support users of spatial data. Hence, this study set out to test the suitability of the coupling
between the two software components (DecisionSite and ArcExplorer) for the purpose of
exploring spatial data. The first section briefly discusses DecisionSiteâs visualization
functionality. The second section describes the test goals, its design, the participants and data
used. The following section concentrates on the analysis of results, while the final section
discusses future areas of research and possible development
Virtual assembly rapid prototyping of near net shapes
Virtual reality (VR) provides another dimension to many engineering applications. Its immersive and interactive nature allows an intuitive approach to study both cognitive activities and performance evaluation. Market competitiveness means having products meet form, fit and function quickly. Rapid Prototyping and Manufacturing (RP&M) technologies are increasingly being applied to produce functional prototypes and the direct manufacturing of small components. Despite its flexibility, these systems have common drawbacks such as slow build rates, a limited number of build axes (typically one) and the need for post processing. This paper presents a Virtual Assembly Rapid Prototyping (VARP) project which involves evaluating cognitive activities in assembly tasks based on the adoption of immersive virtual reality along with a novel non-layered rapid prototyping for near net shape (NNS) manufacturing of components. It is envisaged that this integrated project will facilitate a better understanding of design for manufacture and assembly by utilising equivalent scale digital and physical prototyping in one rapid prototyping system. The state of the art of the VARP project is also presented in this paper
- âŚ