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

National and international freight transport models: overview and ideas for further development

This paper contains a review of the literature on freight transport models, focussing on the types of models that have been developed since the nineties for forecasting, policy simulation and project evaluation at the national and international level. Models for production, attraction, distribution, modal split and assignment are discussed in the paper. Furthermore, the paper also includes a number of ideas for future development, especially for the regional and urban components within national freight transport models

Scoping analytical usability evaluation methods: A case study

Analytical usability evaluation methods (UEMs) can complement empirical evaluation of systems: for example, they can often be used earlier in design and can provide accounts of why users might experience difficulties, as well as what those difficulties are. However, their properties and value are only partially understood. One way to improve our understanding is by detailed comparisons using a single interface or system as a target for evaluation, but we need to look deeper than simple problem counts: we need to consider what kinds of accounts each UEM offers, and why. Here, we report on a detailed comparison of eight analytical UEMs. These eight methods were applied to it robotic arm interface, and the findings were systematically compared against video data of the arm ill use. The usability issues that were identified could be grouped into five categories: system design, user misconceptions, conceptual fit between user and system, physical issues, and contextual ones. Other possible categories such as User experience did not emerge in this particular study. With the exception of Heuristic Evaluation, which supported a range of insights, each analytical method was found to focus attention on just one or two categories of issues. Two of the three "home-grown" methods (Evaluating Multimodal Usability and Concept-based Analysis of Surface and Structural Misfits) were found to occupy particular niches in the space, whereas the third (Programmable User Modeling) did not. This approach has identified commonalities and contrasts between methods and provided accounts of why a particular method yielded the insights it did. Rather than considering measures such as problem count or thoroughness, this approach has yielded insights into the scope of each method