Affordances and Feedback in Nuance-Oriented Interfaces

Virtual Environments (VEs) and perceptive user interfaces must deal with complex users and their modes of interaction. One way to approach this problem is to recognize users’ nuances (subtle conscious or unconscious actions). In exploring nuance-oriented interfaces, we attempted to let users work as they preferred without being biased by feedback or affordances in the system. The hope was that we would discover the users’ innate models of interaction. The results of two user studies were that users are guided not by any innate model but by affordances and feedback in the interface. So, without this guidance, even the most obvious and useful components of an interface will be ignored

Data visualization within urban models

Models of urban environments have many uses for town planning, pre-visualization of new building work and utility service planning. Many of these models are three-dimensional, and increasingly there is a move towards real-time presentation of such large models. In this paper we present an algorithm for generating consistent 3D models from a combination of data sources, including Ordnance Survey ground plans, aerial photography and laser height data. Although there have been several demonstrations of automatic generation of building models from 2D vector map data, in this paper we present a very robust solution that generates models that are suitable for real-time presentation. We then demonstrate a novel pollution visualization that uses these models

Robot eye-hand coordination learning by watching human demonstrations: a task function approximation approach

We present a robot eye-hand coordination learning method that can directly learn visual task specification by watching human demonstrations. Task specification is represented as a task function, which is learned using inverse reinforcement learning(IRL) by inferring differential rewards between state changes. The learned task function is then used as continuous feedbacks in an uncalibrated visual servoing(UVS) controller designed for the execution phase. Our proposed method can directly learn from raw videos, which removes the need for hand-engineered task specification. It can also provide task interpretability by directly approximating the task function. Besides, benefiting from the use of a traditional UVS controller, our training process is efficient and the learned policy is independent from a particular robot platform. Various experiments were designed to show that, for a certain DOF task, our method can adapt to task/environment variances in target positions, backgrounds, illuminations, and occlusions without prior retraining.Comment: Accepted in ICRA 201

Airborne photogrammetry and LIDAR for DSM extraction and 3D change detection over an urban area : a comparative study

A digital surface model (DSM) extracted from stereoscopic aerial images, acquired in March 2000, is compared with a DSM derived from airborne light detection and ranging (lidar) data collected in July 2009. Three densely built-up study areas in the city centre of Ghent, Belgium, are selected, each covering approximately 0.4 km(2). The surface models, generated from the two different 3D acquisition methods, are compared qualitatively and quantitatively as to what extent they are suitable in modelling an urban environment, in particular for the 3D reconstruction of buildings. Then the data sets, which are acquired at two different epochs t(1) and t(2), are investigated as to what extent 3D (building) changes can be detected and modelled over the time interval. A difference model, generated by pixel-wise subtracting of both DSMs, indicates changes in elevation. Filters are proposed to differentiate 'real' building changes from false alarms provoked by model noise, outliers, vegetation, etc. A final 3D building change model maps all destructed and newly constructed buildings within the time interval t(2) - t(1). Based on the change model, the surface and volume of the building changes can be quantified