Uncertainty in Hypothetical 3D Reconstructions: Technical, Visual and Cultural ‘Transitions’

Uncertainty assessment is fundamental when dealing with digital 3D reconstructions of hypothetical artefacts. In this framework, a range of uncertainty scales based on different classifications and visualisation techniques have been proposed through time without reaching a standard. Besides this, we argue that, even starting from a very simple uncertainty scale (which can also become more complex if needed) and assuming that it becomes widespread, a variety of challenges arise at different levels: at least a technical, a visual and a cultural one, which are here analysed describing the different kinds of 'transitions' that they can convey. At a technical level, the uncertainty scale can be applied to different levels of detail (allowing transitions between them), can be communicated through platforms (generating transitions of knowledge) and hopefully by means of (a transition to) standard exchange formats. At a visual level, a transition should be guaranteed between different uncertainty visualisation techniques, but also to infographics representing uncertainty data in more complex ways. At a cultural level, we should take into account that this transition of knowledge may occur in different domains and have different targets, in a balance between complexity and adaptation depending on the audience we refer to. We conclude with two goals for the future: the integration of the uncertainty documentation as a property in the CIDOC CRM ontology for cultural heritage and the visualisation of uncertainty directly on suitable online viewers

Visual motion processing and human tracking behavior

The accurate visual tracking of a moving object is a human fundamental skill that allows to reduce the relative slip and instability of the object's image on the retina, thus granting a stable, high-quality vision. In order to optimize tracking performance across time, a quick estimate of the object's global motion properties needs to be fed to the oculomotor system and dynamically updated. Concurrently, performance can be greatly improved in terms of latency and accuracy by taking into account predictive cues, especially under variable conditions of visibility and in presence of ambiguous retinal information. Here, we review several recent studies focusing on the integration of retinal and extra-retinal information for the control of human smooth pursuit.By dynamically probing the tracking performance with well established paradigms in the visual perception and oculomotor literature we provide the basis to test theoretical hypotheses within the framework of dynamic probabilistic inference. We will in particular present the applications of these results in light of state-of-the-art computer vision algorithms

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

Dendritic inhibition enhances neural coding properties.

The presence of a large number of inhibitory contacts at the soma and axon initial segment of cortical pyramidal cells has inspired a large and influential class of neural network model which use post-integration lateral inhibition as a mechanism for competition between nodes. However, inhibitory synapses also target the dendrites of pyramidal cells. The role of this dendritic inhibition in competition between neurons has not previously been addressed. We demonstrate, using a simple computational model, that such pre-integration lateral inhibition provides networks of neurons with useful representational and computational properties which are not provided by post-integration inhibition

Occupant behaviour in naturally ventilated and hybrid buildings

Adaptive thermal comfort criteria for building occupants are now becoming established. In this paper we illustrate their use in the prediction of occupant behaviour and make a comparison with a non-adaptive temperature threshold approach. A thermal comfort driven adaptive behavioural model for window opening is described and its use within dynamic simulation illustrated for a number of building types. Further development of the adaptive behavioural model is suggested including use of windows, doors, ceiling fans, night cooling, air conditioning and heating, also the setting of opportunities and constraints appropriate to a particular situation. The integration in dynamic simulation of the thermal adaptive behaviours together with non-thermally driven behaviours such as occupancy, lights and blind use is proposed in order to create a more complete model of occupant behaviour. It is further proposed that this behavioural model is implemented in a methodology that includes other uncertainties (e.g. in internal gains) so that a realistic range of occupant behaviours is represented at the design stage to assist in the design of robust, comfortable and low energy buildings

Neural Sensor Fusion for Spatial Visualization on a Mobile Robot

An ARTMAP neural network is used to integrate visual information and ultrasonic sensory information on a B 14 mobile robot. Training samples for the neural network are acquired without human intervention. Sensory snapshots are retrospectively associated with the distance to the wall, provided by on~ board odomctry as the robot travels in a straight line. The goal is to produce a more accurate measure of distance than is provided by the raw sensors. The neural network effectively combines sensory sources both within and between modalities. The improved distance percept is used to produce occupancy grid visualizations of the robot's environment. The maps produced point to specific problems of raw sensory information processing and demonstrate the benefits of using a neural network system for sensor fusion.Office of Naval Research and Naval Research Laboratory (00014-96-1-0772, 00014-95-1-0409, 00014-95-0657