Gaze-Based Human-Robot Interaction by the Brunswick Model

We present a new paradigm for human-robot interaction based on social signal processing, and in particular on the Brunswick model. Originally, the Brunswick model copes with face-to-face dyadic interaction, assuming that the interactants are communicating through a continuous exchange of non verbal social signals, in addition to the spoken messages. Social signals have to be interpreted, thanks to a proper recognition phase that considers visual and audio information. The Brunswick model allows to quantitatively evaluate the quality of the interaction using statistical tools which measure how effective is the recognition phase. In this paper we cast this theory when one of the interactants is a robot; in this case, the recognition phase performed by the robot and the human have to be revised w.r.t. the original model. The model is applied to Berrick, a recent open-source low-cost robotic head platform, where the gazing is the social signal to be considered

EVALUATING SATELLITE DERIVED BATHYMETRY IN REGARD TO TOTAL PROPAGATED UNCERTAINTY, MULTI-TEMPORAL CHANGE DETECTION, AND MULTIPLE NON-LINEAR ESTIMATION

Acoustic and electromagnetic hydrographic surveys produce highly-accurate bathymetric data that can be used to update and improve current nautical charts. For shallow-water surveys (i.e., less than 50m depths), this includes the use of single-beam echo-sounders (SBES), multi-beam echo-sounders (MBES), and airborne lidar bathymetry (ALB). However, these types of hydrographic surveys are time-consuming and require considerable financial and operational resources to conduct. As a result, some maritime regions are seldom surveyed due to their remote location and challenging logistics. Satellite-derived bathymetry (SDB) provides a means to supplement traditional acoustic hydrographic surveys. In particular, Landsat 8 imagery: 1) provides complete coverage of the Earth’s surface every 16 days, 2) has an improved dynamic range (12-bits), and 3) is freely-available from the US Geological Survey. While the 30 m spatial resolution does not match MBES, ALB, or SBES coverage, SDB based on Landsat 8 can be regarded as a type of “reconnaissance survey” that can be used to identify potential hazards to navigation in areas that are seldom surveyed. It is also a useful means to monitor change detection in dynamic regions. This study focused on developing improved image-processing techniques and time-series analysis for SDB from Landsat 8 imagery for three different applications: 1. An improved means to estimate total propagated uncertainty (TPU), mainly the vertical component, for single-image SDB; 2. Identifying the location and movement of dynamic shallow areas in river entrances based on multiple-temporal Landsat 8 imagery; 3. Using a multiple, nonlinear SDB approach to enhance depth estimations and enable bottom discrimination. An improved TPU estimation was achieved based on the two most common optimization approaches (Dierssen et al., 2003 and Stumpf et al., 2003). Various single-image SDB band-ratio outcomes and associated uncertainties were compared against ground truth (i.e., recent Lidar surveys). Several parameters were tested, including various types of filters, kernel sizes, number of control points and their coverage, and recent vs. outdated control points. Based on the study results for two study sites (Cape Ann, MA and Ft Myers, FL), similar performance was observed for both the Stumpf and the Dierssen models. Validation was performed by comparing estimated depths and uncertainties to observed ALB data. The best performing configuration was achieved using low-pass filter (kernel size 3x3) with ALB control points that were distributed over the entire study site. A change detection process using image processing was developed to identify the location and movement of dynamic shallow areas in riverine environments. Yukon River (Alaska) and Amazon River (Brazil) entrances were evaluated as study sites using multiple satellite imagery. A time-series analysis was used to identify probable shallow areas with no usable control points. By using an SDB ratio model with image processing techniques that includes feature extraction and a well-defined topological feature to describe the shoal feature, it is possible to create a time-series of the shoal’s motion, and predict its future location. A further benefit of this approach is that vertical referencing of the SDB ratio model to chart datum is not required. In order to enhance the capabilities of the SDB approach to estimate depth in non-uniform conditions, Dierssen’s band ration SDB algorithm was transformed into a full non-linear SDB model. The model was evaluated in the Simeonof Island, AK, using Lidar control points from a previous NOAA ALB survey. Linear and non-linear SDB models were compared using the ALB survey for performance evaluation. The multi-nonlinear SDB model provides an enhanced performance compared to the more traditional linear SDB method. This is most noticeable in the very shallow waters (0-2 m), where a linear model does not provide a good correlation to the control points. In deep-waters close to the extinction depth, the multi-nonlinear SDB method is also able to better detect bottom features than the linear SDB method. By recognizing the water column contributions to the SDB solution, it is possible to achieve a more accurate estimate of the bathymetry in remote areas

Exploiting Spatio-Temporal Coherence for Video Object Detection in Robotics

This paper proposes a method to enhance video object detection for indoor environments in robotics. Concretely, it exploits knowledge about the camera motion between frames to propagate previously detected objects to successive frames. The proposal is rooted in the concepts of planar homography to propose regions of interest where to find objects, and recursive Bayesian filtering to integrate observations over time. The proposal is evaluated on six virtual, indoor environments, accounting for the detection of nine object classes over a total of ∼ 7k frames. Results show that our proposal improves the recall and the F1-score by a factor of 1.41 and 1.27, respectively, as well as it achieves a significant reduction of the object categorization entropy (58.8%) when compared to a two-stage video object detection method used as baseline, at the cost of small time overheads (120 ms) and precision loss (0.92).</p

Colour and Colorimetry Multidisciplinary Contributions Vol. XIb

It is well known that the subject of colour has an impact on a range of disciplines. Colour has been studied in depth for many centuries, and as well as contributing to theoretical and scientific knowledge, there have been significant developments in applied colour research, which has many implications for the wider socio-economic community. At the 7th Convention of Colorimetry in Parma, on the 1st October 2004, as an evolution of the previous SIOF Group of Colorimetry and Reflectoscopy founded in 1995, the "Gruppo del Colore" was established. The objective was to encourage multi and interdisciplinary collaboration and networking between people in Italy that addresses problems and issues on colour and illumination from a professional, cultural and scientific point of view. On the 16th of September 2011 in Rome, in occasion of the VII Color Conference, the members assembly decided to vote for the autonomy of the group. The autonomy of the Association has been achieved in early 2012. These are the proceedings of the English sessions of the XI Conferenza del Colore

Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations

The study of low-dimensional, noisy manifolds embedded in a higher dimensional space has been extremely useful in many applications, from the chemical analysis of multi-phase flows to simulations of galactic mergers. Building a probabilistic model of the manifolds has helped in describing their essential properties and how they vary in space. However, when the manifold is evolving through time, a joint spatio-temporal modelling is needed, in order to fully comprehend its nature. We propose a first-order Markovian process that propagates the spatial probabilistic model of a manifold at fixed time, to its adjacent temporal stages. The proposed methodology is demonstrated using a particle simulation of an interacting dwarf galaxy to describe the evolution of a cavity generated by a Supernov