Learning about End-User Development for Smart Homes by "Eating Our Own Dog Food"

SPOK is an End-User Development Environment that permits people to monitor, control, and configure smart home services and devices. SPOK has been deployed for more than 4 months in the homes of 5 project team members for testing and refinement, prior to longitudinal experiments in the homes of families not involved in the project. This article reports on the lessons learned in this initial deployment

Head Pose Estimation Using Multi-scale Gaussian Derivatives

International audienceIn this paper we approach the problem of head pose estimation by combining Multi-scale Gaussian Derivatives with Support Vector Machines. We evaluate the approach on the Pointing04 and CMU-PIE data sets and to estimate the pan and tilt of the head from facial images. We achieved a mean absolute error of 6.9 degrees for pan and 8.0 degrees for tilt on the Pointing04 data set

Multimodal Observation and Interpretation of Subjects Engaged in Problem Solving

In this paper we present the first results of a pilot experiment in the capture and interpretation of multimodal signals of human experts engaged in solving challenging chess problems. Our goal is to investigate the extent to which observations of eye-gaze, posture, emotion and other physiological signals can be used to model the cognitive state of subjects, and to explore the integration of multiple sensor modalities to improve the reliability of detection of human displays of awareness and emotion. We observed chess players engaged in problems of increasing difficulty while recording their behavior. Such recordings can be used to estimate a participant's awareness of the current situation and to predict ability to respond effectively to challenging situations. Results show that a multimodal approach is more accurate than a unimodal one. By combining body posture, visual attention and emotion, the multimodal approach can reach up to 93% of accuracy when determining player's chess expertise while unimodal approach reaches 86%. Finally this experiment validates the use of our equipment as a general and reproducible tool for the study of participants engaged in screen-based interaction and/or problem solving

Facial Expression Analysis and The PAD Space

International audienceIn this paper we present a technique for facial expression analysis and representing the underlying emotions in the PAD (Pleasure-Arousal-Dominance) space. We develop a purely appearance based approach using Multi-scale Gaussian derivatives and Support Vector Machines. The system can generalize well and is shown to outperform the baseline method

Placement optimal de caméras contraintes pour la synthèse de nouvelles vues

International audienceNous étudions le problème du placement optimal sous contraintes, de plusieurs caméras, pour la synthèse de nouvelles vues. Une telle configuration optimale est définie comme celle qui minimise l'incertitude de projection des pixels des caméras de prise de vue sur la vue à synthétiser. Le rendu de cette vue est souvent précédé d'une phase de reconstruction 3D approximative. Nous dérivons la matrice de covariance associée à l'incertitude sur la géométrie, puis nous propageons l'erreur sur le plan de la nouvelle vue. Nous observons l'influence de l'interoculaire et de la distance focale des caméras sur l'erreur projetée, pour des distributions de points aléatoires à diverses profondeurs

Depression Estimation Using Audiovisual Features and Fisher Vector Encoding

International audienceWe investigate the use of two visual descriptors: Local Bi-nary Patterns-Three Orthogonal Planes(LBP-TOP) and Dense Trajectories for depression assessment on the AVEC 2014 challenge dataset. We encode the visual information gen-erated by the two descriptors using Fisher Vector encod-ing which has been shown to be one of the best performing methods to encode visual data for image classification. We also incorporate audio features in the final system to intro-duce multiple input modalities. The results produced using Linear Support Vector regression outperform the baseline method[16]

BetaSAC: A New Conditional Sampling For RANSAC

International audienceWe present a new strategy for RANSAC sampling named BetaSAC, in reference to the beta distribution. Our proposed sampler builds a hypothesis set incrementally, select- ing data points conditional on the previous data selected for the set. Such a sampling is shown to provide more suitable samples in terms of inlier ratio but also of consistency and potential to lead to an accurate parameters estimation. The algorithm is presented as a general framework, easily implemented and able to exploit any kind of prior infor- mation on the potential of a sample. As with PROSAC, BetaSAC converges towards RANSAC in the worst case. The benefits of the method are demonstrated on the homog- raphy estimation problem

Facial expression analysis and the affect space

International audienceIn this paper we present a technique for facial expression analysis and representing the underlyingemotions in the affect space. We develop a purely appearance based approach using Multi!scale Gaussianderivatives and Support Vector Machines. The technique is validated on two different databases. The systemis shown to generalize well and performs better than the baseline method

Learning Situation Models in a Smart Home

International audienceThis article addresses the problem of learning situation models for providing context-aware services. Context for modeling human behavior in a smart envi- ronment is represented by a situation model describing environment, users and their activities. A framework for acquiring and evolving different layers of a situation model in a smart environment is proposed. Different learning methods are presented as part of this framework: role detection per entity, unsupervised extraction of situations from multimodal data, supervised learning of situation representations, and the evolution of a predefined situation model with feedback. The situation model serves as frame and support for the different methods, permitting to stay in an intuitive declarative framework. The proposed methods have been integrated into a whole system for smart home environment. The implementation is detailed and two evaluations are conducted in the smart home environment. The obtained results validate the proposed approach

Assessing Inheritance of Zircon and Monazite in Granitic Rocks from the Monashee Complex, Canadian Cordillera

Zircon and monazite from granitic sheets and dikes in the Monashee complex, Canadian Cordillera, were investigated to determine whether igneous crystallization occurred at 1.9 Ga or 50 Ma with 1.9 Ga inherited zircon and monazite. Four of the five samples are weakly deformed to undeformed, despite occurring in a gneiss dome at the structurally deepest exposed level of the orogen that elsewhere was strongly deformed and partly melted at 50 Ma. Based on U-(Th)-Pb dates from zircon and monazite, field relationships, and mineral composition and zoning, we conclude that the granitic rocks crystallized at 1.9 Ga and were metamorphosed at 50 Ma. All dated zircon is 1.9 Ga (except for 2.3-2.0 Ga inherited cores) and 1.9 Ga monazite comprises \u3e90% of the population in four samples. The remainder of the monazite is 50 Ma and all monazite in one sample is 50 Ma. Composition and zoning of 1.9 Ga zircon and monazite are uniform within samples, yet differ between samples, indicating growth from 1.9 Ga magmas that are unique to each sample. This relationship is unlikely if the grains are inherited because the host rocks are heterogeneous 2.3-2.1 Ga gneisses. The 1.9 Ga zircon and monazite have zoning that is consistent with growth from magmas, whereas the 50 Ma monazite has variable composition and zoning that suggest growth from diverse metamorphic fluids. The results demonstrate that part of the Monashee complex was last strongly deformed and partly melted at 1.9 Ga, and thus largely escaped Cordilleran tectonism