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We present a generative model and inference algorithm for 3D nonrigid object tracking. The model, which we call G-flow, enables the joint inference of 3D position, orientation, and nonrigid deformations, as well as object texture and background texture. Optimal inference under G-flow reduces to a conditionally Gaussian stochastic filtering problem. The optimal solution to this problem reveals a new...
This paper explores the process of self-guided learning of realistic facial expression production by a robotic head with 31 degrees of freedom. Facial motor parameters were learned using feedback from real-time facial expression recognition from video. The experiments show that the mapping of servos to expressions was learned in under one-hour of training time. We discuss how our work may help illuminate...
Recent years have seen the development of fast and accurate algorithms for detecting objects in images. However, as the size of the scene grows, so do the running-times of these algorithms. If a 128times102 pixel image requires 20 ms to process, searching for objects in a 1280times1024 image will take 2 s. This is unsuitable under real-time operating constraints: by the time a frame has been processed,...
We present a discriminative approach to frame-by-frame head pose tracking that is robust to a wide range of illuminations and facial appearances and that is inherently immune to accuracy drift. Most previous research on head pose tracking has been validated on test datasets spanning only a small (< 20) subjects under controlled illumination conditions on continuous video sequences. In contrast,...
We present a fully automatic approach to learning the personal facial attractiveness preferences of individual users directly from example images. The target application is computer assisted search of partners in online dating services. The proposed approach is based on the use of epsiv-SVMs to learn a regression function that maps low level image features onto attractiveness ratings. We present empirical...
In this paper, we present a barebones robot which is capable of interacting with humans based on social contingency. It expands the previous work of a contingency detector into having both human-model updating (developmental capability) and policy improvement (learning capability) based on the framework of Infomax control. The proposed new controller interacts with humans in both active and responsive...
Modeling eye-movements during search is important for building intelligent robotic vision systems, and for understanding how humans select relevant information and structure behavior in real time. Previous models of visual search (VS) rely on the idea of ldquosaliency mapsrdquo which indicate likely locations for targets of interest. In these models the eyes move to locations with maximum saliency...
Recent years have seen an explosion of research on the computational modeling of human visual attention in task free conditions, i.e., given an image predict where humans are likely to look. This area of research could potentially provide general purpose mechanisms for robots to orient their cameras. One difficulty is that most current models of visual saliency are computationally very expensive and...
Empirical evidence shows that infants 10 months of age can learn about 10 times faster than infants 2 months of age that a novel entity is socially contingent. This suggests that during the period from 2 to 10 months of age infants became better learners. One possible explanation for this change is that new brain structures grow, in a genetically predetermined manner, that support more efficient learning...
We have been conducting HRI (human-robot interaction) studies with the basic principle of design by immersion, which suggests the importance of researchers moving themselves into unconstrained daily-life environments. This is crucial for design and development of social robots that interact and assist people in the daily real-world. In this paper we report findings on a study where a small humanoid...
This paper introduces the early stages of a study designed to understand the development of dance interactions between QRIO and toddlers in a classroom environment. The study is part of a project to explore the potential use of interactive robots as instructional tools in education. After 3 months observation period, we are starting the experiment. After explaining the experimental environment, component...
Computers are already powerful enough to sustain useful robots that interact and assist humans in every-day life. However progress requires a scientific shakedown in goals and methods not unlike the cognitive revolution that occurred 40 years ago. The document presents the origin and early steps of the RUBI/QRIO project, in which two humanoid robots, RUBI and QRIO, are being brought to an early childhood...
We present a model of behavior according to which organisms react to the environment in a manner that maximizes the information gained about events of interest. We call the approach "Infomax control" for it combines the theory of optimal control with information maximization models of perception. The approach is reactive, not cognitive, in that it is better described as a continuous "dance"...
This paper presents a systematic analysis of Gabor filter banks for detection of facial landmarks (pupils and philtrum). Sensitivity is assessed using the A' statistic, a non-parametric estimate of sensitivity independent of bias commonly used in the psychophysical literature. We find that current Gabor filter bank systems are overly complex. Performance can be greatly improved by reducing the number...
Presents work on a project for the automatic recognition of spontaneous facial actions. Spontaneous facial expressions differ substantially from posed expressions, similar to how spontaneous speech differs from directed speech. Previous methods for automatic facial expression recognition assumed images were collected in controlled environments in which the subjects deliberately faced the camera. Since...
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