A low-cost head and eye tracking system for realistic eye movements in virtual avatars

A virtual avatar or autonomous agent is a digital representation of a human being that can be controlled by either a human or an artificially intelligent computer system. Increasingly avatars are becoming realistic virtual human characters that exhibit human behavioral traits, body language and eye and head movements. As the interpretation of eye and head movements represents an important part of nonverbal human communication it is extremely important to accurately reproduce these movements in virtual avatars to avoid falling into the well-known ``uncanny valley''. In this paper we present a cheap hybrid real-time head and eye tracking system based on existing open source software and commonly available hardware. Our evaluation indicates that the system of head and eye tracking is stable and accurate and can allow a human user to robustly puppet a virtual avatar, potentially allowing us to train an A.I. system to learn realistic human head and eye movements

Sign Language Recognition

This chapter covers the key aspects of sign-language recognition (SLR), starting with a brief introduction to the motivations and requirements, followed by a précis of sign linguistics and their impact on the field. The types of data available and the relative merits are explored allowing examination of the features which can be extracted. Classifying the manual aspects of sign (similar to gestures) is then discussed from a tracking and non-tracking viewpoint before summarising some of the approaches to the non-manual aspects of sign languages. Methods for combining the sign classification results into full SLR are given showing the progression towards speech recognition techniques and the further adaptations required for the sign specific case. Finally the current frontiers are discussed and the recent research presented. This covers the task of continuous sign recognition, the work towards true signer independence, how to effectively combine the different modalities of sign, making use of the current linguistic research and adapting to larger more noisy data set

Head Pose Estimation Based on Random Forests with Binary Pattern Run Length Matrix

In this paper, a novel approach for head pose estimation in gray-level images is presented. In the proposed algorithm, there were two techniques employed. In order to deal with the large set of training data, the method of Random Forests was employed; this is a state-of-the-art classification algorithm in the field of computer vision. In order to make this system robust in terms of illumination, a Binary Pattern Run Length matrix was employed; this matrix combined a Local Binary Pattern and a Run Length matrix. Experimental results show that our algorithm is robust against illumination change. © 2014 Springer-Verlag Berlin Heidelberg

WNet: Joint multiple head detection and head pose estimation from a spectator crowd image

Crowd image analysis has various application areas such as surveillance, crowd management and augmented reality. Existing techniques can detect multiple faces in a single crowd image, but small head/face size and additional non facial regions in the head bounding box makes the head detection (HD) challenging. Additionally, in existing head pose estimations (HPE) of multiple heads in an image, individual cropped head image is passed through a network one by one, instead of estimating poses of multiple heads at the same time. The proposed WNet, performs both HD and HPE jointly on multiple heads in a single crowd image, in a single pass. Experiments are demonstrated on the spectator crowd S-HOCK dataset and results are compared with the HPE benchmarks. WNet proposes to use lesser number of training images compared to number of cropped images used by benchmarks, and does not utilize transferred weights from other networks. WNet not just performs HPE, but joint HD and HPE efficiently i.e. accuracy for more number of heads while depending on lesser number of testing images, compared to the benchmarks

Direct holmium laser-assisted balloon angioplasty in acute myocardial infarction

The holmium laser (Thulium-Holmium-Chromium: yttrium-aluminum garnet [YAG]laser) system was recently introduced for the treatment of atherosclerotic peripheral arterial and coronary artery lesions. 1–6 This pulsed mid-infrared laser (2.1 μm) ablates atherosclerotic tissue without significant thermal damage to adjacent tissue. 2,3,6,7 This study was designed to test the hypothesis that delivery characteristics of and tissue sensitivities to holmium laser energy make it a feasible treatment option in patients with acute myocardial infarction, with thrombosis and atherosclerotic plaque. In this report, we provide data on the first 3 cases of direct laserassisted balloon angioplasty during an acute myocardial infarction

Effectiveness of Holmium laser-assisted coronary angioplasty

The efficacy of Holmium laser-assisted angioplasty was studied in 365 narrowings in 331 consecutive patients with coronary artery disease. Clinical indications for study were unstable angina pectoris in 140 patients (42%), stable angina in 136 patients (41%), postmyocardial infarction angina in 35 patients (10.5%), silent myocardial ischemia in 11 patients (3%), acute myocardial infarction in 1 patient (0.3%) and undefined in 8 patients (2%). Coronary morphology characteristics by Multivessel Angioplasty Prognosis Study group criteria were type A in 12.6%, type B1 in 34.2%, type B2 in 27.4% and type C in 25.4%. The laser successfully crossed the total length of the narrowing in 85.2%. Procedural success was 94.2%. Laser alone reduced mean percent luminal narrowing from 88 ± 11% to 57 ± 22%. Subsequent balloon angioplasty further reduced the mean luminal narrowing to 23 ± 18%. Major complication rate was 2.7% (death 0.3%, Q-wave myocardial infarction 0.5%, and emergent bypass surgery 2.7%). Six-month angiographic restenosis (>50% stenosis) rate was 44%