Generalised Pose Estimation Using Depth

Estimating the pose of an object, be it articulated, deformable or rigid, is an important task, with applications ranging from Human-Computer Interaction to environmental understanding. The idea of a general pose estimation framework, capable of being rapidly retrained to suit a variety of tasks, is appealing. In this paper a solution isproposed requiring only a set of labelled training images in order to be applied to many pose estimation tasks. This is achieved bytreating pose estimation as a classification problem, with particle filtering used to provide non-discretised estimates. Depth information extracted from a calibrated stereo sequence, is used for background suppression and object scale estimation. The appearance and shape channels are then transformed to Local Binary Pattern histograms, and pose classification is performed via a randomised decision forest. To demonstrate flexibility, the approach is applied to two different situations, articulated hand pose and rigid head orientation, achieving 97% and 84% accurate estimation rates, respectively

Getting inside acupuncture trials - Exploring intervention theory and rationale

<p>Abstract</p> <p>Background</p> <p>Acupuncture can be described as a complex intervention. In reports of clinical trials the mechanism of acupuncture (that is, the process by which change is effected) is often left unstated or not known. This is problematic in assisting understanding of how acupuncture might work and in drawing together evidence on the potential benefits of acupuncture. Our aim was to aid the identification of the assumed mechanisms underlying the acupuncture interventions in clinical trials by developing an analytical framework to differentiate two contrasting approaches to acupuncture (traditional acupuncture and Western medical acupuncture).</p> <p>Methods</p> <p>Based on the principles of realist review, an analytical framework to differentiate these two contrasting approaches was developed. In order to see how useful the framework was in uncovering the theoretical rationale, it was applied to a set of trials of acupuncture for fatigue and vasomotor symptoms, identified from a wider literature review of acupuncture and early stage breast cancer.</p> <p>Results</p> <p>When examined for the degree to which a study demonstrated adherence to a theoretical model, two of the fourteen selected studies could be considered TA, five MA, with the remaining seven not fitting into any recognisable model. When examined by symptom, five of the nine vasomotor studies, all from one group of researchers, are arguably in the MA category, and two a TA model; in contrast, none of the five fatigue studies could be classed as either MA or TA and all studies had a weak rationale for the chosen treatment for fatigue.</p> <p>Conclusion</p> <p>Our application of the framework to the selected studies suggests that it is a useful tool to help uncover the therapeutic rationale of acupuncture interventions in clinical trials, for distinguishing between TA and MA approaches and for exploring issues of model validity. English language acupuncture trials frequently fail to report enough detail relating to the intervention. We advocate using this framework to aid reporting, along with further testing and refinement of the framework.</p

Robust Human Pose Tracking For Realistic Service Robot Applications

Robust human pose estimation and tracking plays an integral role in assistive service robot applications, as it provides information regarding the body pose and motion of the user in a scene. Even though current solutions provide high-accuracy results in controlled environments, they fail to successfully deal with problems encountered under real-life situations such as tracking initialization and failure, body part intersection, large object handling and partial-view body-part tracking. This paper presents a framework tailored for deployment under real-life situations addressing the above limitations. The framework is based on the articulated 3D-SDF data representation model, and has been extended with complementary mechanisms for addressing the above challenges. Extensive evaluation on public datasets demonstrates the framework's state-of-the-art performance, while experimental results on a challenging realistic human motion dataset exhibit its robustness in real life scenarios

Fine-grained Material Classification using Micro-geometry and Reflectance

In this paper we focus on an understudied computer vision problem, particularly how the micro-geometry and the reflectance of a surface can be used to infer its material. To this end, we introduce a new, publicly available database for fine-grained material classification, consisting of over 2000 surfaces of fabrics (http://​ibug.​doc.​ic.​ac.​uk/​resources/​fabrics.). The database has been collected using a custom-made portable but cheap and easy to assemble photometric stereo sensor. We use the normal map and the albedo of each surface to recognize its material via the use of handcrafted and learned features and various feature encodings. We also perform garment classification using the same approach. We show that the fusion of normals and albedo information outperforms standard methods which rely only on the use of texture information. Our methodologies, both for data collection, as well as for material classification can be applied easily to many real-word scenarios including design of new robots able to sense materials and industrial inspection

A face and gesture recognition system based on an active stereo sensor

The paper presents several novel 3D image analysis algorithms, applied towards the segmentation and modeling of faces and hands. These are subsequently used to build a face-based authentication system and a system for humancomputer interaction based on static and dynamic gestures. The system relies on an active stereo sensor that uses a structured light approach to obtain 3D information. In this paper we demonstrate how the use of 3D information may significantly improve the efficiency of traditional face and gesture recognition techniques that use 2D images only. 1