Improved depth recovery in consumer depth cameras via disparity space fusion within cross-spectral stereo.

We address the issue of improving depth coverage in consumer depth cameras based on the combined use of cross-spectral stereo and near infra-red structured light sensing. Specifically we show that fusion of disparity over these modalities, within the disparity space image, prior to disparity optimization facilitates the recovery of scene depth information in regions where structured light sensing fails. We show that this joint approach, leveraging disparity information from both structured light and cross-spectral sensing, facilitates the joint recovery of global scene depth comprising both texture-less object depth, where conventional stereo otherwise fails, and highly reflective object depth, where structured light (and similar) active sensing commonly fails. The proposed solution is illustrated using dense gradient feature matching and shown to outperform prior approaches that use late-stage fused cross-spectral stereo depth as a facet of improved sensing for consumer depth cameras

Improved Depth Recovery In Consumer Depth Cameras via Disparity Space Fusion within Cross-spectral Stereo

We address the issue of improving depth coverage in consumer depth cameras based on the combined use of cross-spectral stereo and near infra-red structured light sensing. Specifically we show that fusion of disparity over these modalities, within the disparity space image, prior to disparity optimization facilitates the recovery of scene depth information in regions where structured light sensing fails. We show that this joint approach, leveraging disparity information from both structured light and cross-spectral sensing, facilitates the joint recovery of global scene depth comprising both texture-less object depth, where conventional stereo otherwise fails, and highly reflective object depth, where structured light (and similar) active sensing commonly fails. The proposed solution is illustrated using dense gradient feature matching and shown to outperform prior approaches that use late-stage fused cross-spectral stereo depth as a facet of improved sensing for consumer depth cameras

Exploiting the Brønsted acidity of phosphinecarboxamides for the synthesis of new phosphides and phosphines

International audienc

Real-time Classification of Vehicle Types within Infra-red Imagery

Real-time classification of vehicles into sub-category types poses a significant challenge within infra-red imagery due to the high levels of intra-class variation in thermal vehicle signatures caused by aspects of design, current operating duration and ambient thermal conditions. Despite these challenges, infra-red sensing offers significant generalized target object detection advantages in terms of all-weather operation and invariance to visual camouflage techniques. This work investigates the accuracy of a number of real-time object classification approaches for this task within the wider context of an existing initial object detection and tracking framework. Specifically we evaluate the use of traditional feature-driven bag of visual words and histogram of oriented gradient classification approaches against modern convolutional neural network architectures. Furthermore, we use classical photogrammetry, within the context of current target detection and classification techniques, as a means of approximating 3D target position within the scene based on this vehicle type classification. Based on photogrammetric estimation of target position, we then illustrate the use of regular Kalman filter based tracking operating on actual 3D vehicle trajectories. Results are presented using a conventional thermal-band infra-red (IR) sensor arrangement where targets are tracked over a range of evaluation scenarios

Eliminating the Dreaded Blind Spot: Adapting 3D Object Detection and Monocular Depth Estimation to 360° Panoramic Imagery

Recent automotive vision work has focused almost exclusively on processing forward-facing cameras. However, future autonomous vehicles will not be viable without a more comprehensive surround sensing, akin to a human driver, as can be provided by 360◦ panoramic cameras. We present an approach to adapt contemporary deep network architectures developed on conventional rectilinear imagery to work on equirectangular 360◦ panoramic imagery. To address the lack of annotated panoramic automotive datasets availability, we adapt contemporary automotive dataset, via style and projection transformations, to facilitate the cross-domain retraining of contemporary algorithms for panoramic imagery. Following this approach we retrain and adapt existing architectures to recover scene depth and 3D pose of vehicles from monocular panoramic imagery without any panoramic training labels or calibration parameters. Our approach is evaluated qualitatively on crowd-sourced panoramic images and quantitatively using an automotive environment simulator to provide the first benchmark for such techniques within panoramic imagery

Pharmacological blockade of muscle afferents and perception of effort:A systematic review with meta-analysis

Background The perception of effort provides information on task difficulty and influences physical exercise regulation and human behavior. This perception differs from other-exercise related perceptions such as pain. There is no consensus on the role of group III/IV muscle afferents as a signal processed by the brain to generate the perception of effort. Objective The aim of this meta-analysis was to investigate the effect of pharmacologically blocking muscle afferents on the perception of effort. Methods Six databases were searched to identify studies measuring the ratings of perceived effort during physical exercise, with and without pharmacological blockade of muscle afferents. Articles were coded based on the operational measurement used to distinguish studies in which perception of effort was assessed specifically (effort dissociated) or as a composite experience including other exercise-related perceptions (effort not dissociated). Articles that did not provide enough information for coding were assigned to the unclear group. Results The effort dissociated group (n = 6) demonstrated a slight increase in ratings of perceived effort with reduced muscle afferent feedback (standard mean change raw, 0.39; 95% confidence interval 0.13-0.64). The group effort not dissociated (n = 2) did not reveal conclusive results (standard mean change raw, - 0.29; 95% confidence interval - 2.39 to 1.8). The group unclear (n = 8) revealed a slight ratings of perceived effort decrease with reduced muscle afferent feedback (standard mean change raw, - 0.27; 95% confidence interval - 0.50 to - 0.04). Conclusions The heterogeneity in results between groups reveals that the inclusion of perceptions other than effort in its rating influences the ratings of perceived effort reported by the participants. The absence of decreased ratings of perceived effort in the effort dissociated group suggests that muscle afferent feedback is not a sensory signal for the perception of effort