While Physics-Based Simulation (PBS) can accurately drape a 3D garment on a 3D body, it remains too costly for real-time applications, such as virtual try-on. By contrast, inference in a deep network, requiring a single forward pass, is much faster. Taking advantage of this, we propose a novel architecture to fit a 3D garment template to a 3D body. Specifically, we build upon the recent progress in 3D point cloud processing with deep networks to extract garment features at varying levels of detail, including pointwise, patch-wise and global features. We fuse these features with those extracted in parallel from the 3D body, so as to model the cloth-body interactions. The resulting two-stream architecture, which we call as GarNet, is trained using a loss function inspired by physics-based modeling, and delivers visually plausible garment shapes whose 3D points are, on average, less than 1 cm away from those of a PBS method, while running 100 times faster. Moreover, the proposed method can model various garment types with different cutting patterns when parameters of those patterns are given as input to the network.

, "Deep convolutional neural network for classifying Fusarium wilt of radish from unmanned aerial vehicles," J. Appl. Remote Sens. 11(4), 042621 (2017), doi: 10.1117/1.JRS.11.042621. Abstract. Recently, unmanned aerial vehicles (UAVs) have gained much attention. In particular, there is a growing interest in utilizing UAVs for agricultural applications such as crop monitoring and management. We propose a computerized system that is capable of detecting Fusarium wilt of radish with high accuracy. The system adopts computer vision and machine learning techniques, including deep learning, to process the images captured by UAVs at low altitudes and to identify the infected radish. The whole radish field is first segmented into three distinctive regions (radish, bare ground, and mulching film) via a softmax classifier and K-means clustering. Then, the identified radish regions are further classified into healthy radish and Fusarium wilt of radish using a deep convolutional neural network (CNN). In identifying radish, bare ground, and mulching film from a radish field, we achieved an accuracy of ≥97.4%. In detecting Fusarium wilt of radish, the CNN obtained an accuracy of 93.3%. It also outperformed the standard machine learning algorithm, obtaining 82.9% accuracy. Therefore, UAVs equipped with computational techniques are promising tools for improving the quality and efficiency of agriculture today.

No abstract

No abstract

IntroductionSlavery and human trafficking are crimes involving the violation of human rights and refer to exploitative situations where an individual cannot refuse or leave due to threats, coercion or abuse of power. Activities involving slavery include forced labour exploitation, forced sexual exploitation, forced marriage and servitude. Epidemiological studies show high levels of mental health need and poor provision of appropriate support for survivors. What mental health recovery means to victims/survivors and how it could be promoted is under-researched.Methods and analysisA grounded theory study based on individual interviews will be undertaken. Survivors across the UK will be identified and recruited from non-governmental organisations and via social media. As per grounded theory methodology, data collection and analysis will be undertaken concurrently and recruitment will continue until theoretical saturation is reached. It is anticipated that approximately 30 participants will be recruited. Interviews will be audio recorded, transcribed verbatim and uploaded to NVivo V.11. The constant comparative method will be used to analyse the data, in order to produce a theoretical framework for mental health recovery that is grounded in the experiences of survivors.Ethics and disseminationEthical approval has been obtained from the Faculty of Medicine and Health Sciences Ethics Committee at the University of Nottingham. The findings of the study will be disseminated to academic, professional and survivor-based audiences to inform future policy developments and the provision of mental health recovery support to this population.