Image Segmentation from RGBD Images by 3D Point Cloud Attributes and High-Level Features

In this paper, an approach is developed for segmenting an image into major surfaces and potential objects using RGBD images and 3D point cloud data retrieved from a Kinect sensor. In the proposed segmentation algorithm, depth and RGB data are mapped together. Color, texture, XYZ world coordinates, and normal-, surface-, and graph-based segmentation index features are then generated for each pixel point. These attributes are used to cluster similar points together and segment the image. The inclusion of new depth-related features provided improved segmentation performance over RGB-only algorithms by resolving illumination and occlusion problems that cannot be handled using graph-based segmentation algorithms, as well as accurately identifying pixels associated with the main structure components of rooms (walls, ceilings, floors). Since each segment is a potential object or structure, the output of this algorithm is intended to be used for object recognition. The algorithm has been tested on commercial building images and results show the usability of the algorithm in real time applications

Cerebral activations during viewing of food stimuli in adult patients with acquired structural hypothalamic damage: A functional neuroimaging study

BACKGROUND/OBJECTIVES: Obesity is common following hypothalamic damage due to tumours. Homeostatic and non-homeostatic brain centres control appetite and energy balance but their interaction in the presence of hypothalamic damage remains unknown. We hypothesized that abnormal appetite in obese patients with hypothalamic damage results from aberrant brain processing of food stimuli. We sought to establish differences in activation of brain food motivation and reward neurocircuitry in patients with hypothalamic obesity (HO) compared with patients with hypothalamic damage whose weight had remained stable. SUBJECTS/METHODS: In a cross-sectional study at a University Clinical Research Centre, we studied 9 patients with HO, 10 age-matched obese controls, 7 patients who remained weight-stable following hypothalamic insult (HWS) and 10 non-obese controls. Functional magnetic resonance imaging was performed in the fasted state, 1 h and 3 h after a test meal, while subjects were presented with images of high-calorie foods, low-calorie foods and non-food objects. Insulin, glucagon-like peptide-1, Peptide YY and ghrelin were measured throughout the experiment, and appetite ratings were recorded. RESULTS: Mean neural activation in the posterior insula and lingual gyrus (brain areas linked to food motivation and reward value of food) in HWS were significantly lower than in the other three groups (P=0.001). A significant negative correlation was found between insulin levels and posterior insula activation (P=0.002). CONCLUSIONS: Neural pathways associated with food motivation and reward-related behaviour, and the influence of insulin on their activation may be involved in the pathophysiology of HO.International Journal of Obesity advance online publicatio

DECORAS: detection and characterization of radio-astronomical sources using deep learning

We present DECORAS, a deep learning based approach to detect both point and extended sources from Very Long Baseline Interferometry (VLBI) observations. Our approach is based on an encoder-decoder neural network architecture that uses a low number of convolutional layers to provide a scalable solution for source detection. In addition, DECORAS performs source characterization in terms of the position, effective radius and peak brightness of the detected sources. We have trained and tested the network with images that are based on realistic Very Long Baseline Array (VLBA) observations at 20 cm. Also, these images have not gone through any prior de-convolution step and are directly related to the visibility data via a Fourier transform. We find that the source catalog generated by DECORAS has a better overall completeness and purity, when compared to a traditional source detection algorithm. DECORAS is complete at the 7.5$\sigma$ level, and has an almost factor of two improvement in reliability at 5.5$\sigma$. We find that DECORAS can recover the position of the detected sources to within 0.61 $\pm$ 0.69 mas, and the effective radius and peak surface brightness are recovered to within 20 per cent for 98 and 94 per cent of the sources, respectively. Overall, we find that DECORAS provides a reliable source detection and characterization solution for future wide-field VLBI surveys.Comment: submitted to MNRA

Optimizing the best play in basketball using deep learning

In a close game of basketball, victory or defeat can depend on a single shot. Being able to identify the best player and play scenario for a given opponent’s defense can increase the likelihood of victory. Progress in technology has resulted in an increase in the popularity of sports analytics over the last two decades, where data can be used by teams and individuals to their advantage. A popular data analytic technique in sports is deep learning. Deep learning is a branch of machine learning that finds patterns within big data and can predict future decisions. The process relies on a raw dataset for training purposes. It can be utilized in sports by using deep learning to read the data and provide a better understanding of where players can be the most successful. In this study the data used were on division I women’s basketball games of a private university in a conference featuring top 25 teams. Deep learning was applied to optimize the best offensive play in a game scenario for a given set of features. The system is used to predict the play that would lead to the highest probability of a made shot

Assessment of Levee Erosion using Image Processing and Contextual Cueing

Soil erosion is one of the most severe land degradation problems afflicting many parts of the world where topography of the land is relatively steep. Due to inaccessibility to steep terrain, such as slopes in levees and forested mountains, advanced data processing techniques can be used to identify and assess high risk erosion zones. Unlike existing methods that require human observations, which can be expensive and error-prone, the proposed approach uses a fully automated algorithm to indicate when an area is at risk of erosion; this is accomplished by processing Landsat and aerial images taken using drones. In this paper the image processing algorithm is presented, which can be used to identify the scene of an image by classifying it in one of six categories: levee, mountain, forest, degraded forest, cropland, grassland or orchard. This paper focuses on automatic scene detection using global features with local representations to show the gradient structure of an image. The output of this work counts as a contextual cueing and can be used in erosion assessment, which can be used to predict erosion risks in levees. We also discuss the environmental implications of deferred erosion control in levees

Gesture based Human Computer Interaction for Athletic Training

The invention of depth sensors for mobile devices, has led to availability of relatively inexpensive high-resolution depth and visual (RGB) sensing for a wide range of applications. The complementary nature of the depth and visual information opens up new opportunities to solve fundamental problems in object and activity recognition, people tracking, 3D mapping and localization, etc. One of the most interesting challenges that can be tackled by using these sensors is tracking the body movements of athletes and providing natural interaction as a result. In this study depth sensors and gesture recognition tools will be used to analyze the position and angle of an athlete’s body parts thought out an exercise. The goal is to assess the training performance of an athlete and decrease injury risk by giving warnings when the trainer is performing a high risk activity

A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

Meeting abstrac

Myocardial energy depletion and dynamic systolic dysfunction in hypertrophic cardiomyopathy

Evidence indicates that anatomical and physiological phenotypes of hypertrophic cardiomyopathy (HCM) stem from genetically mediated, inefficient cardiomyocyte energy utilization, and subsequent cellular energy depletion. However, HCM often presents clinically with normal left ventricular (LV) systolic function or hyperkinesia. If energy inefficiency is a feature of HCM, why is it not manifest as resting LV systolic dysfunction? In this Perspectives article, we focus on an idiosyncratic form of reversible systolic dysfunction provoked by LV obstruction that we have previously termed the 'lobster claw abnormality' — a mid-systolic drop in LV Doppler ejection velocities. In obstructive HCM, this drop explains the mid-systolic closure of the aortic valve, the bifid aortic pressure trace, and why patients cannot increase stroke volume with exercise. This phenomenon is characteristic of a broader phenomenon in HCM that we have termed dynamic systolic dysfunction. It underlies the development of apical aneurysms, and rare occurrence of cardiogenic shock after obstruction. We posit that dynamic systolic dysfunction is a manifestation of inefficient cardiomyocyte energy utilization. Systolic dysfunction is clinically inapparent at rest; however, it becomes overt through the mechanism of afterload mismatch when LV outflow obstruction is imposed. Energetic insufficiency is also present in nonobstructive HCM. This paradigm might suggest novel therapies. Other pathways that might be central to HCM, such as myofilament Ca2+ hypersensitivity, and enhanced late Na+ current, are discussed

Surgical aortic valve replacement in the era of transcatheter aortic valve implantation: a review of the UK national database

Objectives To date the reported outcomes of surgical aortic valve replacement (SAVR) are mainly in the settings of trials comparing it with evolving transcatheter aortic valve implantation. We set out to examine characteristics and outcomes in people who underwent SAVR reflecting a national cohort and therefore ‘real-world’ practice. Design Retrospective analysis of prospectively collected data of consecutive people who underwent SAVR with or without coronary artery bypass graft (CABG) surgery between April 2013 and March 2018 in the UK. This included elective, urgent and emergency operations. Participants’ demographics, preoperative risk factors, operative data, in-hospital mortality, postoperative complications and effect of the addition of CABG to SAVR were analysed. Setting 27 (90%) tertiary cardiac surgical centres in the UK submitted their data for analysis. Participants 31 277 people with AVR were identified. 19 670 (62.9%) had only SAVR and 11 607 (37.1%) had AVR+CABG. Results In-hospital mortality for isolated SAVR was 1.9% (95% CI 1.6% to 2.1%) and was 2.4% for AVR+CABG. Mortality by age category for SAVR only were: 75 years=2.2%. For SAVR+CABG these were; 2.2%, 1.8% and 3.1%. For different categories of EuroSCORE, mortality for SAVR in low risk people was 1.3%, in intermediate risk 1% and for high risk 3.9%. 74.3% of the operations were elective, 24% urgent and 1.7% emergency/salvage. The incidences of resternotomy for bleeding and stroke were 3.9% and 1.1%, respectively. Multivariable analyses provided no evidence that concomitant CABG influenced outcome. However, urgency of the operation, poor ventricular function, higher EuroSCORE and longer cross clamp and cardiopulmonary bypass times adversely affected outcomes. Conclusions Surgical SAVR±CABG has low mortality risk and a low level of complications in the UK in people of all ages and risk factors. These results should inform consideration of treatment options in people with aortic valve disease