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

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

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

Drilling Performance Monitoring and Optimization: A Data-driven Approach

Abstract Drilling performance monitoring and optimization are crucial in increasing the overall NPV of an oil and gas project. Even after rigorous planning, drilling phase of any project can be hindered by unanticipated problems, such as bit balling. The objective of this paper is to implement artifcial intelligence technique to develop a smart model for more accurate and robust real-time drilling performance monitoring and optimization. For this purpose, the back propagation, feed forward neural network model was developed to predict rate of penetration (ROP) using diferent input parameters such as weight on bit, rotations per minute, mud fow (GPM) and diferential pressures. The heavy hitter features identifcation and dimensionality reduction are performed to understand the impacts of each of the drilling parameters on ROP. This will be used to optimize the input parameters for model development and validation and performing the operation optimization when bit is underperforming. The model is frst developed based on the drilling experiments performed in the laboratory and then extended to feld applications. From both laboratory and feld test data provided, we have proved that the data-driven model built using multilayer perceptron technique can be successfully used for drilling performance monitoring and optimization, especially identifying the bit malfunction or failure, i.e., bit balling. We have shown that the ROP has complex relationship with other drilling variables which cannot be captured using conventional statistical approaches or from diferent empirical models. The data-driven approach combined with statistical regression analysis provides better understanding of relationship between variables and prediction of ROP

What are the progesterone-induced changes of the outcome and the serum markers of injury, oxidant activity and inflammation in diffuse axonal injury patients?

To permit appropriate targeted therapy, the present clinical studywas aimed to investigate the effects of progesterone on the outcome and the serum markers of injury, oxidant activity and inflammation in diffuse axonal injury (DAI). Forty-eightmaleDAI patients were divided into two groups (control and progesterone). Progesterone group received progesterone in dose of 1 mg/kg per 12 h for five days. The outcome was investigated using Extended Glasgow Outcome Scale (GOS-E) and functional independencemeasure (FIM). Themarkers of inflammation [interleukin-1β (IL-1β), IL-6, transforming growth factor-β1 (TGF-β1)], injury (brain protein of S-100B), and oxidant activity [malondialdehyde (MDA)] were evaluated in the serum of the patients. Higher GOS-E and FIMscoreswere observed in progesterone group at the six-month follow-up (P b 0.05 and P b 0.01, respectively). Meanwhile, a reduction in the serum levels of IL-1β, MDA and S-100B was noticed in progesterone group 24 h after injury (P b 0.05, P b 0.001 and P b 0.05, respectively), and there was an increase in serum levels of IL-6 and TGF-β1 (P b 0.01 and P b 0.05, respectively). Also, lower levels of MDA and S-100B, and higher levels of TGF-β1were observed in progesterone group six days after injury (P b 0.05). According to these findings, progesterone may improve the outcome in DAI patients probably through modulation in the levels of cytokines, and reduction in the injury and oxidant activity

Meet Dr. Mehdi Khazaeli

Meet Dr. Mehdi Khazaeli, Assistant Engineering Management Professor at the University of the Pacific. Hope you like what he has to say. Let us know what you think by commenting below

PIES 2020 Welcome message from Dr. Mehdi Khazaeli

This is the introduction to the 3rd annual PIES event. The event is now virtual due to COVID-19

PIES 2020 Welcome message from Dr. Mehdi Khazaeli

This is the introduction to the 3rd annual PIES event. The event is now virtual due to COVID-19