Color Separation for Background Subtraction

Background subtraction is a vital step in many computer vision systems. In background subtraction, one is given two (or more) frames of a video sequence taken with a still camera. Due to the stationarity of the camera, any color change in the scene is mainly due to the presence of moving objects. The goal of background subtraction is to separate the moving objects (also called the foreground) from the stationary background. Many background subtraction approaches have been proposed over the years. They are usually composed of two distinct stages, background modeling and foreground detection. Most of the standard background subtraction techniques focus on the background modeling. In the thesis, we focus on the improvement of foreground detection performance. We formulate the background subtraction as a pixel labeling problem, where the goal is to assign each image pixel either a foreground or background labels. We solve the pixel labeling problem using a principled energy minimization framework. We design an energy function composed of three terms: the data, smoothness, and color separation terms. The data term is based on motion information between image frames. The smoothness term encourages the foreground and background regions to have spatially coherent boundaries. These two terms have been used for background subtraction before. The main contribution of this thesis is the introduction of a new color separation term into the energy function for background subtraction. This term models the fact that the foreground and background regions tend to have different colors. Thus, introducing a color separation term encourages foreground and background regions not to share the same colors. Color separation term can help to correct the mistakes made due to the data term when the motion information is not entirely reliable. We model color separation term with L1 distance, using the technique developed by Tang et.al. Color clustering is used to efficiently model the color space. Our energy function can be globally and efficiently optimized with graph cuts, which is a very effective method for solving binary energy minimization problems arising in computer vision. To prove the effectiveness of including the color separation term into the energy function for background subtraction, we conduct experiments on standard datasets. Our model depends on color clustering and background modeling. There are many possible ways to perform color clustering and background modeling. We evaluate several different combinations of popular color clustering and background modeling approaches. We find that incorporating spatial and motion information as part of the color clustering process can further improve the results. The best performance of our approach is 97% compared to the approach without color separation that achieves 90%

Intrinsic Spin Hall Conductivity of MoTe2 and WTe2 Semimetals

We report a comprehensive study on the intrinsic spin Hall conductivity (SHC) of semimetals MoTe2 and WTe2 by ab initio calculation. Large SHC and desirable spin Hall angles have been discovered, due to the strong spin orbit coupling effect and low charge conductivity in semimetals. Diverse anisotropic SHC values, attributed to the unusual reduced-symmetry crystalline structure, have been revealed. We report an effective method on SHC optimization by electron doping, and exhibit the mechanism of SHC variation respect to the energy shifting by the spin Berry curvature. Our work provides insights into the realization of strong spin Hall effects in 2D systems

Using internet search data to predict new HIV diagnoses in China: a modelling study.

ObjectivesInternet data are important sources of abundant information regarding HIV epidemics and risk factors. A number of case studies found an association between internet searches and outbreaks of infectious diseases, including HIV. In this research, we examined the feasibility of using search query data to predict the number of new HIV diagnoses in China.DesignWe identified a set of search queries that are associated with new HIV diagnoses in China. We developed statistical models (negative binomial generalised linear model and its Bayesian variants) to estimate the number of new HIV diagnoses by using data of search queries (Baidu) and official statistics (for the entire country and for Guangdong province) for 7 years (2010 to 2016).ResultsSearch query data were positively associated with the number of new HIV diagnoses in China and in Guangdong province. Experiments demonstrated that incorporating search query data could improve the prediction performance in nowcasting and forecasting tasks.ConclusionsBaidu data can be used to predict the number of new HIV diagnoses in China up to the province level. This study demonstrates the feasibility of using search query data to predict new HIV diagnoses. Results could potentially facilitate timely evidence-based decision making and complement conventional programmes for HIV prevention

Controllable Spin Current in van der Waals Ferromagnet Fe3GeTe2

The control of spin current is pivotal for spintronic applications, especially for spin-orbit torque devices. Spin Hall effect (SHE) is a prevalent method to generate spin current. However, it is difficult to manipulate its spin polarization in nonmagnet. Recently, the discovery of spin current in ferromagnet offers opportunity to realize the manipulation. In the present work, the spin current in van der Waals ferromagnet Fe3GeTe2 (FGT) with varying magnetization is theoretically investigated. It has been observed that the spin current in FGT presents the nonlinear behavior with respect to magnetization. The in-plane and out-of-plane spin polarization emerges simultaneously, and the bilayer FGT can even exhibit arbitrary spin polarization thanks to the reduced symmetry. More intriguingly, the correlation between anomalous Hall effect (AHE) and spin anomalous Hall effect (SAHE) has been interpreted from the aspect of Berry curvature. This work illustrates that the interplay of symmetry and magnetism can effectively control the magnitude and spin polarization of the spin current, providing a practical method to realize exotic spin-orbit torques

Finanční analýza společnosti PetroChina Company Limited

Import 05/08/2014The aim of the bachelor thesis is evaluation of financial position of PetroChina limited using the selected financial analysis methods and pyramidal decomposition of return on equity.Theoretical part discribes the concept of this selected the financial analysis method.In practical part financial position of PetroChina Company Limited is described using methods of financial analysis.The aim of the bachelor thesis is evaluation of financial position of PetroChina limited using the selected financial analysis methods and pyramidal decomposition of return on equity.154 - Katedra financívelmi dobř

ESSAYS IN STOCHASTIC MODELING AND OPTIMIZATION

Stochastic modeling plays an important role in estimating potential outcomes where randomness or uncertainty is present. This type of modeling forecasts the probability distributions of potential outcomes by allowing for random variation in one or more inputs over time under different conditions. One of the classic topics of stochastic modeling is queueing theory.Hence, the first part of the dissertation is about a stylized queueing model motivated by paid express lanes on highways. There are two parallel, observable queues with finitely many servers: one queue has a faster service rate, but charges a fee to join, and the other is free but slow. Upon arrival, customers see the state of each queue and choose between them by comparing the respective disutility of time spent waiting, subject to random shocks. This framework encompasses both the multinomial logit and exponomial customer choice models. Using a fluid limit analysis, we give a detailed characterization of the equilibrium in this system. We show that social welfare is optimized when the express queue is exactly at (but not over) full capacity; however, in some cases, revenue is maximized by artificially cre- ating congestion in the free queue. The latter behaviour is caused by changes in the price elasticity of demand as the service capacity of the free queue fills up. The second part of the dissertation is about a new optimal experimental design for linear regression models with continuous covariates, where the expected response is interpreted as the value of the covariate vector, and an “error” occurs if a lower- valued vector is falsely identified as being better than a higher-valued one. Our design optimizes the rate at which the probability of error converges to zero using a large deviations theoretic characterization. This is the first large deviations-based optimal design for continuous decision spaces, and it turns out to be considerably simpler and easier to implement than designs that use discretization. We give a practicable sequential implementation and illustrate its empirical potential