Face pose estimation in monocular images

People use orientation of their faces to convey rich, inter-personal information. For example, a person will direct his face to indicate who the intended target of the conversation is. Similarly in a conversation, face orientation is a non-verbal cue to listener when to switch role and start speaking, and a nod indicates that a person has understands, or agrees with, what is being said. Further more, face pose estimation plays an important role in human-computer interaction, virtual reality applications, human behaviour analysis, pose-independent face recognition, driver s vigilance assessment, gaze estimation, etc. Robust face recognition has been a focus of research in computer vision community for more than two decades. Although substantial research has been done and numerous methods have been proposed for face recognition, there remain challenges in this field. One of these is face recognition under varying poses and that is why face pose estimation is still an important research area. In computer vision, face pose estimation is the process of inferring the face orientation from digital imagery. It requires a serious of image processing steps to transform a pixel-based representation of a human face into a high-level concept of direction. An ideal face pose estimator should be invariant to a variety of image-changing factors such as camera distortion, lighting condition, skin colour, projective geometry, facial hairs, facial expressions, presence of accessories like glasses and hats, etc. Face pose estimation has been a focus of research for about two decades and numerous research contributions have been presented in this field. Face pose estimation techniques in literature have still some shortcomings and limitations in terms of accuracy, applicability to monocular images, being autonomous, identity and lighting variations, image resolution variations, range of face motion, computational expense, presence of facial hairs, presence of accessories like glasses and hats, etc. These shortcomings of existing face pose estimation techniques motivated the research work presented in this thesis. The main focus of this research is to design and develop novel face pose estimation algorithms that improve automatic face pose estimation in terms of processing time, computational expense, and invariance to different conditions

Face pose estimation in monocular images

People use orientation of their faces to convey rich, inter-personal information. For example, a person will direct his face to indicate who the intended target of the conversation is. Similarly in a conversation, face orientation is a non-verbal cue to listener when to switch role and start speaking, and a nod indicates that a person has understands, or agrees with, what is being said. Further more, face pose estimation plays an important role in human-computer interaction, virtual reality applications, human behaviour analysis, pose-independent face recognition, driver s vigilance assessment, gaze estimation, etc. Robust face recognition has been a focus of research in computer vision community for more than two decades. Although substantial research has been done and numerous methods have been proposed for face recognition, there remain challenges in this field. One of these is face recognition under varying poses and that is why face pose estimation is still an important research area. In computer vision, face pose estimation is the process of inferring the face orientation from digital imagery. It requires a serious of image processing steps to transform a pixel-based representation of a human face into a high-level concept of direction. An ideal face pose estimator should be invariant to a variety of image-changing factors such as camera distortion, lighting condition, skin colour, projective geometry, facial hairs, facial expressions, presence of accessories like glasses and hats, etc. Face pose estimation has been a focus of research for about two decades and numerous research contributions have been presented in this field. Face pose estimation techniques in literature have still some shortcomings and limitations in terms of accuracy, applicability to monocular images, being autonomous, identity and lighting variations, image resolution variations, range of face motion, computational expense, presence of facial hairs, presence of accessories like glasses and hats, etc. These shortcomings of existing face pose estimation techniques motivated the research work presented in this thesis. The main focus of this research is to design and develop novel face pose estimation algorithms that improve automatic face pose estimation in terms of processing time, computational expense, and invariance to different conditions.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Face pose estimation in monocular images

People use orientation of their faces to convey rich, inter-personal information. For example, a person will direct his face to indicate who the intended target of the conversation is. Similarly in a conversation, face orientation is a non-verbal cue to listener when to switch role and start speaking, and a nod indicates that a person has understands, or agrees with, what is being said. Further more, face pose estimation plays an important role in human-computer interaction, virtual reality applications, human behaviour analysis, pose-independent face recognition, driver s vigilance assessment, gaze estimation, etc. Robust face recognition has been a focus of research in computer vision community for more than two decades. Although substantial research has been done and numerous methods have been proposed for face recognition, there remain challenges in this field. One of these is face recognition under varying poses and that is why face pose estimation is still an important research area. In computer vision, face pose estimation is the process of inferring the face orientation from digital imagery. It requires a serious of image processing steps to transform a pixel-based representation of a human face into a high-level concept of direction. An ideal face pose estimator should be invariant to a variety of image-changing factors such as camera distortion, lighting condition, skin colour, projective geometry, facial hairs, facial expressions, presence of accessories like glasses and hats, etc. Face pose estimation has been a focus of research for about two decades and numerous research contributions have been presented in this field. Face pose estimation techniques in literature have still some shortcomings and limitations in terms of accuracy, applicability to monocular images, being autonomous, identity and lighting variations, image resolution variations, range of face motion, computational expense, presence of facial hairs, presence of accessories like glasses and hats, etc. These shortcomings of existing face pose estimation techniques motivated the research work presented in this thesis. The main focus of this research is to design and develop novel face pose estimation algorithms that improve automatic face pose estimation in terms of processing time, computational expense, and invariance to different conditions.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Electrical and optical studies of dilute nitride and bismide compound semiconductors

A few percent of nitrogen (N) or bimuth (Bi) incorporation in GaAs compound semiconductors have proved to lower significantly its bandgap. This unusual bandgap reduction is of interest for numerous applications such as long wave-length lasers, solar cells etc. However, the addition of these impurity atoms also introduces defect levels in the bandgap of the host materials. These can have severe implications on the material's quality, for example they can decrease the lifetime of the charge carriers and degrade the optical efficiency. In this work, deep levels traps were investigated in silicon-doped GaAsN epitaxial layers containing N concentrations from 0.2% to 1.2% grown by molecular beam epitaxy (MBE) on n+ GaAs substrates using DLTS and high resolution Laplace DLTS techniques. In addition, a further investigation was carried out to study the effect of annealing and hydrogenation treatments on the defects present in the as-grown layers. Several deep levels were detected in the as-grown GaAsN samples. These were identified with previously reported (SiGa-NAs), EL6 (Ga vacancies-related complex), (N-As)As, EL3 (off-centre substitutional oxygen in As sites) and EL2-like (anti site AsGa) defect levels. It was found that, depending upon the N concentration, heat treatment has a different effect on the traps. For samples with N = 0.2 - 0.4 %, some defects were annihilated and no generation of new defects was observed. In the case of samples with N = 0.8- 1.2 % the annealing results in both generation of new traps and elimination of some existing traps. In general, it was found that hydrogenation of the as-grown GaAsN epilayers passivates most of the deep levels. However, for the samples with N = 0.8%, although hydrogen passivates some of the defects and reduce the concentration of others it also creates new defects which are suspected to be hydrogen-related complexes. (100) and (311)B GaAsBi layers grown by molecular beam epitaxy under various arsenic overpressures have been investigated using optical and structural techniques. The optimised Bi incorporation was found to occur near stoichiometric conditions. The incorporation of Bi into the GaAsBi alloy, as determined by high resolution X-ray diffraction (HRXRD), is sizably larger in the (311)B epilayers than in (100) epilayers. HRXRD reveals 4% Bi-incorporation in (311)B and 3% in (100) GaAs orientations. The conventional optical transmission results confirmed that the bandgap of the (311)B epilayer is around 90 meV lower than that corresponding to (100) sample. This measurement provide further evidence that Bi incorporates more in (311)B than in (100) surfaces. The low temperature post-growth heat treatment of GaAsBi alloys reveals an improvement in the structural and optical properties of these materials. A substantial increase in photoluminescence signal infers a large reduction of defects

Collective Asynchronous Remote Invocation (CARI): A High-Level and Effcient Communication API for Irregular Applications

The Message Passing Interface (MPI) standard continues to dominate the landscape of parallel computing as the de facto API for writing large-scale scientific applications. But the critics argue that it is a low-level API and harder to practice than shared memory approaches. This paper addresses the issue of programming productivity by proposing a high-level, easy-to-use, and effcient programming API that hides and segregates complex low-level message passing code from the application specific code. Our proposed API is inspired by communication patterns found in Gadget-2, which is an MPI-based parallel production code for cosmological N-body and hydrodynamic simulations. In this paper—we analyze Gadget-2 with a view to understanding what high-level Single Program Multiple Data (SPMD) communication abstractions might be developed to replace the intricate use of MPI in such an irregular application—and do so without compromising the effciency. Our analysis revealed that the use of low-level MPI primitives—bundled with the computation code—makes Gadget-2 diffcult to understand and probably hard to maintain. In addition, we found out that the original Gadget-2 code contains a small handful of—complex and recurring—patterns of message passing. We also noted that these complex patterns can be reorganized into a higherlevel communication library with some modifications to the Gadget-2 code. We present the implementation and evaluation of one such message passing pattern (or schedule) that we term Collective Asynchronous Remote Invocation (CARI). As the name suggests, CARI is a collective variant of Remote Method Invocation (RMI), which is an attractive, high-level, and established paradigm in distributed systems programming. The CARI API might be implemented in several ways—we develop and evaluate two versions of this API on a compute cluster. The performance evaluation reveals that CARI versions of the Gadget-2 code perform as well as the original Gadget-2 code but the level of abstraction is raised considerably

Electrical and optical studies of dilute nitride and bismide compound semiconductors

A few percent of nitrogen (N) or bimuth (Bi) incorporation in GaAs compound semiconductors have proved to lower significantly its bandgap. This unusual bandgap reduction is of interest for numerous applications such as long wave-length lasers, solar cells etc. However, the addition of these impurity atoms also introduces defect levels in the bandgap of the host materials. These can have severe implications on the material's quality, for example they can decrease the lifetime of the charge carriers and degrade the optical efficiency. In this work, deep levels traps were investigated in silicon-doped GaAsN epitaxial layers containing N concentrations from 0.2% to 1.2% grown by molecular beam epitaxy (MBE) on n+ GaAs substrates using DLTS and high resolution Laplace DLTS techniques. In addition, a further investigation was carried out to study the effect of annealing and hydrogenation treatments on the defects present in the as-grown layers. Several deep levels were detected in the as-grown GaAsN samples. These were identified with previously reported (SiGa-NAs), EL6 (Ga vacancies-related complex), (N-As)As, EL3 (off-centre substitutional oxygen in As sites) and EL2-like (anti site AsGa) defect levels. It was found that, depending upon the N concentration, heat treatment has a different effect on the traps. For samples with N = 0.2 - 0.4 %, some defects were annihilated and no generation of new defects was observed. In the case of samples with N = 0.8- 1.2 % the annealing results in both generation of new traps and elimination of some existing traps. In general, it was found that hydrogenation of the as-grown GaAsN epilayers passivates most of the deep levels. However, for the samples with N = 0.8%, although hydrogen passivates some of the defects and reduce the concentration of others it also creates new defects which are suspected to be hydrogen-related complexes. (100) and (311)B GaAsBi layers grown by molecular beam epitaxy under various arsenic overpressures have been investigated using optical and structural techniques. The optimised Bi incorporation was found to occur near stoichiometric conditions. The incorporation of Bi into the GaAsBi alloy, as determined by high resolution X-ray diffraction (HRXRD), is sizably larger in the (311)B epilayers than in (100) epilayers. HRXRD reveals 4% Bi-incorporation in (311)B and 3% in (100) GaAs orientations. The conventional optical transmission results confirmed that the bandgap of the (311)B epilayer is around 90 meV lower than that corresponding to (100) sample. This measurement provide further evidence that Bi incorporates more in (311)B than in (100) surfaces. The low temperature post-growth heat treatment of GaAsBi alloys reveals an improvement in the structural and optical properties of these materials. A substantial increase in photoluminescence signal infers a large reduction of defects

Two stages hybrid model of fuzzy linear regression with support vector machines for colorectal cancer

Fuzzy linear regression analysis has become popular among researchers and standard model in analyzing data in vagueness phenomena. However, the factor and symptoms to predict tumor size of colorectal cancer still ambiguous and not clear. The problem in using a linear regression will arise when uncertain data and not precise data were presented. Since the fuzzy set theory‟s concept can deal with data not to a precise point value (uncertainty data), fuzzy linear regression was applied. In this study, two new models for hybrid model namely the multiple linear regression clustering with support vector machine model (MLRCSVM) and fuzzy linear regression with symmetric parameter with support vector machine (FLRWSPCSVM) were proposed to analyze colorectal cancer data. Other than that, the parameter, error and explanation of the five procedures to both new models were included. These models applying five statistical models such as multiple linear regression, fuzzy linear regression, fuzzy linear regression with symmetric parameter, fuzzy linear regression with asymmetric parameter and support vector machine model. At first, the proposed models were applied to the 1000 simulated data. Furthermore, secondary data of 180 colorectal cancer patients who received treatment in general hospital with twenty five independent variables with different combination of variable types were considered to find the best models to predict the tumor size of CRC. The main objective of this study is to determine the best model to predicting the tumor size of CRC and to identify the factors and symptoms that contribute to the size of CRC. The comparisons among all the models were carried out to find the best model by using statistical measurements of mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). The results showed that the FLRWSPCSVM was found to be the best model, having the lowest MSE, RMSE, MAE and MAPE value by 100.605, 10.030, 7.556 and 14.769. Hence, the size of colorectal cancer could be predicted by managing twenty five independent variables

Integrated Fungicidal Management for Downy Mildew of Pumpkin (Pseudoperonosporacubensis)

Downy mildew of pumpkin is caused by the fungus (Pseudoperonosporacubensis), which is responsible for considerable damage to the cucurbits. This pathogen plays a major role for yield losses in pumpkin crop. Current study was intended to verify the effectiveness of different fungicides alone and in combination against downy mildew of pumpkin. Diseased samples were collected for inoculation from different farms of Okara district. A pumpkin variety (Mahadeev) was inoculated by spraying method grown at experimental area of the Department of Plant Pathology, University of Agriculture, Faisalabad under randomized complete block design (RCBD). Selected chemotherapeutic mixtures were sprayed for the control of (Pseudoperonasporacubensis) under field condition. The data were recorded and analyzed statistically. Ipovalicarb (s) + Propanib (P) revealed maximum efficacy against disease (60%) followed by Tebuconazole (s) + Metiram (p) (58%) and Matlaxyal (s) + Mancozeb (p) (55%), Cymoxinal (s) + Mancozeb (p) (52%), Difenconazole (s) +Mancozeb (p) (46%) and Chlorothalonil (p) + Fosytyle Al (39%) respectively. Thus, Ipovalicarb (s) + Propanib (P) can be used to manage the disease under field conditions