Automated daily human activity recognition for video surveillance using neural network

Surveillance video systems are gaining increasing attention in the field of computer vision due to its demands of users for the seek of security. It is promising to observe the human movement and predict such kind of sense of movements. The need arises to develop a surveillance system that capable to overcome the shortcoming of depending on the human resource to stay monitoring, observing the normal and suspect event all the time without any absent mind and to facilitate the control of huge surveillance system network. In this paper, an intelligent human activity system recognition is developed. Series of digital image processing techniques were used in each stage of the proposed system, such as background subtraction, binarization, and morphological operation. A robust neural network was built based on the human activities features database, which was extracted from the frame sequences. Multi-layer feed forward perceptron network used to classify the activities model in the dataset. The classification results show a high performance in all of the stages of training, testing and validation. Finally, these results lead to achieving a promising performance in the activity recognition rate

Atlas-based segmentation and classification of magnetic resonance brain images

A wide range of different image modalities can be found today in medical imaging. These modalities allow the physician to obtain a non-invasive view of the internal organs of the human body, such as the brain. All these three dimensional images are of extreme importance in several domains of medicine, for example, to detect pathologies, follow the evolution of these pathologies, prepare and realize surgical planning with, or without, the help of robot systems or for statistical studies. Among all the medical image modalities, Magnetic Resonance (MR) imaging has become of great interest in many research areas due to its great spatial and contrast image resolution. It is therefore perfectly suited for anatomic visualization of the human body such as deep structures and tissues of the brain. Medical image analysis is a complex task because medical images usually involve a large amount of data and they sometimes present some undesirable artifacts, as for instance the noise. However, the use of a priori knowledge in the analysis of these images can greatly simplify this task. This prior information is usually represented by the reference images or atlases. Modern brain atlases are derived from high resolution cryosections or in vivo images, single subject-based or population-based, and they provide detailed images that may be interactively and easily examined in their digital format in computer assisted diagnosis or intervention. Then, in order to efficiently combine all this information, a battery of registration techniques is emerging based on transformations that bring two medical images into voxel-to-voxel correspondence. One of the main aims of this thesis is to outline the importance of including prior knowledge in the medical image analysis framework and the indispensable role of registration techniques in this task. In order to do that, several applications using atlas information are presented. First, the atlas-based segmentation in normal anatomy is shown as it is a key application of medical image analysis using prior knowledge. It consists of registering the brain images derived from different subjects and modalities within the atlas coordinate system to improve the localization and delineation of the structures of interest. However, the use of an atlas can be problematic in some particular cases where some structures, for instance a tumor or a sulcus, exists in the subject and not in the atlas. In order to solve this limitation of the atlases, a new atlas-based segmentation method for pathological brains is proposed in this thesis as well as a validation method to assess this new approach. Results show that deep structures of the brain can still be efficiently segmented using an anatomic atlas even if they are largely deformed because of a lesion. The importance of including a priori knowledge is also presented in the application of brain tissue classification. The prior information represented by the tissue templates can be included in a brain tissue segmentation approach thanks to the registration techniques. This is another important issue presented in this thesis and it is analyzed through a comparative study of several non-supervised classification techniques. These methods are selected to represent the whole range of prior information that can be used in the classification process: the image intensity, the local spatial model, and the anatomical priors. Results show that the registration between the subject and the tissue templates allows the use of prior information but the accuracy of both the prior information and the registration highly influence the performance of the classification techniques. Another aim of this thesis is to present the concept of dynamic medical image analysis, in which the prior knowledge and the registration techniques are also of main importance. Actually, many medical image applications have the objective of statically analyzing one single image, as for instance in the case of atlas-based segmentation or brain tissue classification. But in other cases the implicit idea of changes detection is present. Intuitively, since the human body is changing continuously, we would like to do the image analysis from a dynamic point of view by detecting these changes, and by comparing them afterwards with templates to know if they are normal. The need of such approaches is even more evident in the case of many brain pathologies such as tumors, multiple sclerosis or degenerative diseases. In these cases, the key point is not only to detect but also to quantify and even characterize the evolving pathology. The evaluation of lesion variations over time can be very useful, for instance in the pharmaceutical research and clinical follow up. Of course, a sequence of images is needed in order to do such an analysis. Two approaches dealing with the idea of change detection are proposed as the last (but not least) issue presented in this work. The first one consists of performing a static analysis of each image forming the data set and, then, of comparing them. The second one consists of analyzing the non-rigid transformation between the sequence images instead of the images itself. Finally, both static and dynamic approaches are illustrated with a potential application: the cortical degeneration study is done using brain tissue segmentation, and the study of multiple sclerosis lesion evolution is performed by non-rigid deformation analysis. In conclusion, the importance of including a priori information encoded in the brain atlases in medical image analysis has been put in evidence with a wide range of possible applications. In the same way, the key role of registration techniques is shown not only as an efficient way to combine all the medical image modalities but also as a main element in the dynamic medical image analysis

Moving Object Detection based on RGBD Information

This thesis is targeting the Moving Object Detection topic, more specifically, the Background Subtraction. In this study, we proposed two approaches using color and depth information to solve the background subtraction. The following two paragraphs will give a brief abstract for each approach. In this research study, we propose a framework for improving traditional Background Subtraction techniques. This framework is based on two data types: color and depth; it stands for obtaining preliminary results of the background segmentation using Depth and RGB channels independently, then using an algorithm to fuse them to create the final results. The experiments on the SBM-RGBD dataset using four methods: ViBe, LOBSTER, SuBSENSE, and PAWCS, proved that the proposed framework achieves an impressive performance compared to the original RGB-based techniques from the state-of-the-art. This dissertation also proposes a novel deep learning model called Deep Multi-Scale Network (DMSN) for Background Subtraction. This convolutional neural network is built to use RGB color channels and Depth maps as inputs with which it can fuse semantic and spatial information. Compared with previous Deep Learning Background Subtraction techniques that lack information due to their use of only RGB channels, our RGBD version can overcome most of the drawbacks, especially in some particular challenges. Further, this study introduces a new protocol for the SBM-RGBD dataset regarding scene-independent evaluation, dedicated to Deep Learning methods to set up a competitive platform that includes more challenging situations. The proposed method proved its efficiency in solving the background subtraction in complex problems at different levels. The experimental results verify that the proposed work outperforms the state-of-the-art on SBM-RGBD and GSM datasets

Spatial econometrics and the Lasso estimator : theory and applications

This thesis links two topics of empirical economics: spatial econometrics and the Lasso estimator. Spatial econometrics is concerned with methods and models accounting for interaction effects between units. The Lasso estimator is a regularisation technique that allows for simultaneous variable selection and estimation in a high dimensional setting where the number of parameters may exceed the sample size. Three applied and theoretical articles are presented that demonstrate how spatial econometric research can benefit from high-dimensional methods and, specifically, the Lasso. The introduction in Chapter 1 presents a literature review of both fields and discusses the connections between the two topics. Chapter 2 examines the effect of economic growth on civil conflicts in Africa. The Lasso estimator is employed to generate instrumental variables, which account for non-linearity and spatial heterogeneity. The theoretical contribution in Chapter 3 proposes a two-step Lasso estimator that can consistently estimate the spatial weights matrix in a spatial autoregressive panel model. Chapter 4 is an application to the US housing market. A Lasso-based estimation method is considered that controls for spatial effects in a spatial error-correction model. Chapter 5 provides concluding remarks

Spatial econometrics and the Lasso estimator : theory and applications

This thesis links two topics of empirical economics: spatial econometrics and the Lasso estimator. Spatial econometrics is concerned with methods and models accounting for interaction effects between units. The Lasso estimator is a regularisation technique that allows for simultaneous variable selection and estimation in a high dimensional setting where the number of parameters may exceed the sample size. Three applied and theoretical articles are presented that demonstrate how spatial econometric research can benefit from high-dimensional methods and, specifically, the Lasso. The introduction in Chapter 1 presents a literature review of both fields and discusses the connections between the two topics. Chapter 2 examines the effect of economic growth on civil conflicts in Africa. The Lasso estimator is employed to generate instrumental variables, which account for non-linearity and spatial heterogeneity. The theoretical contribution in Chapter 3 proposes a two-step Lasso estimator that can consistently estimate the spatial weights matrix in a spatial autoregressive panel model. Chapter 4 is an application to the US housing market. A Lasso-based estimation method is considered that controls for spatial effects in a spatial error-correction model. Chapter 5 provides concluding remarks

Probabilistic partial volume modelling of biomedical tomographic image data

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Review of the techniques used in motor‐cognitive human‐robot skill transfer

Abstract A conventional robot programming method extensively limits the reusability of skills in the developmental aspect. Engineers programme a robot in a targeted manner for the realisation of predefined skills. The low reusability of general‐purpose robot skills is mainly reflected in inability in novel and complex scenarios. Skill transfer aims to transfer human skills to general‐purpose manipulators or mobile robots to replicate human‐like behaviours. Skill transfer methods that are commonly used at present, such as learning from demonstrated (LfD) or imitation learning, endow the robot with the expert's low‐level motor and high‐level decision‐making ability, so that skills can be reproduced and generalised according to perceived context. The improvement of robot cognition usually relates to an improvement in the autonomous high‐level decision‐making ability. Based on the idea of establishing a generic or specialised robot skill library, robots are expected to autonomously reason about the needs for using skills and plan compound movements according to sensory input. In recent years, in this area, many successful studies have demonstrated their effectiveness. Herein, a detailed review is provided on the transferring techniques of skills, applications, advancements, and limitations, especially in the LfD. Future research directions are also suggested

URINARY TRACT INFECTION IN WOMEN AGED 18-64: DOCTORS', PATIENTS’, AND LAY PERCEPTIONS AND UNDERSTANDINGS.

This thesis provides an insight into the problems of urinary tract infection (UTI) through the eyes of women sufferers, non-sufferers, and health professionals. It describes the use of language and metaphor in women’s descriptions. It investigates current ideas and knowledge published in academic journals, in books, and on the Internet, and assesses the quality of currently available web-based information. The thesis is based almost entirely on qualitative methodologies. I used grounded theory for the studies of lay and professional ideas. Focus groups preceded one-to-one interviews. The study of language and metaphor is derived from lay interviews and uses discourse analysis. I based the studies of Internet information on two surveys, one year apart, of popular websites drawn from four commonly used search engines. I rank ordered popular websites and assessed information in the ‘top twenty’ using content analysis and a simple, predominantly binary, scoring system based on an internationally recognised set of criteria. Folklore and myths, often passed down the generations, and sometimes shared by doctors, are important factors in women’s health beliefs. Early learning experiences during medical training may contribute disproportionately to doctors’ beliefs. UTIs cause embarrassment, and women rarely discuss their illness with male friends and relatives. They are also happier to discuss their problems with female health professionals, though they more commonly cite shared experience rather than embarrassment as the reason for this choice. Since these studies were completed, a major project concluded that delayed prescriptions should be used for UTI. The natural history of this illness and women’s prior use of self-management prior to attendance suggest that this strategy may not be readily accepted. Nurses and pharmacists are keen to manage UTI. As UTI lends itself to management by algorithm, delegation to professionals other than doctors may be effective. Easy access to antibiotics increases resistance; fear of this inhibits the implementation of devolved care. The quality of information on the Internet is variable and some of the most popular sites score poorly when compared against recognised criteria. Better quality sites are becoming more prominent when searching the Internet through popular search engines, and efforts to improve this source of information are important. Future research is probably best directed at information transfer and new models of delivery of care

Palaeoenvironmental and palaeogeographic reconstruction of the Tequixquiac Basin, Central Eastern Mexico: Mid to late Pleistocene environments

This PhD research utilises, for the first time, sedimentary evidence for mid to late Quaternary environmental change in the Tequixquiac region of Central-eastern Mexico. This project has logged over 50 stratigraphic sections and geochemically analysed a 55 m lithostratigraphic sequence for multi-proxy palaeoenvironmental information. The main research objective was to develop a spatial and temporal Palaeogeographic and Palaeoenvironmental model for the study area the covered the late Pleistocene to early Holocene. The findings of the study, based on the analysis of sedimentology, micromorphology, stable isotopes δ18Ocarbonate and δ13CDIC as well as ICP-OES sediment and tephra geochemistry, LOI, AMS radiocarbon, 40Ar/39Ar and Uranium-series dating has allowed a chronologically constrained paleoenvironmental and palaeogeographic reconstruction of the study area. The results of the study suggest that the Tequixquiac Basin has undergone a significant hydrological change from perennial lacustrine to ephemeral fluvial conditions between MIS15 – MIS 1 controlled by a combination of; climatic fluctuations, expressed as depositional cyclicity driven by precessional fluctuations to insolation levels. On shorter time-scales, changes in the mean position of the ITCZ related to SST, latitudinal gradients, atmospheric surface pressure gradients, the extent of Northern Hemisphere land and sea ice cover, and oceanic circulation patterns. Fluctuations in the TOC content of sediments are thought to be related to El Niño-like (dry) and La Niña-like (wet) events. While climate is thought to have been critical to the development the Quaternary localised uplift, deformation and normal faulting have also influenced palaeohydrology and water-table elevation during the recorded depositional period (Figs 8.15 d & 8.16 d)