23 research outputs found

    A Methodology for Extracting Human Bodies from Still Images

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    Monitoring and surveillance of humans is one of the most prominent applications of today and it is expected to be part of many future aspects of our life, for safety reasons, assisted living and many others. Many efforts have been made towards automatic and robust solutions, but the general problem is very challenging and remains still open. In this PhD dissertation we examine the problem from many perspectives. First, we study the performance of a hardware architecture designed for large-scale surveillance systems. Then, we focus on the general problem of human activity recognition, present an extensive survey of methodologies that deal with this subject and propose a maturity metric to evaluate them. One of the numerous and most popular algorithms for image processing found in the field is image segmentation and we propose a blind metric to evaluate their results regarding the activity at local regions. Finally, we propose a fully automatic system for segmenting and extracting human bodies from challenging single images, which is the main contribution of the dissertation. Our methodology is a novel bottom-up approach relying mostly on anthropometric constraints and is facilitated by our research in the fields of face, skin and hands detection. Experimental results and comparison with state-of-the-art methodologies demonstrate the success of our approach

    Quantifying of vision through polymer dispersed liquid crystal double-glazed window

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    The visual linking of a building’s occupants with the outside views is a basic property of windows. However, vision through windows is not yet a metricized factor. The previous research employs a human survey methods to assess the vision through conventional windows. The recently fabricated smart films add a changeable visual transparency feature to the windows. The varied operating transparency challenges the evaluation of vision. Therefore, surveying human preferences is no longer a feasible approach for smart windows. This paper proposes an image-processing-based approach to quantify the vision quality through smart windows. The proposed method was experimentally applied to a polymer dispersed liquid crystal (PDLC) double-glazed window. The system instantaneously determines the available contrast band of the scenes seen through the window. The system adjusts the excitation of the PDLC film to maintain a desired vision level within the determined vision band. A preferred vision ratio (PVR) is proposed to meet the requirements of occupant comfort. The impact of the PVR on vision quality, solar heat gain, and daylight performance was investigated experimentally. The results show that the system can determine the available vision comfort band during daytime considering different occupant requirements

    John Cage’s ‘Rock’ Music: Ecocritical and Performance Considerations in Ryoanji for Solo Oboe and Percussion Obbligato (1983)

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    In the years following World War II, several American composers began breaking from the confines of music notation relegated to five lines and four spaces. Of particular interest to this study, John Cage (1912–1992) began liberating his compositions from the restraint posed by traditional notation in 1951 with his work Imaginary Landscape No. 4. He continued to create and develop varying systems of graphic notation with his indeterminate works, which became increasingly influenced by his interest in the environment and in South and East Asian aesthetic and philosophical considerations, themselves environmentally influenced. One of the latest products of Cage’s coalescence of Asian aesthetics and graphic notation was Ryoanji for solo oboe with percussion obbligato (1983). Inspired by his visit to the Ryōan-ji dry landscape garden in Kyoto, Japan, Cage created graphic notation for his piece bearing the same name by tracing the contours of fifteen rocks onto modified manuscript staves. Although significant research examines the Asian philosophical and aesthetic influences on John Cage’s compositions, and more recent scholarship has examined his works through the lens of ecocritical methodologies, very little critical attention has been given to the ways in which environmentalism informed his compositional process. Furthermore, even less has been given to performance practice considerations in his works. By examining Ryoanji through an ecocritical lens and exploring the impact of Cage’s relationship with the natural environment on his compositional process in this piece, this monograph will show that Cage’s ecomusicological aesthetic was used to inform his works and specifically this piece. Additionally, this study will offer an ecocritical reading of Ryoanji to illustrate how Cage’s environmental considerations could inform performing practices of Ryoanji for Solo Oboe and Percussion Obbligato

    A Novel Synergistic Model Fusing Electroencephalography and Functional Magnetic Resonance Imaging for Modeling Brain Activities

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    Study of the human brain is an important and very active area of research. Unraveling the way the human brain works would allow us to better understand, predict and prevent brain related diseases that affect a significant part of the population. Studying the brain response to certain input stimuli can help us determine the involved brain areas and understand the mechanisms that characterize behavioral and psychological traits. In this research work two methods used for the monitoring of brain activities, Electroencephalography (EEG) and functional Magnetic Resonance (fMRI) have been studied for their fusion, in an attempt to bridge together the advantages of each one. In particular, this work has focused in the analysis of a specific type of EEG and fMRI recordings that are related to certain events and capture the brain response under specific experimental conditions. Using spatial features of the EEG we can describe the temporal evolution of the electrical field recorded in the scalp of the head. This work introduces the use of Hidden Markov Models (HMM) for modeling the EEG dynamics. This novel approach is applied for the discrimination of normal and progressive Mild Cognitive Impairment patients with significant results. EEG alone is not able to provide the spatial localization needed to uncover and understand the neural mechanisms and processes of the human brain. Functional Magnetic Resonance imaging (fMRI) provides the means of localizing functional activity, without though, providing the timing details of these activations. Although, at first glance it is apparent that the strengths of these two modalities, EEG and fMRI, complement each other, the fusion of information provided from each one is a challenging task. A novel methodology for fusing EEG spatiotemporal features and fMRI features, based on Canonical Partial Least Squares (CPLS) is presented in this work. A HMM modeling approach is used in order to derive a novel feature-based representation of the EEG signal that characterizes the topographic information of the EEG. We use the HMM model in order to project the EEG data in the Fisher score space and use the Fisher score to describe the dynamics of the EEG topography sequence. The correspondence between this new feature and the fMRI is studied using CPLS. This methodology is applied for extracting features for the classification of a visual task. The results indicate that the proposed methodology is able to capture task related activations that can be used for the classification of mental tasks. Extensions on the proposed models are examined along with future research directions and applications

    Memristors

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    This Edited Volume Memristors - Circuits and Applications of Memristor Devices is a collection of reviewed and relevant research chapters, offering a comprehensive overview of recent developments in the field of Engineering. The book comprises single chapters authored by various researchers and edited by an expert active in the physical sciences, engineering, and technology research areas. All chapters are complete in itself but united under a common research study topic. This publication aims at providing a thorough overview of the latest research efforts by international authors on physical sciences, engineering, and technology,and open new possible research paths for further novel developments

    Reinforcement Learning

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    Brains rule the world, and brain-like computation is increasingly used in computers and electronic devices. Brain-like computation is about processing and interpreting data or directly putting forward and performing actions. Learning is a very important aspect. This book is on reinforcement learning which involves performing actions to achieve a goal. The first 11 chapters of this book describe and extend the scope of reinforcement learning. The remaining 11 chapters show that there is already wide usage in numerous fields. Reinforcement learning can tackle control tasks that are too complex for traditional, hand-designed, non-learning controllers. As learning computers can deal with technical complexities, the tasks of human operators remain to specify goals on increasingly higher levels. This book shows that reinforcement learning is a very dynamic area in terms of theory and applications and it shall stimulate and encourage new research in this field

    MULTIPLE STRUCTURE RECOVERY VIA PREFERENCE ANALYSIS IN CONCEPTUAL SPACE

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    Finding multiple models (or structures) that fit data corrupted by noise and outliers is an omnipresent problem in empirical sciences, includingComputer Vision, where organizing unstructured visual data in higher level geometric structures is a necessary and basic step to derive better descriptions and understanding of a scene. This challenging problem has a chicken-and-egg pattern: in order to estimate models one needs to first segment the data, and in order to segment the data it is necessary to know which structure points belong to. Most of the multi-model fitting techniques proposed in the literature can be divided in two classes, according to which horn of the chicken-egg-dilemma is addressed first, namely consensus and preference analysis. Consensus-based methods put the emphasis on the estimation part of the problem and focus on models that describe has many points as possible. On the other side, preference analysis concentrates on the segmentation side in order to find a proper partition of the data, from which model estimation follows. The research conducted in this thesis attempts to provide theoretical footing to the preference approach and to elaborate it in term of performances and robustness. In particular, we derive a conceptual space in which preference analysis is robustly performed thanks to three different formulations of multiple structures recovery, i.e. linkage clustering, spectral analysis and set coverage. In this way we are able to propose new and effective strategies to link together consensus and preferences based criteria to overcome the limitation of both. In order to validate our researches, we have applied our methodologies to some significant Computer Vision tasks including: geometric primitive fitting (e.g. line fitting; circle fitting; 3D plane fitting), multi-body segmentation, plane segmentation, and video motion segmentation

    A COMPARISON BETWEEN MOTIVATIONS AND PERSONALITY TRAITS IN RELIGIOUS TOURISTS AND CRUISE SHIP TOURISTS

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    The purpose of this paper is to analyze the motivations and the personality traits that characterize tourists who choose religious travels versus cruises. Participating in the research were 683 Italian tourists (345 males and 338 females, age range 18–63 years); 483 who went to a pilgrimage travel and 200 who chose a cruise ship in the Mediterranean Sea. Both groups of tourists completed the Travel Motivation Scale and the Big Five Questionnaire. Results show that different motivations and personality traits characterize the different types of tourists and, further, that motivations for traveling are predicted by specific —some similar, other divergent— personality trait
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