Offender profiling aims at generating information of an offender based upon the crime committed and the goal is to be an adjunct in the crime invesigation process. The aim of the study was to examine crime scene characteristics and corroborating evidence and their associations with offenders' interrogation behavior. Results from 207 analyzed police files indicated that reactive (impulsive) offenders confessed more often when there was technical evidence against them compared to instrumental (calculating) offenders. Reactive offenders also had more injuries. Injured reactive offenders were more inclined to deny, whereas injured instrumental offenders claimed amnesia more often. When comparing the confessions injured reactive offenders were more inclined to confess than injured instrumental offenders. There was also a strong tendency implying more confessions with witness reports. The findings point towards the practical usefulness of information of crime scene characteristics and corroborating evidence in the interrogation phase by better tailoring the interviewing strategy.

Thesis--Freie Universität Berlin. / Includes bibliographical references (p. 144-150).

L'utilisation de systèmes multi-caméras est de plus en plus populaire et il y a un intérêt croissant à résoudre les problèmes de vision par ordinateur dans ce contexte particulier. L'objectif étant de ne pas se limiter à l'application des méthodes monoculaires mais de proposer de nouvelles approches intrinsèquement orientées vers les systèmes multi-caméras. Le travail de cette thèse a pour objectif une meilleure compréhension du problème de segmentation multi-vues, pour proposer une nouvelle approche qui tire meilleur parti de la redondance d'information inhérente à l'utilisation de plusieurs points de vue. La segmentation multi-vues est l'identification de l'objet observé simultanément dans plusieurs caméras et sa séparation de l'arrière-plan. Les approches monoculaires classiques raisonnent sur chaque image de manière indépendante et ne bénéficient pas de la présence de plusieurs points de vue. Une question clé de la segmentation multi-vues réside dans la propagation d'information sur la segmentation entres les images tout en minimisant la complexité et le coût en calcul. Dans ce travail, nous investiguons en premier lieu l'utilisation d'un ensemble épars d'échantillons de points 3D. L'algorithme proposé classe chaque point comme "vide" s'il se projette sur une région du fond et "occupé" s'il se projette sur une région avant-plan dans toutes les vues. Un modèle probabiliste est proposé pour estimer les modèles de couleur de l'avant-plan et de l'arrière-plan, que nous testons sur plusieurs jeux de données de l'état de l'art. Deux extensions du modèle sont proposées. Dans la première, nous montrons la flexibilité de la méthode proposée en intégrant les mélanges de Gaussiennes comme modèles d'apparence. Cette intégration est possible grâce à l'utilisation de l'inférence variationelle. Dans la seconde, nous montrons que le modèle bayésien basé sur les échantillons 3D peut aussi être utilisé si des mesures de profondeur sont présentes. Les résultats de l'évaluation montrent que les problèmes de robustesse, typiquement causés par les ambigüités couleurs entre fond et forme, peuvent être au moins partiellement résolus en utilisant cette information de profondeur. A noter aussi qu'une approche multi-vues reste meilleure qu'une méthode monoculaire utilisant l'information de profondeur. Les différents tests montrent aussi les limitations de la méthode basée sur un échantillonnage éparse. Cela a montré la nécessité de proposer un modèle reposant sur une description plus riche de l'apparence dans les images, en particulier en utilisant les superpixels. L'une des contributions de ce travail est une meilleure modélisation des contraintes grâce à un schéma par coupure de graphes liant les régions d'images aux échantillons 3D. Dans le cas statique, les résultats obtenus rivalisent avec ceux de l'état de l'art mais sont obtenus avec beaucoup moins de points de vue. Les résultats dans le cas dynamique montrent l'intérêt de la propagation de l'information de segmentation à travers la géométrie et le mouvement. Enfin, la dernière partie de cette thèse explore la possibilité d'améliorer le suivi dans les systèmes multi-caméras non calibrés. Un état de l'art sur le suivi monoculaire et multi-caméras est présenté et nous explorons l'utilisation des matrices d'autosimilarité comme moyen de décrire le mouvement et de le comparer entre plusieurs caméras. / There has been a growing interest for multi-camera systems and many interesting works have tried to tackle computer vision problems in this particular configuration. The general objective is to propose new multi-view oriented methods instead of applying limited monocular approaches independently for each viewpoint. The work in this thesis is an attempt to have a better understanding of the multi-view object segmentation problem and to propose an alternative approach making maximum use of the available information from different viewpoints. Multiple view segmentation consists in segmenting objects simultaneously in several views. Classic monocular segmentation approaches reason on a single image and do not benefit from the presence of several viewpoints. A key issue in that respect is to ensure propagation of segmentation information between views while minimizing complexity and computational cost. In this work, we first investigate the idea that examining measurements at the projections of a sparse set of 3D points is sufficient to achieve this goal. The proposed algorithm softly assigns each of these 3D samples to the scene background if it projects on the background region in at least one view, or to the foreground if it projects on foreground region in all views. A complete probabilistic framework is proposed to estimate foreground/background color models and the method is tested on various datasets from state of the art. Two different extensions of the sparse 3D sampling segmentation framework are proposed in two scenarios. In the first, we show the flexibility of the sparse sampling framework, by using variational inference to integrate Gaussian mixture models as appearance models. In the second scenario, we propose a study of how to incorporate depth measurements in multi-view segmentation. We present a quantitative evaluation, showing that typical color-based segmentation robustness issues due to color-space ambiguity between foreground and background, can be at least partially mitigated by using depth, and that multi-view color depth segmentation also improves over monocular color depth segmentation strategies. The various tests also showed the limitations of the proposed 3D sparse sampling approach which was the motivation to propose a new method based on a richer description of image regions using superpixels. This model, that expresses more subtle relationships of the problem trough a graph construction linking superpixels and 3D samples, is one of the contributions of this work. In this new framework, time related information is also integrated. With static views, results compete with state of the art methods but they are achieved with significantly fewer viewpoints. Results on videos demonstrate the benefit of segmentation propagation through geometric and temporal cues. Finally, the last part of the thesis explores the possibilities of tracking in uncalibrated multi-view scenarios. A summary of existing methods in this field is presented, in both mono-camera and multi-camera scenarios. We investigate the potential of using self-similarity matrices to describe and compare motion in the context of multi-view tracking.

Documentary is a genre of film that portrays "real" events using depictions that connote the objectivity and facticity implied by the processes of photorealism. Many contemporary documentary theorists and critics observe a constitutive problem in this ethos: despite the apparent constructions and agendas of documentary filmmaking, the framing and assumption of documentary as a window on the world tend to naturalize its own constructions as "real." Critics who engage documentary trace the multitude of ways this problem plays out in particular films. These projects yield many important insights, but they most often approach documentary as a form of inherently deficient representation fraught with ethical questions-- questions created by the frame and ethos of objectivity it fails to achieve. Are events portrayed truthfully? Are people depicted fairly? Are filmmakers misrepresenting? In this study I seek to show that a rhetorical approach to documentary shifts the critical focus to instead examine how documentary constructions and images work as evidence in the claims and rhetorical agendas of documentary. I study recent film texts (2000-2012) that explicitly and primarily structure their documentary materials as evidence for the truth of an argument or interpretation, and I argue that documentaries, when they work as documentary, establish and verify their depictions as evidence by drawing on the elements of their "scene." I use Kenneth Burke's dramatistic approach to observe that the "real world" as depicted in documentary is at once experienced as representation of the world outside the documentary, but also constructed as the scene of a dramatization. Understanding the dramatism of documentary helps me to characterize what I call a "rhetoric of evidence" that may be particular to documentary expression. In the films I study documentary "scene" interacts at key moments and particular ways to locate the events of films in the "real world," not just as evidence that something is real, but also as meaningful for particular arguments and rhetorical moves. This study reveals the often extremely subtle ways that documentaries wield the influence of "truth," and also offers filmmakers an understanding of how evidence might be deployed more deliberately to present a social world that is open for transformation.

There is an increasing interest in the operas of Georg Frideric Handel, both from a scholarly perspective, and that of the modern, professional opera company. Producers of Handelian opera have moved away from productions similar to those staged in Halle, Germany, in the 1920s, which featured vastly reduced recitative and stripped the da capo aria to a single statement of the 'A' section. Modern productions have restored Handel's musical text, and in addition have attempted to recreate the original dramatic conditions and ethos of the work. The problem faced by the Halle producers still exists, however. How does the modern producer satisfy the expectations of the modern audience, while remaining faithful to the intention of the composer and the original production. This paper will investigate a possible approach to staging Handelian opera, with specific reference to the 'Mad Scene' from Handel's opera Orlando. Included in this examination will be a discussion of eighteenth-century British staging practices. These elements will be considered in the light of stage design and scenic practices of the period.

Darbe nagrinėjama. kaip muzikantas-atlikėjas meninės kūrybos procese išgyvena daugybę įvairiausių emocijų. Pasirodymas scenoje visada susijęs su gebėjimu susikurti tinkamą sceninę būseną. Scenos baimė – psichologinis reiškinys, kurį jaučia muzikantai-atlikėjai sceninio pasirodymo metu. Tyrimo objektas – muzikantų-atlikėjų scenos baimės raiška ir jos įveikos galimybės. Tyrimo tikslas – analizuoti muzikantų-atlikėjų scenos baimės raiškos ypatumus ir jos įveikos galimybes. Tyrimo uždaviniai: 1) atskleisti meninės kūrybos ir muzikanto-atlikėjo profesijos specifiškumą, analizuojant mokslinę, psichologinę, pedagoginę literatūrą; 2) nustatyti psichologinę, filosofinę, teologinę baimės prigimtį; 3) apibūdinti scenos baimės apibrėžtį; 4) ištirti veiksnius, lemiančius scenos baimės atsiradimo priežastis; 5) įvardinti scenos baimės įveikos būdus, kurie padėtų muzikantui-atlikėjui siekti emocinio stabilumo sceninėje veikloje. Atlikto tyrimo rezultatai, kuriame dalyvavo 28 respondentai, atskleidė, kad scenos baimė egzistuoja, kuri įtakoja muzikanto-atlikėjo sceninį pasirodymą. Pagrindinėmis priežastimis, kurios lemia scenos baimės atsiradimą, tyrime dalyvavę muzikantai-atlikėjai įvardijo: • nepakankamą profesinis pasirengimą; • koncertinės patirties stoką; • individualias charakterio savybės (nepasitikėjimas savimi, valios stoka); • aplinkinių nuomonę; • nepavykusius sceninius pasirodymus. Tyrimo rezultatai atskleidė šiuos scenos baimės įveikos būdus: • profesinio meistriškumo kėlimas... [toliau žr. visą tekstą] / The study focuses on a musician-performer‘s experiences of a lot of various emotions in his artistic creativity process. A performance on the scene is always related with the ability of creating a certain scenic state. Scene fair is a psychological phenomenon which is experienced by musician- performers while performing on the scene. The object of the investigation is the expression of scene fair of a musician- performer and the possibilities to defeat it. The aim of the investigation is to analyze specific features of the expression of the scene fair and the possibilities to defeat it. The objectives are: 1) to reveal the specific features of artistic creativity and the profession of a musician- performer while analyzing scientific, psychological, pedagogical literature; 2) to identify psychological, philosophical, theological nature of fair; 3) to characterize the definition of scene fair; 4) to investigate the factors influencing the rise of the scene fair; 5) to name the ways of coping with scene fair which would help the musician- performer to achieve emotional stability in his scenic activity. The results of the investigation, in which 28 respondents took part, showed that scene fair exists and it affects the performance of the musician- performer. As the main reasons which affect the rise of the scene fair the respondent musician- performers named the following: • insufficient professional preparation; • lack of concert experience; • individual features of character... [to full text]

No description available.

With the development of GPS and 3G network, mobile navigation systems are widely used in people’s daily life. From 2D electronic map to 3D simulation map, the navigation services based on real-scene information is becoming the mainstream. Due to the rapid growth of mobile navigation systems, users are not satisfied with the""basic navigation services any more, such as locating places and devising paths. They put forward higher demands on location-based services (LBS for short). Thus, LBS applications based on mobile navigation systems has attracted more and more attention. This thesis studies the drawbacks and relevant technologies of LBS, then designs and implements the LBS application for mobile virtual navigation systems based on Android platform. First of all, it analyzes the current status of LBS services, and come up with a self-adapting individuation LBS proposal based on user interests. Secondly, according to the requirements of LBS functional integrity and type diversity, it designs three function modules of the LBS application, including Mobile LBS Module, Location Sharing Module and Designated Reconstruction of Real Scene Module. What’s more, Mobile  LBS Module is aimed to provide basic and extended location services to users, as well as user interest-based LBS information. Location Sharing Module puts forward a new check-in mode using physical medium, such as 2D barcodes and Bluetooth devices to share users’ current position. Designated Reconstruction of Real Scene Module is responsible for navigation route review and historical track queries, via the technology of SVG image compression and dynamic caching mechanism. Finally, the LBS application invokes Augmented Reality Module to show the location data, service results and navigation route in 3D form. The LBS application for mobile virtual navigation system based on Android platform in this thesis can provide navigation and location-based services of high quality directly and clearly, as well as bring users new experiences of mobile function. It has already achieved good results in experimental environment.

In social scenes, humans interact with each other by sending visible social signals, such as facial expressions, body gestures, and gaze movements. Social cognition, the ability to perceive, model, and predict such social signals, enables people to understand social interactions and to plan their behavior in accordance with the understanding. Computational social cognition is a necessary function allowing artificial agents to enter the social spaces because it enables a socially acceptable behavior. However, two key challenges preclude developing computational social cognition: (1) the core attributes of social cognition such as attention, emotion, and intent are latent quantities that cannot be directly measured by existing sensors; (2) social behaviors are interdependent to each other, i.e., a unified representation is required to understand social behavior as wholes. In this thesis, we address these challenges by establishing a computational foundation towards social scene understanding from social cameras. A social camera is a camera held or worn by a member of a social group that inherits his/her gaze behavior. This social camera is an ideal sensor to capture social signals for three reasons: (1) social cameras naturally secure the best view because the wearers or holders intelligently localize the best view point to attend to what they find interesting; (2) social cameras produce more views of events of greater interest; (3) social cameras efficiently capture socially important events by following social behaviors when the scenes are dynamic. We leverage these advantages of social cameras to understand social scenes. We present a framework to develop social cognition by perceiving social signals, modeling the relationship between them, and predicting social behaviors. Social Signal Reconstruction: Reconstructing social signals in a unified 3D coordinate system provides a computational basis to analyze social scenes, e.g., to build a model, reason about relationships, and predict social behaviors. We leverage social cameras to reconstruct three types of social signals: gaze movement, body motion, and general scene motion. (1) Gaze is a strong indicator of attentive behaviors. We model the gaze using the primary gaze direction that is emitted from the center of the eyes and aligned with the head orientation. This gaze model is reconstructed in 3D by leveraging ego- and exo-motion of social cameras. (2) Human body motion such as gestures often conveys intent of social interactions. We model skeletal motion using a set of articulated joint trajectories where the distance between the trajectories of adjacent joints remains constant. This articulation constraint in conjunction with a temporal constraint is applied to reconstruct human body motion without an activity specific prior. (3)We further relax the articulation constraint to model general scene motion occurring in social interactions. We represent a 3D trajectory using a linear combination of predefined trajectory basis vectors. We solve for the parameters of each trajectory by formulating it as a linear least squares system that allows us to reconstruct topology-independent motion and handle missing data. Social Behavior Understanding: Social behaviors are interactive by definition and therefore, an individual behavioral analysis in isolation cannot fully account for the fundamental relationship between behaviors. For instance, a social signal transmitted by one person can trigger responses in other and the responses can, in turn, affect the behavior of the person. A relational analysis between the signals is needed to characterize the social interactions. We exploit the reconstructed social signals in a unified coordinate system to understand the relationship between them. In particular, our analysis focuses on joint attention, the primary social attribute that is strongly corv related with attentive behaviors. We present a method to reconstruct 3D joint attention modeled by social charges—latent quantities that form at locations where primary gaze directions of members in a social group intersect. Inspired by the study of electric fields, we model the relationship between gaze behaviors using a gradient field induced by the social charges. This gradient field allows us to predict gaze behaviors given social charges at any location in the scene. Our overarching goal is to develop computational social cognition that will enable artificial agents to accomplish their tasks in a socially acceptable way. This thesis takes a first step towards the goal by leveraging social cameras. We present a 3D representation of social signals and based on the reconstructed signals, we build a relational model of social behaviors, which allows us to predict the behaviors. We apply our frameworks in real-world social scenes including sporting events, meetings, and parties.

We are capable of effortlessly parsing a complex scene presented to us. In order to do this, we must segregate objects from each other and from the background. While this process has been extensively studied in vision science, it remains relatively less understood in auditory science. This thesis sought to characterize the neuroelectric correlates of auditory scene analysis using electroencephalography. Chapter 2 determined components evoked by first-order energy boundaries and second-order pitch boundaries. Chapter 3 determined components evoked by first-order and second-order discontinuous motion boundaries. Both of these chapters focused on analysis of event-related potential (ERP) waveforms and time-frequency analysis. In addition, these chapters investigated the contralateral nature of a negative ERP component. These results extend the current knowledge of auditory scene analysis by providing a starting point for discussing and characterizing first-order and second-order boundaries in an auditory scene. / x, 90 leaves : col. ill. ; 29 cm