The present study examines the relationships between recognition and recall accuracy of faces, and recognition and recall accuracy of objects. Secondly, this study examines the influence of weapon presence on description and identification accuracy, and whether encoding time moderates the effect. 713 participants watched an image that was either displayed for five seconds or twenty seconds, and either included a weapon or no weapon. Subsequently, they were asked to give descriptions of what they saw before viewing a lineup that either included the perpetrator or was made up of innocent suspects. Results indicated that witnesses’ description accuracy of the crime scene had little or no predictive abilities with regards to their facial identification accuracy. Secondly, there was a weapon focus effect found for faces but not for objects. Furthermore, this effect was eliminated at long encoding times. Finally, increasing encoding time improved recognition of objects, but not faces. Results suggest that prior inaccuracy on one aspect of testimony is not necessarily indicative of subsequent inaccuracy on another aspect of testimony. This finding has implications for how jurors and judges should evaluate witness testimony when assessing credibility in the courtroom.

Recent advances in representation learning have led to an increasing variety of vision-based approaches in traffic scene understanding. This includes general vision problems such as object detection, depth estimation, edge/boundary/contour detection, semantic segmentation and scene classification, as well as application-driven problems such as pedestrian detection, vehicle detection, lane marker detection and road segmentation, etc. In this thesis, we approach some of these problems by exploring structured and invariant representations from the visual input. Our research is mainly motivated by two facts: 1. Traffic scenes often contain highly structured layouts. Exploring structured priors is expected to help considerably in improving the scene understanding performance. 2. A major challenge of traffic scene understanding lies in the diverse and changing nature of the contents. It is therefore important to find robust visual representations that are invariant against such variability. We start from highway scenarios where we are interested in detecting the hard road borders and estimating the drivable space before such physical boundary. To this end, we treat the task as a joint detection and tracking problem, and formulate it with structured Hough voting (SVH): A conditional random field model that explores both intra-frame geometric and interframe temporal information to generate more accurate and stable predictions. Turning from highway scenes to urban scenes, we consider dense prediction problems such as category-aware semantic edge detection and semantic segmentation. Category-aware semantic edge detection is challenging as the model is required to jointly localize object contours and classify each edge pixel to one or multiple predefined classes. We propose CASENet, a multilabel deep network with state of the art edge detection performance. To address the label misalignment problem in edge learning, we also propose SEAL, a framework towards simultaneous edge alignment and learning. Failure across different domains has been a common bottleneck of semantic segmentation methods. In this thesis, we address the problem of adapting a segmentation model trained on a source domain to another different target domain without knowing the target domain labels, and propose a class-balanced self-training approach for such unsupervised domain adaptation. We adopt the \synthetic-to-real" setting where a model is pre-trained on GTA-5 and adapted to real world datasets such as Cityscapes and Nexar, as well as the \cross-city" setting where a model is pre-trained on Cityscapes, and adapted to unseen data from Rio, Tokyo, Rome and Taipei. Experiment shows the superior performance of our method compared to state of the art methods, such as adversarial training based domain adaptation.

Recognition of text from camera-captured scene/born-digital images help in the development of aids for the blind, unmanned navigation systems and spam filters. However, text in such images is not confined to any page layout, and its location within in the image is random in nature. In addition, motion blur, non-uniform illumination, skew, occlusion and scale-based degradations increase the complexity in locating and recognizing the text in a scene/born-digital image. Text localization and segmentation techniques are proposed for the born-digital image data set. The proposed OTCYMIST technique won the first place and placed in the third position for its performance on the text segmentation task in ICDAR 2011 and ICDAR 2013 robust reading competitions for born-digital image data set, respectively. Here, Otsu’s binarization and Canny edge detection are separately carried out on the three colour planes of the image. Connected components (CC’s) obtained from the segmented image are pruned based on thresholds applied on their area and aspect ratio. CC’s with sufficient edge pixels are retained. The centroids of the individual CC’s are used as nodes of a graph. A minimum spanning tree is built using these nodes of the graph. Long edges are broken from the minimum spanning tree of the graph. Pairwise height ratio is used to remove likely non-text components. CC’s are grouped based on their proximity in the horizontal direction to generate bounding boxes (BB’s) of text strings. Overlapping BB’s are removed using an overlap area threshold. Non-overlapping and minimally overlapping BB’s are used for text segmentation. These BB’s are split vertically to localize text at the word level. A word cropped from a document image can easily be recognized using a traditional optical character recognition (OCR) engine. However, recognizing a word, obtained by manually cropping a scene/born-digital image, is not trivial. Existing OCR engines do not handle these kinds of scene word images effectively. Our intention is to first segment the word image and then pass it to the existing OCR engines for recognition. In two aspects, it is advantageous: it avoids building a character classifier from scratch and reduces the word recognition task to a word segmentation task. Here, we propose two bottom-up approaches for the task of word segmentation. These approaches choose different features at the initial stage of segmentation. Power-law transform (PLT) was applied to the pixels of the gray scale born-digital images to non-linearly modify the histogram. The recognition rate achieved on born-digital word images is 82.9%, which is 20% more than the top performing entry (61.5%) in ICDAR 2011 robust reading competition. In addition, we explored applying PLT to the colour planes such as red, green, blue, intensity and lightness plane by varying the gamma value. We call this technique as Nonlinear enhancement and selection of plane (NESP) for optimal segmentation, which is an improvement over PLT. NESP chooses a particular plane with a proper gamma value based on Fisher discrimination factor. The recognition rate is 72.8% for scene images of ICDAR 2011 robust reading competition, which is 30% higher than the best entry (41.2%). The recognition rate is 81.7% and 65.9% for born-digital and scene images of ICDAR 2013 robust reading competition, respectively, using NESP. Another technique, midline analysis and propagation of segmentation (MAPS), has also been proposed. Here, the middle row pixels of the gray scale image are first segmented and the statistics of the segmented pixels are used to assign text and non-text labels to the rest of the image pixels using min-cut method. Gaussian model is fitted on the middle row segmented pixels before the assignment of other pixels. In MAPS, we assume the middle row pixels are least affected by any of the degradations. This assumption is validated by the good word recognition rate of 71.7% on ICDAR 2011 robust reading competition for scene images. The recognition rate is 83.8% and 66.0% for born-digital and scene images of ICDAR 2013 robust reading competition, respectively, using MAPS. The best reported results for ICDAR 2003 word images is 61.1% using custom lexicons containing the list of test words. On the other hand, NESP and MAPS achieve 66.2% and 64.5% for ICDAR 2003 word images without using any lexicon. By using similar custom lexicon, the recognition rates for ICDAR 2003 word images go up to 74.9% and 74.2% for NESP and MAPS methods, respectively. In place of passing an image segmented by a method, manually segmented word image is submitted to an OCR engine for benchmarking maximum possible recognition rate for each database. The recognition rates of the proposed methods and the benchmark results are reported on the seven publicly available word image data sets and compared with these of reported results in the literature. Since no good Kannada OCR is available, a classifier is designed to recognize Kannada characters and words from Chars74k data set and our own image collection, respectively. Discrete cosine transform (DCT) and block DCT are used as features to train separate classifiers. Kannada words are segmented using the same techniques (MAPS and NESP) and further segmented into groups of components, since a Kannada character may be represented by a single component or a group of components in an image. The recognition rate on Kannada words is reported for different features with and without the use of a lexicon. The obtained recognition performance for Kannada character recognition (11.4%) is three times the best performance (3.5%) reported in the literature.

In this thesis, we present a tool that is an extension of the NAVIRE Framework and used in remote environment exploration and navigation. It is available for end users on everyday devices such as desktops and mobile devices. The tool offers the characteristic of being cross-platform and easy to use and manipulate. Cubic panoramas that are generated by the NAVIRE Framework are loaded in the tool and rendered in 3D space on the user’s device. The 3D panoramas are interactive by allowing the user to view the scene in all direction, move from one scene to another or through interaction with augmented objects. The tool is geared to be interactive in the most natural and intuitive fashion using the inputs available in the different devices of the end-user.

The diploma thesis discusses the Czech club music scene in the context of the transformation of the music industry in recent years. The first section describes recent trends in the music market in the world and in the Czech Republic. The second part is devoted to the specifics of the management and promotion of Czech music scene at club level, defines classical models of contracts between artists, managers, record companies and other entities. The following chapter describes the possibilities of the multi-source financing of the club music scene. The next two chapters characterize cultural policies of Austria and Germany with a focus on creative industries and of the club music scene. The last part is devoted to a specific modelated project, an organization focused on music export and promotional activities.

Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2017-06-22T14:29:24Z No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) Tese - As Ondas Musicais do Pós-Manguebit - Ricardo Maia.com.pdf: 3068947 bytes, checksum: 22686e6c32fe763057f349d5056f9033 (MD5) / Made available in DSpace on 2017-06-22T14:29:24Z (GMT). No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) Tese - As Ondas Musicais do Pós-Manguebit - Ricardo Maia.com.pdf: 3068947 bytes, checksum: 22686e6c32fe763057f349d5056f9033 (MD5) Previous issue date: 2016-02-26 / tese aborda o debate sobre algumas das ondas musicais que surgiram após o Manguebit – manifestação cultural que marcou a cultura local nos anos 1990 e que teve importantes representantes da música pernambucana reconhecidos nacionalmente e no mundo afora, como: Chico Science, Nação Zumbi, Mundo Livre S/A, Mestre Ambrósio, Otto etc. Tendo como foco as análises a partir dos estudos culturais, comunicacionais, sociológicos e antropológicos sobre Cena, Movimento e Carreiras Musicais, principalmente. Escolhemos algumas das manifestações musicais que mais destacaram-se dentre os agrupamentos musicais do Pós-Manguebit: Recife Lo-fi, TsuMangue e Cena Beto. Como forma de encontrar alguns pontos-chave no ambiente musical do Grande Recife do século XXI. As formas de agrupamentos em meio à música despertam o interesse da pesquisa em entender como ocorrem essas dinâmicas de encontros e desencontros, consensos e dissensos, afinidades sociais e violências simbólicas, só para citar algumas maneiras de partilha do sensível musical. E o conceito de onda musical ajuda-nos a entender melhor como os agentes contemporâneos atuam de forma fragmentada, efêmera, líquida e fluída nos grupos que têm a música enquanto sentido aglutinador das manifestações culturais. As movimentações da música alternativa contemporânea da Região Metropolitana da capital pernambucana, tendo como estudo de caso as ondas do Pós-Manguebit pretendem abordar uma parcela da complexa dinâmica cultural recifense mais recente. / The thesis discusses the debate on the musical wave that emerged after the Manguebit - cultural event that marked the local culture in the 1990s and had important representatives of Pernambuco music recognized nationally and worldwide, as Chico Science, Nação Zumbi, Mundo Livre S/A, Mestre Ambrósio, Otto etc. Focusing on analysis from cultural studies, communication, sociological and anthropological on scene, movement and music careers, mostly. We chose some of the musical manifestations that most stood out among the musical groups of the Post-Manguebit: Recife Lo-fi, TsuMangue and Cena Beto. In order to find some key points in the musical environment of Recife of the twenty-first century. The grouping forms amid music arouse the interest of research to understand how these dynamics of meetings and disagreements, consensus and dissent, social affinities and symbolic violence, just to name a few ways of sharing sensitive musical. And the concept of musical wave helps us to better understand how the contemporary agents act in a fragmented, ephemeral, liquid and fluid way in groups that have music as a unifying sense of cultural manifestations. The drives by the contemporary alternative music in the Metropolitan Region of Recife, having as a case study of the waves of Post-Manguebit intend to approach a portion of the complex latest Recife cultural dynamics.

A área de Personalização de Conteúdo tem sido foco de pesquisas recentes em Ciências da Computação, sendo a segmentação automática de vídeos digitais em cenas uma linha importante no suporte à composição de serviços de personalização, tais como recomendação ou sumarização de conteúdo. Uma das principais abordagens de segmentação em cenas se baseia no agrupamento de tomadas relacionadas. Logo, para que esse processo seja bem sucedido, é necessário que as tomadas estejam bem representadas. Porém, percebe-se que esse tópico tem sido deixado em segundo plano pelas pesquisas relacionadas à segmentação. Assim, este trabalho tem o objetivo de desenvolver um método baseado nas características visuais dos quadros, que possibilite aprimorar a representação de tomadas de vídeos digitais e, consequentemente, contribuir para a melhoria do desempenho de técnicas de segmentação em cenas. / The Content Personalization area has been the focus of recent researches in Computer Science and the automatic scene segmentation of digital videos is an important field supporting the composition of personalization services, such as content recommendation or summarization. One of the main approaches for scene segmentation is based on the clustering of related shots. Thus, in order to this process to be successful, is necessary to properly represent shots. However, we can see that the works reported on the literature have left this topic in backgroud. Therefore, this work aims to develop a method based on frames visual features, which enables to improve video shots representation and, consequently, the performance of scene segmentation techniques.

Este trabalho tem como objetivo desenvolver um método de segmentação de cenas em vídeos digitais que trate segmentos semânticamente complexos. Como prova de conceito, é apresentada uma abordagem multimodal que utiliza uma definição mais geral para cenas em telejornais, abrangendo tanto cenas onde âncoras aparecem quanto cenas onde nenhum âncora aparece. Desse modo, os resultados obtidos da técnica multimodal foram signifiativamente melhores quando comparados com os resultados obtidos das técnicas monomodais aplicadas em separado. Os testes foram executados em quatro grupos de telejornais brasileiros obtidos de duas emissoras de TV diferentes, cada qual contendo cinco edições, totalizando vinte telejornais / This work aims to develop a method for scene segmentation in digital video which deals with semantically complex segments. As proof of concept, we present a multimodal approach that uses a more general definition for TV news scenes, covering both: scenes where anchors appear on and scenes where no anchor appears. The results of the multimodal technique were significantly better when compared with the results from monomodal techniques applied separately. The tests were performed in four groups of Brazilian news programs obtained from two different television stations, containing five editions each, totaling twenty newscasts

Graduate stage design portfolio of Jessica Fialko

Crime scene examiners (CSEs) spend long hours at traumatic crime scenes, and interact closely with various elements of crime scenes. The topic has only recently attracted international research interest, and in the South African context the offering is meagre. The study set out to determine the profile of Southern Gauteng CSEs; to establish the scope of their tasks and responsibilities; to ascertain their trauma levels in relation to traumatic crime scenes and autopsies; to identify the specific coping strategies they use; to determine their context-specific experiences of job-related stress and trauma; and to ascertain the measure to which they experience contentedness within their workplace and their views on both briefing and debriefing. In pursuit of the objectives of the study, the research paradigm was rooted in positivism. Consequently, a quantitative approach was adopted and a cross-sectional design was employed. Through convenience sampling, 103 respondents were recruited from all eight Local Criminal Record Centres (LCRCs) in Southern Gauteng. The data gathering instrument was a paper-based, self-administered structured questionnaire, which included two standardised measuring instruments for trauma levels and coping strategies respectively. The Kolmogorov-Smirnov and Shapiro-Wilk tests indicated that non-parametric statistical procedures were required. Using the Mann Whitney U test, bivariate analysis allowed for testing the relationships between variables. The findings demonstrate that male CSEs outnumber female CSEs by approximately 3:1. CSEs attend violent crime scenes with striking regularity, but autopsies less frequently. Significant proportions of CSEs are routinely required to both take photographs of crime scenes and compile photo albums. A sizeable number of CSEs present with concerning levels of Post-traumatic Stress Disorder (PTSD) symptomology. The coping strategy adopted by majority of respondents is acceptance. Although attended less frequently, crime scenes involving deceased children are experienced as very distressing. A large proportion of CSEs are hesitant to access debriefing services, for numerous reasons, despite feeling the need to talk to someone about their work. The trauma experienced by CSEs is unique compared with other policing units. Therefore, it is recommended that trauma interventions should be responsive to their needs. Lastly, the CSEs who have been diagnosed with mental health conditions seem to be at significant risk, and thus they should receive special attention in future interventions. Keywords: crime scene examiner, Local Criminal Record Centres, trauma, traumatic event, crime scene, traumatic crime scene, emotional stress, coping strategy, autopsy, Post-traumatic Stress Disorder, Southern Gauteng. / Dissertation (MA (Criminology))--University of Pretoria, 2020. / Social Work and Criminology / MA (Criminology) / Restricted