Thesis (M.F.A.)--Mississippi State University. Department of Art. / Accompanying digital video (mini DV), VHS format, is a copy of the edited documentary, "Crew 19," with a running time of 35 minutes. Accompanying CD-ROM, "384th Bomb Group, Inc.," includes related website and URL. Slides includes screen captures from the website and images from the video. Includes bibliographical references (leaves 19-21).

Thesis (M.Ed.)--York University, 2008. Graduate Programme in Education. / Typescript. Includes bibliographical references (leaves 138-147). Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL:http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&res_dat=xri:pqdiss&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft_dat=xri:pqdiss:MR45929

The objective of this Thesis research is to develop algorithms for temporally consistent semantic segmentation in videos. Though many different forms of semantic segmentations exist, this research is focused on the problem of temporally-consistent holistic scene understanding in outdoor videos. Holistic scene understanding requires an understanding of many individual aspects of the scene including 3D layout, objects present, occlusion boundaries, and depth. Such a description of a dynamic scene would be useful for many robotic applications including object reasoning, 3D perception, video analysis, video coding, segmentation, navigation and activity recognition. Scene understanding has been studied with great success for still images. However, scene understanding in videos requires additional approaches to account for the temporal variation, dynamic information, and exploiting causality. As a first step, image-based scene understanding methods can be directly applied to individual video frames to generate a description of the scene. However, these methods do not exploit temporal information across neighboring frames. Further, lacking temporal consistency, image-based methods can result in temporally-inconsistent labels across frames. This inconsistency can impact performance, as scene labels suddenly change between frames. The objective of our this study is to develop temporally consistent scene descriptive algorithms by processing videos efficiently, exploiting causality and data-redundancy, and cater for scene dynamics. Specifically, we achieve our research objectives by (1) extracting geometric context from videos to give broad 3D structure of the scene with all objects present, (2) Detecting occlusion boundaries in videos due to depth discontinuity, (3) Estimating depth in videos by combining monocular and motion features with semantic features and occlusion boundaries.

Wooden railway sleeper inspections in Sweden are currently performed manually by a human operator; such inspections are based on visual analysis. Machine vision based approach has been done to emulate the visual abilities of human operator to enable automation of the process. Through this process bad sleepers are identified, and a spot is marked on it with specific color (blue in the current case) on the rail so that the maintenance operators are able to identify the spot and replace the sleeper. The motive of this thesis is to help the operators to identify those sleepers which are marked by color (spots), using an “Intelligent Vehicle” which is capable of running on the track. Capturing video while running on the track and segmenting the object of interest (spot) through this vehicle; we can automate this work and minimize the human intuitions. The video acquisition process depends on camera position and source light to obtain fine brightness in acquisition, we have tested 4 different types of combinations (camera position and source light) here to record the video and test the validity of proposed method. A sequence of real time rail frames are extracted from these videos and further processing (depending upon the data acquisition process) is done to identify the spots. After identification of spot each frame is divided in to 9 regions to know the particular region where the spot lies to avoid overlapping with noise, and so on. The proposed method will generate the information regarding in which region the spot lies, based on nine regions in each frame. From the generated results we have made some classification regarding data collection techniques, efficiency, time and speed. In this report, extensive experiments using image sequences from particular camera are reported and the experiments were done using intelligent vehicle as well as test vehicle and the results shows that we have achieved 95% success in identifying the spots when we use video as it is, in other method were we can skip some frames in pre-processing to increase the speed of video but the segmentation results we reduced to 85% and the time was very less compared to previous one. This shows the validity of proposed method in identification of spots lying on wooden railway sleepers where we can compromise between time and efficiency to get the desired result.

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Lecture videos have proliferated in recent years thanks to the increasing bandwidths of Internet connections and availability of video cameras. Despite the massive volume of videos available, there are very few systems that parse useful information from them. Extracting meaningful data can help with searching and indexing of lecture videos as well as improve understanding and usability for the viewers. While video tags and user preferences are good indicators for relevant videos, it is completely dependent on human-generated data. Furthermore, many lecture videos are technical by nature and sparse video tags are too coarse-grained to relate parts of a video by a specific topic. While extracting the text from the presentation slide will ameliorate this issue, a lecture video still contains significantly more information than what is just available on the presentation slides. That is, the actions and words of the speaker contribute to a richer and more nuanced understanding of the lecture material. The goal of the Semantically Linked Instructional Content (SLIC) project is to relate videos using more specific and relevant features such as slide text and other entities. In this work, we will present the algorithms used to recognize the entities of the lecture. Specifically, the entities in lecture videos are the laser and pointing hand gestures and the location of the slide and its text and images in the video. Our algorithms work under the assumption that the slide location (homography) is known for each frame and extend the knowledge of the scene. Specifically, gestures inform when and where on a slide notable events occur. We will also show how recognition of these entities can help with understanding lectures better and energy-savings on mobile devices. We conducted a user study that shows that magnifying text based on laser gestures on a slide helps direct a viewer's attention to the relevant text. We also performed empirical measurements on real cellphones to confirm that selectively dimming less relevant regions of the video frame would reduce energy consumption significantly.

This work fills a gap in research on Indonesian popular culture by delving into the presentation of femininity and masculinity in Indonesian music videos. Through a textual analysis of four videos, a survey of the video YouTube comments, and interviews with Indonesians about these videos, the author examines the presentation of Order/Chaos and other Male/Female binaries in the music videos and their relationship with the current pop culture and political environment.

In this study we will explore impact exposure to brands references in music videos may have on the development of consumers’ brand knowledge. We assert that an understanding of this relationship is a function of both executional elements of the message and the intervening effects of select individual-difference factors. This dissertation applies social cognitive theories, the cultivation hypothesis, attribution theory and the elaboration likelihood model to develop the set of hypotheses. This dissertation seeks to provide initial evidence regarding the key factors brand managers and music executives must be aware of when implementing music video brand placements. A conceptual model of music video brand placement is presented and evaluated utilizing qualitative and quantitative techniques. The qualitative methodology employs real music fans as informants and music videos as stimuli in developing an understanding of the relationship consumers have with music as well as their reactions to music videos. The quantitative methodology uses an original music video as the stimulus, real music fans as respondents and a real-time on-line survey to measure the relationship among the variables. Study findings support the ability of music videos to impact extra-musical consumption and provide early evidence regarding factors important to understanding consumers’ responses to music video brand placements.

En este trabajo se desarrolla un Plan Estratégico para una cadena de tiendas dedicada al alquiler y venta de videos. El objetivo es colocarla como líder en el mercado nacional. Previamente se hace una presentación sucinta y didáctica de los conceptos fundamentales de la Planificación Estratégica. Se formula, además, un modelo básico de Programación Lineal para la Toma de Decisiones, el cual ha sido probado con datos reales. / --- In this work a Strategic Plan is developed for a chain of stores dedicated to the rent and sale of videos. The objective is to place it as leader in the domestic market. Previously it is made a short presentation and didactics of the fundamental concepts of the Strategic Planning. It is formulated, also, a basic model of Linear programming for the taking of Decisions, which has been proven with real data.

In this study we will explore impact exposure to brands references in music videos may have on the development of consumers’ brand knowledge. We assert that an understanding of this relationship is a function of both executional elements of the message and the intervening effects of select individual-difference factors. This dissertation applies social cognitive theories, the cultivation hypothesis, attribution theory and the elaboration likelihood model to develop the set of hypotheses. This dissertation seeks to provide initial evidence regarding the key factors brand managers and music executives must be aware of when implementing music video brand placements. A conceptual model of music video brand placement is presented and evaluated utilizing qualitative and quantitative techniques. The qualitative methodology employs real music fans as informants and music videos as stimuli in developing an understanding of the relationship consumers have with music as well as their reactions to music videos. The quantitative methodology uses an original music video as the stimulus, real music fans as respondents and a real-time on-line survey to measure the relationship among the variables. Study findings support the ability of music videos to impact extra-musical consumption and provide early evidence regarding factors important to understanding consumers’ responses to music video brand placements.