969 resultados para Motion pictures in schools


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Mode of access: Internet.

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In the movie industry, the extraordinarily successful theatrical performance of certain films is largely attributed to buzz. Despite longstanding commentary about the role of buzz in successful movie marketing and the belief that it accelerates new product diffusion, limited scholarly evidence exists to support these assertions. This is primarily due to the lack of conceptual distinction of buzz from word-of-mouth, which is often used as the main basis for conceptualising buzz. However, word-of-mouth does not fully explain the buzz surrounding films such as 'Gone With The Wind', 'The Dark Knight' and 'Avatar'. Informed by valuable insights from key experts who have launched some of the most successful movies in box office history, as well as a range of moviegoers, this thesis developed a deeper understanding of what buzz is and how it is created. This thesis concludes that buzz is not the same as word-of-mouth.

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This article examines Len Lye’s film-making in the 1930s within a broader visual arts context, seeking to clarify the nature and extent of his involvement in British documentary film culture at this time. In particular, it demonstrates how Lye's method of fusing 'live action', found footage, and animation techniques created the possibility of a radical documentary practice that could reconcile promotional advertising and commercial art with avant-garde abstraction and kinaesthetic experimentation. In particular, the article focusses on Lye's N. or N.W. (1937, 35mm, b&w, 10 mns), arguing that his work from this period should be regarded as central - and not marginal - to any serious reassessment of Britain's “Documentary Movement” of the inter-war era, and its relations to any history of the cinema and visual culture.

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This work constitutes the first attempt to extract the important narrative structure, the 3-Act storytelling paradigm in film. Widely prevalent in the domain of film, it forms the foundation and framework in which a film can be made to function as an effective tool for story telling, and its extraction is a vital step in automatic content management for film data. The identification of act boundaries allows for structuralizing film at a level far higher than existing segmentation frameworks, which include shot detection and scene identification, and provides a basis for inferences about the semantic content of dramatic events in film. A novel act boundary likelihood function for Act 1 and 2 is derived using a Bayesian formulation under guidance from film grammar, tested under many configurations and the results are reported for experiments involving 25 full-length movies. The result proves to be a useful tool in both the automatic and semi-interactive setting for semantic analysis of film, with potential application to analogues occuring in many other domains, including news, training video, sitcoms.

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This paper addresses the challenge of bridging the semantic gap between the rich meaning users desire when they query to locate and browse media and the shallowness of media descriptions that can be computed in today's content management systems. To facilitate high-level semantics-based content annotation and interpretation, we tackle the problem of automatic decomposition of motion pictures into meaningful story units, namely scenes. Since a scene is a complicated and subjective concept, we first propose guidelines from fill production to determine when a scene change occurs. We then investigate different rules and conventions followed as part of Fill Grammar that would guide and shape an algorithmic solution for determining a scene. Two different techniques using intershot analysis are proposed as solutions in this paper. In addition, we present different refinement mechanisms, such as film-punctuation detection founded on Film Grammar, to further improve the results. These refinement techniques demonstrate significant improvements in overall performance. Furthermore, we analyze errors in the context of film-production techniques, which offer useful insights into the limitations of our method.

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This paper presents an original computational approach to extraction of movie tempo for deriving story sections and events that convey high level semantics of stories portrayed in motion pictures, thus enabling better video annotation and interpretation systems. This approach, inspired by the existing cinematic conventions known as film grammar, uses the attributes of motion and shot length to define and compute a novel continuous measure of tempo of a movie. Tempo flow plots are derived for several full-length motion pictures and edge detection is performed to extract dramatic story sections and events occurring in the movie, underlined by their unique tempo. The results confirm reliable detection of actual distinct tempo changes and serve as useful index into the dramatic development and narration of the story in motion pictures.

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This work constitutes the first attempt to extract an important narrative structure, the 3-Act story telling paradigm, in film. This narrative structure is prevalent in the domain of film as it forms the foundation and framework in which the film can be made to function as an effective tool for story telling, and its extraction is a vital step in automatic content management for film data. A novel act boundary likelihood function for Act 1 is derived using a Bayesian formulation under guidance from film grammar, tested under many configurations and the results are reported for experiments involving 25 full length movies. The formulation is shown to be a useful tool in both the automatic and semi-interactive setting for semantic analysis of film.

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Motivated by existing cinematic conventions known as film grammar, we proposed a computational approach to determine tempo as a high-level movie content descriptor as well as means for deriving dramatic story sections and events occurring in movies. Movie tempo is extracted from two easily computed aspects in our approach: shot length and motion. Story sections and events are generally associated with changes in tempo, and are thus identified by edges located in the tempo function. In this paper, we analyze our initial founding of the tempo function on the basis that the distribution of both shot length and motion in movies is normal. Given that the distribution of shot length is approximately Weibull as confirmed in our experiments, we examine the impact of modelling and modifying the contributions of shot length to tempo. We derive an appropriate normalization function that faithfully encapsulates the role of shot length in tempo perception, and analyze the changes to the story sections identified in films.

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To enable high-level semantic indexing of video, we tackle the problem of automatically structuring motion pictures into meaningful story units, namely scenes. In our recent work, drawing guidance from film grammar, we proposed an algorithmic solution for extracting scenes in motion pictures based on a shot neighborhood color coherence measure. In this paper, we extend our work by presenting various refinement mechanisms, inspired by the knowledge of film devices that are brought to bear while crafting scenes, to further improve the results of the scene detection algorithm. We apply the enhanced algorithm to ten motion pictures and demonstrate the resulting improvements in performance.