994 resultados para EXTRACTION COLUMNS


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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 addresses the challenge of bridging the semantic gap that exists between the simplicity of features that can be currently computed in automated content indexing systems and the richness of semantics in user queries posed for media search and retrieval. It proposes a unique computational approach to extraction of expressive elements of motion pictures for deriving high-level semantics of stories portrayed, thus enabling rich video annotation and interpretation. This approach, motivated and directed by the existing cinematic conventions known as film grammar, as a first step toward demonstrating its effectiveness, uses the attributes of motion and shot length to define and compute a novel measure of tempo of a movie. Tempo flow plots are defined and derived for a number of full-length movies and edge analysis is performed leading to the extraction of dramatic story sections and events signaled by their unique tempo. The results confirm tempo as a useful high-level semantic construct in its own right and a promising component of others such as rhythm, tone or mood of a film. In addition to the development of this computable tempo measure, a study is conducted as to the usefulness of biasing it toward either of its constituents, namely, motion or shot length. Finally, a refinement is made to the shot length normalizing mechanism, driven by the peculiar characteristics of shot length distribution exhibited by movies. Results of these additional studies, and possible applications and limitations are discussed.

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Demand for new and novel natural compounds has intensified the development of plant-derived compounds known as bioactives that either promote health or are toxic when ingested. Enhanced release of these bioactives from plant cells by cell disruption and extraction through the cell wall can be optimized using enzyme preparations either alone or in mixtures. However, the biotechnological application of enzymes is not currently exploited to its maximum potential within the food industry. Here, we discuss the use of environmentally friendly enzyme-assisted extraction of bioactive compounds from plant sources, particularly for food and nutraceutical purposes. In particular, we discuss an enzyme-assisted extraction of stevioside from Stevia rebaudiana, as an example of a process of potential value to the food industry.

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This paper proposes a unique computational approach to extraction of expressive elements of motion pictures for deriving high level semantics of stories portrayed, thus enabling better video annotation and interpretation systems. This approach, motivated and directed by the existing cinematic conventions known as film grammar, as a first step towards demonstrating its effectiveness, uses the attributes of motion and shot length to define and compute a novel measure of tempo of a movie. Tempo flow plots are defined and derived for four full-length movies and edge analysis is performed leading to the extraction of dramatic story sections and events signaled by their unique tempo. The results confirm tempo as a useful attribute in its own right and a promising component of semantic constructs such as tone or mood of a film.

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In order to enable high-level semantics-based video 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 film production to determine when a scene change occurs in film. We examine different rules and conventions followed as part of Film Grammar to guide and shape our algorithmic solution for determining a scene boundary. Two different techniques are proposed as new solutions in this paper. Our experimental results on 10 full-length movies show that our technique based on shot sequence coherence performs well and reasonably better than the color edges-based approach.

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Background elimination models are widely used in motion tracking systems. Our aim is to develop a system that performs reliably under adverse lighting conditions. In particular, this includes indoor scenes lit partly or entirely by diffuse natural light. We present a modified "median value" model in which the detection threshold adapts to global changes in illumination. The responses of several models are compared, demonstrating the effectiveness of the new model.

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In this paper, we propose novel computational models for the extraction of high level expressive constructs related to, namely thematic and dramatic functions of the content shown in educational and training videos. Drawing on the existing knowledge of film theory, and media production rules and conventions used by the filmmakers. we hypothesize key aesthetic elements contributing to convey these functions of the content. Computational models to extract them are then formulated and their performance evaluated on a set of ten educational and training videos is presented.

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Precision edge feature extraction is a very important step in vision, Researchers mainly use step edges to model an edge at subpixel level. In this paper we describe a new technique for two dimensional edge feature extraction to subpixel accuracy using a general edge model. Using six basic edge types to model edges, the edge parameters at subpixel level are extracted by fitting a model to the image signal using least-.squared error fitting technique.

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This paper examines film rhythm, an important expressive element in motion pictures, based on our ongoing study to exploit film grammar as a broad computational framework for the task of automated film and video understanding. Of the many, more or less elusive, narrative devices contributing to film rhythm, this paper discusses motion characteristics that form the basis of our analysis, and presents novel computational models for extracting rhythmic patterns induced through a perception of motion. In our rhythm model, motion behaviour is classified as being either nonexistent, fluid or staccato for a given shot. Shot neighbourhoods in movies are then grouped by proportional makeup of these motion behavioural classes to yield seven high-level rhythmic arrangements that prove to be adept at indicating likely scene content (e.g. dialogue or chase sequence) in our experiments. Underlying causes for this level of codification in our approach are postulated from film grammar, and are accompanied by detailed demonstration from real movies for the purposes of clarification.