999 resultados para Adapted Art


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Later published under the title: The art of discourse.

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Mode of access: Internet.

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For over half a century art directors within the advertising industry have been adapting to the changes occurring in media, culture and the corporate sector, toward enhancing professional performance and competitiveness. These professionals seldom offer explicit justification about the role images play in effective communication. It is uncertain how this situation affects advertising performance, because advertising has, nevertheless, evolved in parallel to this as an industry able to fabricate new opportunities for itself. However, uncertainties in the formalization of art direction knowledge restrict the possibilities of knowledge transfer in higher education. The theoretical knowledge supporting advertising art direction has been adapted spontaneously from disciplines that rarely focus on specific aspects related to the production of advertising content, like, for example: marketing communication, design, visual communication, or visual art. Meanwhile, in scholarly research, vast empirical knowledge has been generated about advertising images, but often with limited insight into production expertise. Because art direction is understood as an industry practice and not as an academic discipline, an art direction perspective in scholarly contributions is rare. Scholarly research that is relevant to art direction seldom offers viewpoints to help understand how it is that research outputs may specifically contribute to art direction practices. This thesis is dedicated to formally understanding the knowledge underlying art direction and using it to explore models for visual analysis and knowledge transfer in higher education. The first three chapters of this thesis offer, firstly, a review of practical and contextual aspects that help define art direction, as a profession and as a component in higher education; secondly, a discussion about visual knowledge; and thirdly, a literature review of theoretical and analytic aspects relevant to art direction knowledge. Drawing on these three chapters, this thesis establishes explicit structures to help in the development of an art direction curriculum in higher education programs. Following these chapters, this thesis explores a theoretical combination of the terms ‘aesthetics’ and ‘strategy’ as foundational notions for the study of art direction. The theoretical exploration of the term ‘strategic aesthetics’ unveils the potential for furthering knowledge in visual commercial practices in general. The empirical part of this research explores ways in which strategic aesthetics notions can extend to methodologies of visual analysis. Using a combination of content analysis and of structures of interpretive analysis offered in visual anthropology, this research discusses issues of methodological appropriation as it shifts aspects of conventional methodologies to take into consideration paradigms of research that are producer-centred. Sampled out of 2759 still ads from the online databases of Cannes Lions Festival, this study uses an instrumental case study of love-related advertising to facilitate the analysis of content. This part of the research helps understand the limitations and functionality of the theoretical and methodological framework explored in the thesis. In light of the findings and discussions produced throughout the thesis, this project aims to provide directions for higher education in relation to art direction and highlights potential pathways for further investigation of strategic aesthetics.

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This paper offers insight into the development of a PhD in advertising art direction. For over half a century art directors within the advertising industry have been adapting to the changes occurring in media, culture and the corporate sector, toward enhancing professional performance and competitiveness. These professionals seldom offer explicit justification about the role images play in effective communication. It is uncertain how this situation affects advertising performance, because advertising has, nevertheless, evolved in parallel to this as an industry able to fabricate new opportunities for itself. However, uncertainties in the formalization of art direction knowledge restrict the possibilities of knowledge transfer in higher education. The theoretical knowledge supporting advertising art direction has been adapted spontaneously from disciplines that rarely focus on specific aspects related to the production of advertising content, like, for example: marketing communication, design, visual communication, or visual art. Meanwhile, in scholarly research, vast empirical knowledge has been generated about advertising images, but often with limited insight into production expertise. Because art direction is understood as an industry practice and not as an academic discipline, an art direction perspective in scholarly contributions is rare. Scholarly research that is relevant to art direction seldom offers viewpoints to help understand how it is that research outputs may specifically contribute to art direction practices. There is a need to formally understanding the knowledge underlying art direction and using it to explore models for visual analysis and knowledge transfer in higher education. This paper provides insight into the development of a thesis that explored this need. The PhD thesis to which this paper refers is Strategic Aesthetics in Advertising Campaigns: Implications for Art Direction Education.

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This paper describes the ground target detection, classification and sensor fusion problems in distributed fiber seismic sensor network. Compared with conventional piezoelectric seismic sensor used in UGS, fiber optic sensor has advantages of high sensitivity and resistance to electromagnetic disturbance. We have developed a fiber seismic sensor network for target detection and classification. However, ground target recognition based on seismic sensor is a very challenging problem because of the non-stationary characteristic of seismic signal and complicated real life application environment. To solve these difficulties, we study robust feature extraction and classification algorithms adapted to fiber sensor network. An united multi-feature (UMF) method is used. An adaptive threshold detection algorithm is proposed to minimize the false alarm rate. Three kinds of targets comprise personnel, wheeled vehicle and tracked vehicle are concerned in the system. The classification simulation result shows that the SVM classifier outperforms the GMM and BPNN. The sensor fusion method based on D-S evidence theory is discussed to fully utilize information of fiber sensor array and improve overall performance of the system. A field experiment is organized to test the performance of fiber sensor network and gather real signal of targets for classification testing.