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» » Media Computing: Computational Media Aesthetics (The International Series in Video Computing)
Media Computing: Computational Media Aesthetics (The International Series in Video Computing) e-book

Author:

Chitra Dorai,Svetha Venkatesh

Language:

English

Category:

IT

Subcategory:

Computer Science

ePub size:

1547 kb

Other formats:

docx lrf doc azw

Rating:

4.7

Publisher:

Springer; 2002 edition (June 30, 2002)

Pages:

198

ISBN:

1402071027

Media Computing: Computational Media Aesthetics (The International Series in Video Computing) e-book

by Chitra Dorai,Svetha Venkatesh


Computer graphics, image processing, and video databases have obvious overlap with computer . Computational Media Aesthetics. The International Series in Video Computing.

Computer graphics, image processing, and video databases have obvious overlap with computer vision. The main goal of computer graphics is to gener­ ate and animate realistic looking images, and videos.

It will also be of interest to those working in signal processing, image processing, computer vision, audio analysis, and speech processing.

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2 media computing: computational media aesthetics. oceedings{Shah2002MediaC, title {Media Computing}, author {Mubarak Shah and Chitra Dorai and Svetha Venkatesh}, booktitle {The Springer International Series in Video Computing}, year {2002} }. Mubarak Shah, Chitra Dorai, Svetha Venkatesh. Published in. The Springer International Series in Vide. 002. A good example of this is image-based rendering and modeling techniques, in which geometry, appearance, and lighting is de rived from real images using computer vision techniques.

Computational Media Aesthetics. Chapter · January 2016 with 3 Reads. This paper proposes a novel framework for automatic chapter generation of consumer videos, in which the knowledge of media aesthetics and video production is utilized. How we measure 'reads'. For an input video, a set of specific visual and aural aesthetic features, called expressive elements, is first extracted. Then a high-level structure with aesthetic semantics, namely aesthetic intensity, is obtained and used to generate the corresponding video chapters. Experimental results demonstrate the effectiveness and efficiency of the proposed approach.

Other publications that explore media aesthetics are, and

Other publications that explore media aesthetics are, and. The problem that we perceive from this approach is that each person will interpret the content information in a different way, and thus, users which don't have the same sensori-emotional values as those defined by the technique will have the possibility to not being satisfied. Mood detection is implemented on the viewpoint of computational media aesthetics, that is, by analyzing two music dimensions, tempo and articulation, in the procedure of making music, we derive four categories of mood, happiness, anger, sadness and fear.

Media Computing : Computational Media Aesthetics. For instance, the original goal of computer vision was to understand a single image of a scene, by identifying objects, their structure, and spatial arrangements

Media Computing : Computational Media Aesthetics. For instance, the original goal of computer vision was to understand a single image of a scene, by identifying objects, their structure, and spatial arrangements. This has been referred to as image understanding.

Series: The International Series in Video Computing (Book 6). Hardcover: 340 pages.

International Book Series on Video Computing .

Multimodal Video Characterization and Summarization Michael A. Smith and Takeo Kanade, October, 2004. 3D Face Processing: Modeling, Analysis and Synthesis Zhen Wen and Thomas Huang, June 2004. Content-Based Image and Video Retrieval Xian Sean Zhu and Thomas Huang, August 2003. Media Computing: Computational Media Aesthetics Chitra Dorai and Svetha Venkatesh, April 2002. Analyzing Video Sequences of Multiple Humans Jun Ohya, Akira Utsumi, and Junjo Yamato, March 2002.

Traditionally, scientific fields have defined boundaries, and scientists work on research problems within those boundaries. However, from time to time those boundaries get shifted or blurred to evolve new fields. For instance, the original goal of computer vision was to understand a single image of a scene, by identifying objects, their structure, and spatial arrangements. This has been referred to as image understanding. Recently, computer vision has gradually been making the transition away from understanding single images to analyz­ ing image sequences, or video understanding. Video understanding deals with understanding of video sequences, e. g. , recognition of gestures, activities, fa­ cial expressions, etc. The main shift in the classic paradigm has been from the recognition of static objects in the scene to motion-based recognition of actions and events. Video understanding has overlapping research problems with other fields, therefore blurring the fixed boundaries. Computer graphics, image processing, and video databases have obvious overlap with computer vision. The main goal of computer graphics is to gener­ ate and animate realistic looking images, and videos. Researchers in computer graphics are increasingly employing techniques from computer vision to gen­ erate the synthetic imagery. A good example of this is image-based rendering and modeling techniques, in which geometry, appearance, and lighting is de­ rived from real images using computer vision techniques. Here the shift is from synthesis to analysis followed by synthesis.

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