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|Abstract|Algorithms|Results|Conclusion|References|

   ABSTRACT   

Amateur home video makers are unaware of the principles of cinema grammar. The videos they shoot are meant to convey an intent. But often, their inexperience, ignorance, lack of editing skills, or the limitations of the means of video capture they have at their disposal belie their intentions. Intent delivery techniques introduced in this paper can aid such footage and convey a range of evident intents as well as those not intended initially, by using principles of cinema grammar, aesthetics and video analysis. In this paper, a general approach for video intent delivery is described with respect to a brief catalog of the intentions of the cinematographer and editor. Then four basic examples of delivering intents: cheer, serenity, gloom and excitement through pieces of raw footage are presented. Thus, we seek the theoretical support from video grammar and cinematic rules, in order to model computable features for repurposing home videos.

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  ALGORITHMS  

    RESULTS   
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ORIGINAL CLIPS

CHEER

GLOOM

SERENITY/ TIME PASSAGE

EXCITEMENT

Nadia

Cheer-nadia

Gloom-nadia

Serene-nadia

Excite-nadia

Wagon

Cheer-wagon

Gloom-wagon

Serene-wagon

Excite-wagon

Walk

Cheer-walk

Gloom-walk

Serene-walk

Excite-walk

Warrior

Cheer-warrior

Gloom-warrior

Serene-warrior

Excite-warrior

  CONCLUSION 

This paper imparts users' intent to an input video clip without requiring the user to be proficient either with computers or with cinema grammar. It uses simple video processing tools on raw video to generate video clips that conform to cinema grammar rules. Currently it only relies on some simple low level properties of the video. It does not require any complicated semantic understanding of the video; The user may have to use the
individual image processing options to get the video intents he wants. We select the computable features from human intents and digital videos, thus we are successful in reducing the dependence on ad-hoc method in media aesthetics.
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| Abstract | Algorithms | Results | Conclusion | References |

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Latest Update: 2005-06-20