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Automatic image retargeting
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Source ACM International Conference Proceeding Series; Vol. 154 archive
Proceedings of the 4th international conference on Mobile and ubiquitous multimedia table of contents
Christchurch, New Zealand
SESSION: Graphics 1 table of contents
Pages: 59 - 68  
Year of Publication: 2005
ISBN:0-473-10658-2
Authors
Vidya Setlur  Northwestern University and Nokia Research Center
Saeko Takagi  Wakayama University
Ramesh Raskar  Mitsubishi Electric Research Laboratories (MERL)
Michael Gleicher  University of Wisconsin, Madison
Bruce Gooch  Northwestern University
Publisher
ACM  New York, NY, USA
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ABSTRACT

We present a non-photorealistic algorithm for retargeting large images to small size displays, particularly on mobile devices. This method adapts large images so that important objects in the image are still recognizable when displayed at a lower target resolution. Existing image manipulation techniques such as cropping works well for images containing a single important object, and down-sampling works well for images containing low frequency information. However, when these techniques are automatically applied to images with multiple objects, the image quality degrades and important information may be lost. Our algorithm addresses the case of multiple important objects in an image. The retargeting algorithm segments an image into regions, identifies important regions, removes them, fills the resulting gaps, resizes the remaining image, and re-inserts the important regions. Our approach lies in constructing a topologically constrained epitome of an image based on a visual attention model that is both comprehensible and size varying, making the method suitable for display-critical applications.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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CITED BY  11

Collaborative Colleagues:
Vidya Setlur: colleagues
Saeko Takagi: colleagues
Ramesh Raskar: colleagues
Michael Gleicher: colleagues
Bruce Gooch: colleagues