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Authors: Jianxiong Liu ; Christos Bouganis and Peter Y. K. Cheung

Affiliation: Imperial College London, United Kingdom

Keyword(s): Progressive, Image Sampling, Kernel Regression.

Related Ontology Subjects/Areas/Topics: Computer Vision, Visualization and Computer Graphics ; Image Enhancement and Restoration ; Image Formation and Preprocessing ; Image Formation, Acquisition Devices and Sensors

Abstract: This paper presents an adaptive progressive image acquisition algorithm based on the concept of kernel construction. The algorithm takes the conventional route of blind progressive sampling to sample and reconstruct the ground truth image in an iterative manner. During each iteration, an equivalent kernel is built for each unsampled pixel to capture the spatial structure of its local neighborhood. The kernel is normalized by the estimated sample strength in the local area and used as the projection of the influence of this unsampled pixel to the consequent sampling procedure. The sampling priority of a candidate unsampled pixel is the sum of such projections from other unsampled pixels in the local area. Pixel locations with the highest priority are sampled in the next iteration. The algorithm does not require to pre-process or compress the ground truth image and therefore can be used in various situations where such procedure is not possible. The experiments show that the proposed a lgorithm is able to capture the local structure of images to achieve a better reconstruction quality than that of the existing methods. (More)

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Paper citation in several formats:
Liu, J.; Bouganis, C. and Cheung, P. (2014). Kernel-based Adaptive Image Sampling. In Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2014) - Volume 2: VISAPP; ISBN 978-989-758-003-1; ISSN 2184-4321, SciTePress, pages 25-32. DOI: 10.5220/0004653100250032

@conference{visapp14,
author={Jianxiong Liu. and Christos Bouganis. and Peter Y. K. Cheung.},
title={Kernel-based Adaptive Image Sampling},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2014) - Volume 2: VISAPP},
year={2014},
pages={25-32},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004653100250032},
isbn={978-989-758-003-1},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2014) - Volume 2: VISAPP
TI - Kernel-based Adaptive Image Sampling
SN - 978-989-758-003-1
IS - 2184-4321
AU - Liu, J.
AU - Bouganis, C.
AU - Cheung, P.
PY - 2014
SP - 25
EP - 32
DO - 10.5220/0004653100250032
PB - SciTePress

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