A Dense Medial Descriptor for Image Analysis

Matthew van der Zwan, Yuri Meiburg, Alexandru Telea

2013

Abstract

We present dense medial descriptors, a new technique which generalizes the well-known medial axes to encode and manipulate whole 2D grayvalue images, rather than binary shapes. To compute our descriptors, we first reduce an image to a set of threshold-sets in luminance space. Next, we compute a simplified representation of each threshold-set using a noise-resistant medial axis transform. Finally, we use these medial axis transforms to perform a range of operations on the input image, from perfect reconstruction to segmentation, simplification, and artistic effects. Our pipeline can robustly handle any 2D grayscale image, is easy to use, and allows an efficient CPU or GPU-based implementation. We demonstrate our dense medial descriptors with several image-processing applications.

Paper Citation

in Harvard Style

Zwan M., Meiburg Y. and Telea A. (2013). A Dense Medial Descriptor for Image Analysis . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013) ISBN 978-989-8565-47-1, pages 285-293. DOI: 10.5220/0004279202850293

in Bibtex Style

@conference{visapp13,
author={Matthew van der Zwan and Yuri Meiburg and Alexandru Telea},
title={A Dense Medial Descriptor for Image Analysis},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013)},
year={2013},
pages={285-293},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004279202850293},
isbn={978-989-8565-47-1},
}

in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013)
TI - A Dense Medial Descriptor for Image Analysis
SN - 978-989-8565-47-1
AU - Zwan M.
AU - Meiburg Y.
AU - Telea A.
PY - 2013
SP - 285
EP - 293
DO - 10.5220/0004279202850293