Segmentation is one of the most important low-level tasks in image processing as it enables many higher level computer vision tasks like object recognition and tracking. Segmentation can also be exploited for image compression using recent graph-based algorithms, provided that the corresponding contours can be represented efficiently. Transmission of borders is also key to distributed computer vision. In this paper we propose a new chain code tailored to compress segmentation contours. Based on the widely known 3OT, our algorithm is able to encode regions avoiding borders it has already coded once and without the need of any starting point information for each region. We tested our method against three other state of the art chain codes over the BSDS500 dataset, and we demonstrated that the proposed chain code achieves the highest compression ratio, resulting on average in over 27% bit-per-pixel saving.

Efficient representation of segmentation contours using chain codes

VERDOJA, FRANCESCO;GRANGETTO, Marco
2017

Abstract

Segmentation is one of the most important low-level tasks in image processing as it enables many higher level computer vision tasks like object recognition and tracking. Segmentation can also be exploited for image compression using recent graph-based algorithms, provided that the corresponding contours can be represented efficiently. Transmission of borders is also key to distributed computer vision. In this paper we propose a new chain code tailored to compress segmentation contours. Based on the widely known 3OT, our algorithm is able to encode regions avoiding borders it has already coded once and without the need of any starting point information for each region. We tested our method against three other state of the art chain codes over the BSDS500 dataset, and we demonstrated that the proposed chain code achieves the highest compression ratio, resulting on average in over 27% bit-per-pixel saving.
2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
New Orleans, LA
2017
Acoustics, Speech and Signal Processing (ICASSP), 2017 IEEE International Conference on
IEEE
1462
1466
978-1-5090-4117-6
978-1-5090-4118-3
http://ieeexplore.ieee.org/document/7952399/
Verdoja, Francesco; Grangetto, Marco
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1643753
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