Detail výsledku
Efficient tree construction for multiscale image representation and processing
With the continuous growth of sensor performances,
image analysis and processing algorithms have to
cope with larger and larger data volumes. Besides, the
informative components of an image might not be the
pixels themselves, but rather the objects they belong to.
This has led to a wide range of successful multiscale
techniques in image analysis and computer vision. Hierarchical
representations are thus of first importance, and
require efficient algorithms to be computed in order to
address real-life applications. Among these hierarchical
models, we focus on morphological trees (e.g., min/maxtree,
tree of shape, binary partition tree, a-tree) that come
with interesting properties and already led to appropriate
techniques for image processing and analysis, with a
growing interest from the image processing community.
More precisely, we build upon two recent algorithms for
efficient a-tree computation and introduce several
improvements to achieve higher performance. We also
discuss the impact of the data structure underlying the tree
representation, and provide for the sake of illustration
several applications where efficient multiscale image representation
leads to fast but accurate techniques, e.g., in
remote sensing image analysis or video segmentation.
Multiscale representation, Connected operators, Alpha-tree, Parallelization, Map-reduce
@article{BUT130992,
author="Jiří {Havel} and Francois {Merciol} and Sebastien {Lefevre}",
title="Efficient tree construction for multiscale image representation and processing",
journal="Journal of Real-Time Image Processing",
year="2016",
volume="16",
number="4",
pages="1129--1146",
doi="10.1007/s11554-016-0604-0",
issn="1861-8200",
url="http://link.springer.com/article/10.1007/s11554-016-0604-0"
}