A Comparative Review of Component Tree Computation Algorithms

Authors

Edwin Carlinet, Thierry Géraud

Where

IEEE Transactions on Image Processing

Type

inproceedings

Keywords

Image

Date

2014-06-16

Abstract

Connected operators are morphological tools that have the property of filtering images without creating new contours and without moving the contours that are preserved. Those operators are related to the max-tree and min-tree repre- sentations of images, and many algorithms have been proposed to compute those trees. However, no exhaustive comparison of these algorithms has been proposed so farand the choice of an algorithm over another depends on many parameters. Since the need for fast algorithms is obvious for production code, we present an in-depth comparison of the existing algorithms in a unique framework, as well as variations of some of them that improve their efficiency. This comparison involves both sequential and parallel algorithms, and execution times are given with respect to the number of threads, the input image size, and the pixel value quantization. Eventuallya decision tree is given to help the user choose the most appropriate algorithm with respect to the user requirements. To favor reproducible research, an online demo allows the user to upload an image and bench the different algorithms, and the source code of every algorithms has been made available.

Publications/carlinet.13.ismm.inc

Bibtex (lrde.bib)

@InProceedings{	  carlinet.14.itip,
  author	= {Edwin Carlinet and Thierry G\'eraud},
  title		= {A Comparative Review of Component Tree Computation
		  Algorithms},
  booktitle	= {IEEE Transactions on Image Processing},
  year		= 2014,
  pages		= {1--11},
  project	= {Image},
  abstract	= {Connected operators are morphological tools that have the
		  property of filtering images without creating new contours
		  and without moving the contours that are preserved. Those
		  operators are related to the max-tree and min-tree repre-
		  sentations of images, and many algorithms have been
		  proposed to compute those trees. However, no exhaustive
		  comparison of these algorithms has been proposed so far,
		  and the choice of an algorithm over another depends on many
		  parameters. Since the need for fast algorithms is obvious
		  for production code, we present an in-depth comparison of
		  the existing algorithms in a unique framework, as well as
		  variations of some of them that improve their efficiency.
		  This comparison involves both sequential and parallel
		  algorithms, and execution times are given with respect to
		  the number of threads, the input image size, and the pixel
		  value quantization. Eventually, a decision tree is given to
		  help the user choose the most appropriate algorithm with
		  respect to the user requirements. To favor reproducible
		  research, an online demo allows the user to upload an image
		  and bench the different algorithms, and the source code of
		  every algorithms has been made available.},
  note		= {Accepted}
}