Efficient scan mask techniques for connected components labeling algorithm

<p>Abstract</p> <p>Block-based connected components labeling is by far the fastest algorithm to label the connected components in 2D binary images, especially when the image size is quite large. This algorithm produces a decision tree that contains 211 leaf nodes with 14 levels for...

Full description

Bibliographic Details
Main Authors: Sutheebanjard Phaisarn, Premchaiswadi Wichian
Format: Article
Language:English
Published: SpringerOpen 2011-01-01
Series:EURASIP Journal on Image and Video Processing
Subjects:
Online Access:http://jivp.eurasipjournals.com/content/2011/1/14
id doaj-a6b34349cd664321afc1bc4963dadf1c
record_format Article
spelling doaj-a6b34349cd664321afc1bc4963dadf1c2020-11-25T02:18:57ZengSpringerOpenEURASIP Journal on Image and Video Processing1687-51761687-52812011-01-012011114Efficient scan mask techniques for connected components labeling algorithmSutheebanjard PhaisarnPremchaiswadi Wichian<p>Abstract</p> <p>Block-based connected components labeling is by far the fastest algorithm to label the connected components in 2D binary images, especially when the image size is quite large. This algorithm produces a decision tree that contains 211 leaf nodes with 14 levels for the depth of a tree and an average depth of 1.5923. This article attempts to provide a faster method for connected components labeling. We propose two new scan masks for connected components labeling, namely, the pixel-based scan mask and the block-based scan mask. In the final stage, the block-based scan mask is transformed to a near-optimal decision tree. We conducted comparative experiments using different sources of images for examining the performance of the proposed method against the existing methods. We also performed an average tree depth analysis and tree balance analysis to consolidate the performance improvement over the existing methods. Most significantly, the proposed method produces a decision tree containing 86 leaf nodes with 12 levels for the depth of a tree and an average depth of 1.4593, resulting in faster execution time, especially when the foreground density is equal to or greater than the background density of the images.</p> http://jivp.eurasipjournals.com/content/2011/1/14connected componentsimage processinglabeling algorithmlinear time algorithmpattern recognition.
collection DOAJ
language English
format Article
sources DOAJ
author Sutheebanjard Phaisarn
Premchaiswadi Wichian
spellingShingle Sutheebanjard Phaisarn
Premchaiswadi Wichian
Efficient scan mask techniques for connected components labeling algorithm
EURASIP Journal on Image and Video Processing
connected components
image processing
labeling algorithm
linear time algorithm
pattern recognition.
author_facet Sutheebanjard Phaisarn
Premchaiswadi Wichian
author_sort Sutheebanjard Phaisarn
title Efficient scan mask techniques for connected components labeling algorithm
title_short Efficient scan mask techniques for connected components labeling algorithm
title_full Efficient scan mask techniques for connected components labeling algorithm
title_fullStr Efficient scan mask techniques for connected components labeling algorithm
title_full_unstemmed Efficient scan mask techniques for connected components labeling algorithm
title_sort efficient scan mask techniques for connected components labeling algorithm
publisher SpringerOpen
series EURASIP Journal on Image and Video Processing
issn 1687-5176
1687-5281
publishDate 2011-01-01
description <p>Abstract</p> <p>Block-based connected components labeling is by far the fastest algorithm to label the connected components in 2D binary images, especially when the image size is quite large. This algorithm produces a decision tree that contains 211 leaf nodes with 14 levels for the depth of a tree and an average depth of 1.5923. This article attempts to provide a faster method for connected components labeling. We propose two new scan masks for connected components labeling, namely, the pixel-based scan mask and the block-based scan mask. In the final stage, the block-based scan mask is transformed to a near-optimal decision tree. We conducted comparative experiments using different sources of images for examining the performance of the proposed method against the existing methods. We also performed an average tree depth analysis and tree balance analysis to consolidate the performance improvement over the existing methods. Most significantly, the proposed method produces a decision tree containing 86 leaf nodes with 12 levels for the depth of a tree and an average depth of 1.4593, resulting in faster execution time, especially when the foreground density is equal to or greater than the background density of the images.</p>
topic connected components
image processing
labeling algorithm
linear time algorithm
pattern recognition.
url http://jivp.eurasipjournals.com/content/2011/1/14
work_keys_str_mv AT sutheebanjardphaisarn efficientscanmasktechniquesforconnectedcomponentslabelingalgorithm
AT premchaiswadiwichian efficientscanmasktechniquesforconnectedcomponentslabelingalgorithm
_version_ 1724879613379739648