.png){kind=icon}
Return
Morphological component analysis: An adaptive thresholding strategy
.png){kind=icon}
OA
AI
DOI：10.1109/TIP.2007.907073.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In a recent paper, a method called morphological component analysis (MCA) has been proposed to separate the texture from the natural part in images. MCA relies on an iterative thresholding algorithm, using a threshold which decreases linearly towards zero along the iterations. This paper shows how the MCA convergence can be drastically improved using the mutual incoherence of the dictionaries associated to the different components. This modified MCA algorithm is then compared to basis pursuit, and experiments show that MCA and BP solutions are similar in terms of sparsity, as measured by the l(1) norm, but MCA is much faster and gives us the possibility of handling large scale data sets.
Keywords:
feature extraction
morphological component analysis (MCA)
sparse representations
Journal
IF:
13.7
Papers:
1.0W
Citations:
8.4W
Organization
No organization information available
Cited Papers
ShareSaveHydrolysis of Dinucleoside Monophosphates Containing Arabinose in Various Internucleotide Linkages by Exonuclease from the Venom of Crotalus adamanteus*.png)
Biochemistry
BiochemistryIF0
PREAI
ShareSave
ShareSave
View more