Document Detail


Binary halftone image resolution increasing by decision tree learning.
MedLine Citation:
PMID:  15326855     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
This paper presents a new, accurate, and efficient technique to increase the spatial resolution of binary halftone images. It makes use of a machine learning process to automatically design a zoom operator starting from pairs of input-output sample images. To accurately zoom a halftone image, a large window and large sample images are required. Unfortunately, in this case, the execution time required by most of the previous techniques may be prohibitive. The new solution overcomes this difficulty by using decision tree (DT) learning. Original DT learning is modified to obtain a more efficient technique (WZDT learning). It is useful to know, a priori, sample complexity (the number of training samples needed to obtain, with probability 1 - delta, an operator with accuracy epsilon): we use the probably approximately correct (PAC) learning theory to compute the sample complexity. Since the PAC theory usually yields an overestimated sample complexity, statistical estimation is used to evaluate, a posteriori, a tight error bound. Statistical estimation is also used to choose an appropriate window and to show that DT learning has good inductive bias. The new technique is more accurate than a zooming method based on simple inverse halftoning techniques. The quality of the proposed solution is very close to the theoretical optimal obtainable quality for a neighborhood-based zooming process using the Hamming distance to quantify the error.
Authors:
Hae Yong Kim
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Publication Detail:
Type:  Comparative Study; Evaluation Studies; Journal Article; Research Support, Non-U.S. Gov't; Validation Studies    
Journal Detail:
Title:  IEEE transactions on image processing : a publication of the IEEE Signal Processing Society     Volume:  13     ISSN:  1057-7149     ISO Abbreviation:  IEEE Trans Image Process     Publication Date:  2004 Aug 
Date Detail:
Created Date:  2004-08-25     Completed Date:  2004-09-14     Revised Date:  2006-11-15    
Medline Journal Info:
Nlm Unique ID:  9886191     Medline TA:  IEEE Trans Image Process     Country:  United States    
Other Details:
Languages:  eng     Pagination:  1136-46     Citation Subset:  IM    
Affiliation:
Departamento de Engenharia de Sistemas Eletrônicos, Escola Politécnica, Universidade de São Paulo, 05508-900, São Paulo, Brazil. hae@lps.usp.br
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MeSH Terms
Descriptor/Qualifier:
Algorithms*
Artificial Intelligence
Decision Trees*
Image Enhancement / methods*
Image Interpretation, Computer-Assisted / methods*
Likelihood Functions
Models, Statistical
Pattern Recognition, Automated
Reproducibility of Results
Sensitivity and Specificity
Signal Processing, Computer-Assisted*
Subtraction Technique*

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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