Paper Info
Title
Binary fingerprint image thinning using template-based PCNNs.
Abstract
This correspondence presents a coarse-to-fine binary-image-thinning algorithm by proposing a template-based pulse-coupled neural-network model. Under the control of coupled templates, this algorithm iteratively skeletonizes a binary image by changing the load signals of pulse neurons. A direction-constraining scheme for avoiding fingerprint ridge spikes has been discussed. Experiments show that this algorithm is effective for fingerprint thinning, as well as other common images. Moreover, this algorithm can be coupled with a fingerprint identification system to improve the recognition performance.
Year
DOI
Venue
2007
10.1109/TSMCB.2007.903369
IEEE Transactions on Systems, Man, and Cybernetics, Part B
Keywords
Field
DocType
image thinning,recognition performance,fingerprint identification,load signal,coupled templates,direction-constraining scheme,binary fingerprint image thinning,coupled template,image skeletonization,coarse-to-fine binary-image-thinning algorithm,pulse neurons,template-based pulse-coupled neural-network model,binary-image-thinning algorithm,fingerprint ridge spike,pulse-coupled neural network (pcnn),binary fingerprint image,common image,fingerprint thinning,template-based pcnn,algorithm iteratively,fingerprint identification system,pulse neuron,fingerprint ridge spikes,neural nets,template-based pcnns,thinning rate (tr),binary image,artificial neural networks,fingerprint recognition,classification algorithms
Thinning,Computer science,Fingerprint recognition,Binary image,Artificial intelligence,Artificial neural network,Binary number,Computer vision,Pattern recognition,Fingerprint,Template,Statistical classification,Machine learning
Journal
Volume
Issue
ISSN
37
5
1083-4419
Citations 
PageRank 
References 
21
0.76
26
Authors
4
Name
Order
Citations
PageRank
Luping Ji114910.31
Zhang Yi21765194.41
Lifeng Shang348530.96
Xiaorong Pu48511.17