Towards a Hybrid Approach of K-Means and Density-Based Spatial Clustering of Applications with Noise for Image Segmentation

2017 ◽  
Author(s):  
Chun Guan ◽  
Kevin Kam Fung Yuen ◽  
Qi Chen
Keyword(s):  
Image Segmentation ◽  
Spatial Clustering ◽  
Hybrid Approach

The Research on Image Segmentation for Fingerprint Identification

2011 ◽  
Vol 217-218 ◽  
pp. 396-401
Author(s):  
Xiao Jie Xu ◽  
Xi Yan Dong
Keyword(s):  
Image Segmentation ◽  
Mixture Model ◽  
Hybrid Approach ◽  
Identification Accuracy ◽  
Fingerprint Identification ◽  
Image Feature ◽  
Background Information ◽  
Bayesian Decision ◽  
Fingerprint Image ◽  
Directional Information

As the precondition of fingerprint identification, the effective image segmentation plays the significant role in the following image processing. Unlike other images, the fingerprint images are obviously directional. Aiming at this feature, in this paper, an image segmentation method based on the directional information of fingerprint image is introduced, which sufficiently utilizes the directional information of fingerprint image and succeeds in separating the background information. However, owing to the absence of directional information in some local areas of fingerprint image, this method will produce large segmentation errors, even fail. Therefore, for these local regions without directional information, it is proposed to apply Bayesian decision-making theory based on minimum error probability to realize image segmentation. On the assumption that the gray values accord with the probability distribution of Gaussian finite mixture model in image feature space, EM algorithm is used to estimate the parameters of mixture model. The mixture application of two methods can effectively separate the background information from fingerprint image while saving the preprocessing time and ensuring the following identification accuracy of fingerprint. The experiments illustrate the feasibility of the hybrid approach.

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