Exponential principal component analysis and non-local means based two-stage method for photon-limited Poisson image reconstruction

2015 ◽  
Author(s):  
Nan Huang ◽  
Jun Zhang
Keyword(s):  
Principal Component Analysis ◽  
Image Reconstruction ◽  
Principal Component ◽  
Component Analysis ◽  
Two Stage ◽  
Local Means ◽  
Non Local

scholarly journals Two-stage image denoising by principal component analysis with local pixel grouping

2010 ◽  
Vol 43 (4) ◽  
pp. 1531-1549 ◽  
Author(s):  
Lei Zhang ◽  
Weisheng Dong ◽  
David Zhang ◽  
Guangming Shi
Keyword(s):  
Principal Component Analysis ◽  
Image Denoising ◽  
Principal Component ◽  
Component Analysis ◽  
Two Stage ◽  
Pixel Grouping

scholarly journals A Novel Two-Stage Spectrum-Based Approach for Dimensionality Reduction: A Case Study on the Recognition of Handwritten Numerals

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Mohammad Amin Shayegan ◽  
Saeed Aghabozorgi ◽  
Ram Gopal Raj
Keyword(s):  
Principal Component Analysis ◽  
Pattern Recognition ◽  
Feature Selection ◽  
Dimensionality Reduction ◽  
Feature Vector ◽  
Principal Component ◽  
Random Projection ◽  
Component Analysis ◽  
Two Stage ◽  
Linear Discriminant

Dimensionality reduction (feature selection) is an important step in pattern recognition systems. Although there are different conventional approaches for feature selection, such as Principal Component Analysis, Random Projection, and Linear Discriminant Analysis, selecting optimal, effective, and robust features is usually a difficult task. In this paper, a new two-stage approach for dimensionality reduction is proposed. This method is based on one-dimensional and two-dimensional spectrum diagrams of standard deviation and minimum to maximum distributions for initial feature vector elements. The proposed algorithm is validated in an OCR application, by using two big standard benchmark handwritten OCR datasets, MNIST and Hoda. In the beginning, a 133-element feature vector was selected from the most used features, proposed in the literature. Finally, the size of initial feature vector was reduced from 100% to 59.40% (79 elements) for the MNIST dataset, and to 43.61% (58 elements) for the Hoda dataset, in order. Meanwhile, the accuracies of OCR systems are enhanced 2.95% for the MNIST dataset, and 4.71% for the Hoda dataset. The achieved results show an improvement in the precision of the system in comparison to the rival approaches, Principal Component Analysis and Random Projection. The proposed technique can also be useful for generating decision rules in a pattern recognition system using rule-based classifiers.

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