A Theory of Automatic Parameter Selection for Feature Extraction With Application to Feature-Based Multisensor Image Registration

2007 ◽  
Vol 16 (11) ◽  
pp. 2733-2742 ◽  
Author(s):  
Stephen P. DelMarco ◽  
Victor Tom ◽  
Helen F. Webb
Keyword(s):  
Feature Extraction ◽  
Image Registration ◽  
Parameter Selection ◽  
Feature Based ◽  
Selection For

Automatic parameter selection for feature-based multi-sensor image registration

2006 ◽  
Author(s):  
Stephen DelMarco ◽  
Victor Tom ◽  
Helen Webb ◽  
Alan Chao
Keyword(s):  
Image Registration ◽  
Parameter Selection ◽  
Feature Based ◽  
Selection For

Automatic Parameter Selection for Multimodal Image Registration

2010 ◽  
Vol 29 (5) ◽  
pp. 1140-1155 ◽  
Author(s):  
Dieter A Hahn ◽  
Volker Daum ◽  
Joachim Hornegger
Keyword(s):  
Image Registration ◽  
Parameter Selection ◽  
Multimodal Image Registration ◽  
Selection For

scholarly journals Fundus Image Registration Technique Based on Local Feature of Retinal Vessels

2021 ◽  
Vol 11 (23) ◽  
pp. 11201
Author(s):  
Roziana Ramli ◽  
Khairunnisa Hasikin ◽  
Mohd Yamani Idna Idris ◽  
Noor Khairiah A. Karim ◽  
Ainuddin Wahid Abdul Wahab
Keyword(s):  
Feature Extraction ◽  
Image Registration ◽  
Extraction Methods ◽  
Local Feature ◽  
Retinal Vessels ◽  
Feature Points ◽  
Fundus Image ◽  
Scale Invariant ◽  
Feature Based ◽  
Image Pairs

Feature-based retinal fundus image registration (RIR) technique aligns fundus images according to geometrical transformations estimated between feature point correspondences. To ensure accurate registration, the feature points extracted must be from the retinal vessels and throughout the image. However, noises in the fundus image may resemble retinal vessels in local patches. Therefore, this paper introduces a feature extraction method based on a local feature of retinal vessels (CURVE) that incorporates retinal vessels and noises characteristics to accurately extract feature points on retinal vessels and throughout the fundus image. The CURVE performance is tested on CHASE, DRIVE, HRF and STARE datasets and compared with six feature extraction methods used in the existing feature-based RIR techniques. From the experiment, the feature extraction accuracy of CURVE (86.021%) significantly outperformed the existing feature extraction methods (p ≤ 0.001*). Then, CURVE is paired with a scale-invariant feature transform (SIFT) descriptor to test its registration capability on the fundus image registration (FIRE) dataset. Overall, CURVE-SIFT successfully registered 44.030% of the image pairs while the existing feature-based RIR techniques (GDB-ICP, Harris-PIIFD, Ghassabi’s-SIFT, H-M 16, H-M 17 and D-Saddle-HOG) only registered less than 27.612% of the image pairs. The one-way ANOVA analysis showed that CURVE-SIFT significantly outperformed GDB-ICP (p = 0.007*), Harris-PIIFD, Ghassabi’s-SIFT, H-M 16, H-M 17 and D-Saddle-HOG (p ≤ 0.001*).

scholarly journals An improved wrapper-based feature selection for efficient opinion mining

2017 ◽  
Vol 7 (1.3) ◽  
pp. 140
Author(s):  
Prasanna Moorthi N ◽  
Mathivanan V
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Evolutionary Algorithms ◽  
Opinion Mining ◽  
Selection Mechanism ◽  
Large Problem ◽  
Problem Space ◽  
Product Features ◽  
Feature Based ◽  
Selection For

Opinion mining analyses people’s opinions, evaluations, sentiments, attitudes, appraisals and emotions to entities like products, organizations, services, issues, individuals, topics, events and their attributes. It is a large problem space having high feature dimensionality. Feature extraction is important in opinion mining as customers do not usually express product opinions totally, but separately based on individual features. Two tasks should be accomplished in feature-based opinion mining. First, product features on which reviewers expressed opinions must be identified and extracted. Second, opinion orientation or polarities must be determined. Finally, opinion mining summarizes extracted features and opinions. In this work a novel wrapper based feature selection mechanism using concept based feature expansion is proposed. The wrapper based technique uses the principles of evolutionary algorithms.

scholarly journals Non-rigid image registration using a modified fuzzy feature-based inference system for 3D cardiac motion estimation

2021 ◽  
Vol 205 ◽  
pp. 106085
Author(s):  
Monire Sheikh Hosseini ◽  
Mahammad Hassan Moradi ◽  
Mahdi Tabassian ◽  
Jan D'hooge
Keyword(s):  
Image Registration ◽  
Motion Estimation ◽  
Cardiac Motion ◽  
Inference System ◽  
Feature Based
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