Research on registration algorithm based on feature points for human eye retina images

2009 ◽  
Author(s):  
Yuran Liu ◽  
Huizhen Yang ◽  
Liyun Su ◽  
Yudong Zhang ◽  
Xuejun Rao
Keyword(s):  
Feature Points ◽  
Human Eye ◽  
Registration Algorithm ◽  
Eye Retina ◽  
Retina Images

A New Algorithm of Image Registration Based on Contour Feature

2014 ◽  
Vol 912-914 ◽  
pp. 1092-1097
Author(s):  
Fu Hua Song ◽  
Peng Hui Li ◽  
Jian Ran Deng
Keyword(s):  
Image Registration ◽  
Rotation Angle ◽  
Closed Contour ◽  
Feature Points ◽  
Registration Algorithm ◽  
Image Registration Algorithm ◽  
Contour Feature ◽  
Circular Correlation ◽  
Open Contour ◽  
Calculated Amount

Image registration is an important task in image processing. In this paper, a new and fast contour-based image registration algorithm is proposed. In this algorithm, we fetch contour points and calculate the normal angles firstly, then figure out the histogram of the contour-feature points. By computing circular correlation of the histogram, the rotation angle can be gained. As the rotation angle is obtained, it vastly simplifies the complexity of estimating other registration parameters and reduces the calculated amount, thus achieving a fast image registration algorithm. This algorithm has the invariance of rotation, translation and scale, and it has high robustness for either open contour or closed contour.

A Robust Image Registration Algorithm Based on Feature Points Matching

2008 ◽  
Vol 28 (3) ◽  
pp. 454-461 ◽  
Author(s):  
刘贵喜 Liu Guixi ◽  
刘冬梅 Liu Dongmei ◽  
刘凤鹏 Liu Fengpeng ◽  
周亚平 Zhou Yaping
Keyword(s):  
Image Registration ◽  
Feature Points ◽  
Registration Algorithm ◽  
Feature Points Matching ◽  
Image Registration Algorithm ◽  
Robust Image

A New Fast Multi-Source Remote Sensing Image Registration Algorithm

2012 ◽  
Vol 452-453 ◽  
pp. 950-953
Author(s):  
Yong Mei Zhang ◽  
Li Ma
Keyword(s):  
Remote Sensing ◽  
Image Registration ◽  
Control Point ◽  
Feature Points ◽  
Remote Sensing Images ◽  
Registration Algorithm ◽  
Image Registration Algorithm ◽  
Shift Rotation ◽  
Back Ground

Aiming at the registration of multi-sensor remote sensing images, a fast and effective image registration algorithm method is presented. In this algorithm, using SIFT to extract feature points, remove mismatching points through Delaunay triangulation, and introduce distance calculation into the determination of homologous control point pairs. The effect of image registration algorithm is evaluated by subjective and objective method. Experiment results show the proposed algorithm can accurately register multi-spectral and panchromatic images with some shift, rotation angles and back-ground noises, and it can inrease the speed and precision of registration.

Speckle structure of a light field scattered by human eye retina

Laser Physics ◽  
2007 ◽  
Vol 17 (9) ◽  
pp. 1157-1165 ◽  
Author(s):  
A. S. Goncharov ◽  
A. V. Larichev
Keyword(s):  
Light Field ◽  
Speckle Structure ◽  
Human Eye ◽  
Eye Retina

scholarly journals Extraction of Iris Crypt, Pigment Spot, and Wolfflin Nodule Biological Feature using Feature Point Selection Algorithms

2019 ◽  
Vol 8 (4) ◽  
pp. 2225-2230
Keyword(s):  
Iris Recognition ◽  
Recognition System ◽  
Feature Points ◽  
Biological Feature ◽  
Point Selection ◽  
Human Eye ◽  
Biological Features ◽  
Human Iris ◽  
The Individual ◽  
Selection Algorithms

The iris biometrics is an important biological feature of the human. The iris is the part of human eye. The human eye consists of many features. Iris is one of the unique features of human eye. In this paper we propose an algorithm to extract the features of iris. The existing algorithms are based on combined biological features of iris. We are going to introduce separate biological features and extract them one by one using suitable algorithms. The proposed method is used to extract the biological features of human iris. The proposed method uses crypts, pigment layers, and Wolfflin nodules features of iris. Each feature is extracted initially and suitable feature selection algorithm is identified. The manual cropping is initially applied in the eye image which extracts iris layer. The manual cropping is further applied on iris to locate the biological layers. Canny edge detection is applied on each iris feature. The FAST, SURF, MinEigen, BRISK, and MSER feature points are collected from each biological layer. The MinEigen extracts 218 feature points from the crypt layer. The BRISK extracts 161 and 89 feature points from the pigment and Wolfflin nodules. The proposed system can be used in iris recognition system all over the world for various authentication and security purposes. The individual feature extraction helps to make the recognition system more secure and accurate as compared to the existing systems which uses combined biological features. Thus, the proposed system is advantageous as compared to the existing systems and the efficiency will also be high if it is used for iris recognition.

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