An Improved Random Sample Consensus Based on Density-Based Spatial Clustering of Applications with Noise for Image Mosaic

被引：1
|
作者：
Liu, Jinda [1 ,2 ,3 ]
Hou, Yanyang [2 ]
Pei, Hongxing [3 ]
机构：
[1] Jilin Univ, Sch Instrument Sci & Elect Engn, Changchun 130061, Jilin, Peoples R China
[2] Zhengzhou Univ Ind Technol, Sch Informat Engn, Xinzheng 451150, Henan, Peoples R China
[3] Zhengzhou Univ, Sch Phys & Engn, Zhengzhou 450001, Henan, Peoples R China
来源：
PATTERN RECOGNITION AND IMAGE ANALYSIS | 2021年 / 31卷 / 04期
关键词：
image registration; random sample consensus; density-based spatial clustering of applications with noise; clustering;
D O I：
10.1134/S1054661821040155
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
Image mosaic is the technique of constructing a sequence of images into a high-resolution image, which mainly includes image registration and image fusion. In this paper we propose a new method for image registration: feature vectors of matching points are formed firstly, then we use density-based spatial clustering of applications with noise to process feature vectors to improve Random Sample Consensus in the process of estimating transformation model between two images. The results show that proposed method outperforms the traditional method, which estimates the transformation model by random sample consensus only, on the spatial frequency, definition, and peak signal-to-noise ratio in images.
引用
收藏
页码：625 / 631
页数：7
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