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

• Published in: Pattern recognition and image analysis Vol. 31; no. 4; pp. 625 - 631
• Main Authors: Jinda Liu, Hou, Yanyang, Pei, Hongxing
• Format: Journal Article
• Language: English
• Published: Moscow Pleiades Publishing 01.10.2021; Springer Nature B.V
• Subjects: Analysis; Clustering; Computer Science; Computer vision; Density; Image processing; Image Processing and Computer Vision; Image registration; Image resolution; Mathematical Theory of Images and Signals Representing; Mosaics; Noise; Pattern Recognition; Processing; Recognition; Signal to noise ratio; Understanding; clustering; density-based spatial clustering of applications with noise; image registration; random sample consensus
• ISSN: 1054-6618; 1555-6212
• DOI: 10.1134/S1054661821040155

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.