Abstract
ABSTRACT In this paper, we propose a multi-scale image segmentation algorithm based on mean shift, a simple iterative procedurethat shifts each data point to the average of data points in its neighborhood, which has been proven to be a mode-seekingprocess on a surface constructed with a shadow kernel. In the presented algorithm, not only the color features, but alsothe space relationship of each pixel are considered in multiple scales, thus getting a more reasonable clustering sequence,furthermore, center candidates are validated by contour map. Experimental examples are illustrated and compared toshow that the approach is effective not only in segmentation, but also in denoising.Keywords: multi-scale image segmentation, denoising, mean shift 1. INTRODUCTION Mean shift is a simple iterative procedure that shifts each data point to the average of data points in itsneighborhood. Because of its property of mode-seeking, mean shift is appropriate to cluster analysis and global
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
