A Comparative Study of Image Segmentation Based on the Improved Meanshift Software with Edison

Abstract:

Image segmentation is the technique and the process to separate the image into regions which have different characteristics and extract the interested objects from the image. Meanwhile, image segmentation is a vital important issue in many fields such as image processing, pattern recognition and artificial intelligence and it has wide application in various fields. This paper performs a great deal of contrastive analysis experiments on a series of images by using improved meanshift software and Edison software. The results show that improved meanshift software is easier to segment clearly than Edison in terms of similar color; the improved meanshift software segmentation is smoother than Edison in image shadow, the segmentation results hold favorable consistency in terms of human perception; the improved meanshift software segmentation is clearer than Edison in texture segmentation such as vegetation. The improved meanshift software has a better effect on the segmentation of boundary, road, etc. Both of them can remove the noise points effectively, but improved meanshift software is more sensitive to brightness; while the Edison software has a faster speed compared to the improved meanshift software.

• Periodical: Applied Mechanics and Materials (Volumes 701-702)
• Pages: 253-256
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.701-702.253
• Online since: December 2014
• Authors: Ning Li, Jing Wen Xu*, Jun Fang Zhao, Yu Dan Zhao, Peng Hou
• Keywords: Comparison, Edison, Image Segmentation, Meanshift

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