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Image segmentation by improved minimum spanning tree with fractional differential and Canny detector
Fuzhou Univ, Coll Phys & Informat Engn, Natl Local Joint Engn Lab Flat Panel Display Tech, Fuzhou 10086, Fujian, Peoples R China..
Fuzhou Univ, Coll Phys & Informat Engn, Natl Local Joint Engn Lab Flat Panel Display Tech, Fuzhou 10086, Fujian, Peoples R China..
Fuzhou Univ, Coll Phys & Informat Engn, Natl Local Joint Engn Lab Flat Panel Display Tech, Fuzhou 10086, Fujian, Peoples R China..
KTH. Fuzhou Univ, Coll Phys & Informat Engn, Natl Local Joint Engn Lab Flat Panel Display Tech, Fuzhou 10086, Fujian, Peoples R China..
2019 (English)In: Journal of Algorithms and Computational Technology, ISSN 1748-3018, E-ISSN 1748-3026, Vol. 13, article id UNSP 1748302619873599Article in journal (Refereed) Published
Abstract [en]

In this study, we propose an algorithm that uses an improved Minimum Spanning Tree algorithm and a modified Canny edge detector to segment images that contain a considerable amount of noises. First, we use our modified Canny operator to pre-process an image, and record the obtained object boundary information; then, we apply the improved Minimum Spanning Tree algorithm to associate the above information with boundary points in order to separate edges into two classes in the image, namely the inner and boundary regions. In particular, Minimum Spanning Tree algorithm is improved by using Fractional differential and combining the functions of the intra-regional and inter-regional differences with a function for edge weights. Based on the experimental results, compared with the other four exiting algorithms, the new algorithm has the higher accuracy and the better effect for noised image segmentation.

Place, publisher, year, edition, pages
SAGE PUBLICATIONS LTD , 2019. Vol. 13, article id UNSP 1748302619873599
Keywords [en]
Canny, graph, minimum spanning tree, image segmentation, fractional differential
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-261030DOI: 10.1177/1748302619873599ISI: 000485245700001Scopus ID: 2-s2.0-85073018100OAI: oai:DiVA.org:kth-261030DiVA, id: diva2:1356747
Note

QC 20191002

Available from: 2019-10-02 Created: 2019-10-02 Last updated: 2020-02-04Bibliographically approved

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