dc.contributor.author | Holuša, Michael | |
dc.contributor.author | Sojka, Eduard | |
dc.date.accessioned | 2017-10-30T13:01:04Z | |
dc.date.available | 2017-10-30T13:01:04Z | |
dc.date.issued | 2017 | |
dc.identifier.citation | Pattern Recognition Letters. 2017, vol. 98, p. 103-109. | cs |
dc.identifier.issn | 0167-8655 | |
dc.identifier.issn | 1872-7344 | |
dc.identifier.uri | http://hdl.handle.net/10084/120959 | |
dc.description.abstract | In this paper, we propose a new distance called the k - max distance that is intended for graphs and images. The length of a path is defined as the sum of the k maximum arc weights along the path. The distance between two nodes is the length of the shortest path between them. We show that the k max distance is a metric. The algorithm for computing the k - max distance is presented. Certain positive properties of the k - max distance are shown, namely in the context of measuring the distances for image segmentation. The comparison with the geodesic distance, the max- arc distance, the minimum barrier distance, and the random walker technique is carried out in the segmentation of real- life images. | cs |
dc.language.iso | en | cs |
dc.publisher | Elsevier | cs |
dc.relation.ispartofseries | Pattern Recognition Letters | cs |
dc.relation.uri | https://doi.org/10.1016/j.patrec.2017.09.003 | cs |
dc.rights | © 2017 Elsevier B.V. All rights reserved. | cs |
dc.subject | distance measuring | cs |
dc.subject | shortest path | cs |
dc.subject | k-max distance | cs |
dc.subject | geodesic distance | cs |
dc.subject | image processing | cs |
dc.subject | image segmentation | cs |
dc.title | The k-max distance in graphs and images | cs |
dc.type | article | cs |
dc.identifier.doi | 10.1016/j.patrec.2017.09.003 | |
dc.type.status | Peer-reviewed | cs |
dc.description.source | Web of Science | cs |
dc.description.volume | 98 | cs |
dc.description.lastpage | 109 | cs |
dc.description.firstpage | 103 | cs |
dc.identifier.wos | 000411766300015 | |