A k-max geodesic distance and its application in image segmentation

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F15%3A86095834" target="_blank" >RIV/61989100:27240/15:86095834 - isvavai.cz</a>
Result on the web
<a href="http://link.springer.com/chapter/10.1007%2F978-3-319-23192-1_52" target="_blank" >http://link.springer.com/chapter/10.1007%2F978-3-319-23192-1_52</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-23192-1_52" target="_blank" >10.1007/978-3-319-23192-1_52</a>

Result language
angličtina
Original language name
A k-max geodesic distance and its application in image segmentation
Original language description
The geodesic distance is commonly used when solving image processing problems. In noisy images, unfortunately, it often gives unsatisfactory results. In this paper, we propose a new k-max geodesic distance. The length of path is defined as the sum of thek maximum edge weights along the path. The distance is defined as the length of the path that is the shortest one in this sense. With an appropriate choice of the value of k, the influence of noise can be reduced substantially. The positive properties are demonstrated on the problem of seeded image segmentation. The results are compared with the results of geodesic distance and with the results of the random walker segmentation algorithm. The influence of k value is also discussed.
Czech name
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Czech description
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Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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