Diffusion-based similarity for image analysis

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F15%3A86096744" target="_blank" >RIV/61989100:27240/15:86096744 - isvavai.cz</a>

Výsledek na webu

<a href="http://link.springer.com/content/pdf/10.1007%2F978-3-319-27677-9_7.pdf" target="_blank" >http://link.springer.com/content/pdf/10.1007%2F978-3-319-27677-9_7.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-27677-9_7" target="_blank" >10.1007/978-3-319-27677-9_7</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Diffusion-based similarity for image analysis

Popis výsledku v původním jazyce

Measuring the distances is a key problem in many imageanalysis algorithms. This is especially true for image segmentation. It provides a basis for the decision whether two image points belong to a single or to two different image segments. Many algorithms use the Euclidean distance, which may not be the right choice. The geodesic distance or the k shortest paths measure the distance along the surface that is defined by the image function. The diffusion distance seems to provide better properties since all the paths are taken into account. In this paper, we show that the diffusion distance has the properties that make it difficult to use in some image processing algorithms, mainly in image segmentation, which extends the recent observations of some other authors. We propose a new measure called normalised diffusion cosine similarity that overcomes some problems of diffusion distance. Lastly, we present the necessary theory and the experimental results.

Název v anglickém jazyce

Diffusion-based similarity for image analysis

Popis výsledku anglicky

Measuring the distances is a key problem in many imageanalysis algorithms. This is especially true for image segmentation. It provides a basis for the decision whether two image points belong to a single or to two different image segments. Many algorithms use the Euclidean distance, which may not be the right choice. The geodesic distance or the k shortest paths measure the distance along the surface that is defined by the image function. The diffusion distance seems to provide better properties since all the paths are taken into account. In this paper, we show that the diffusion distance has the properties that make it difficult to use in some image processing algorithms, mainly in image segmentation, which extends the recent observations of some other authors. We propose a new measure called normalised diffusion cosine similarity that overcomes some problems of diffusion distance. Lastly, we present the necessary theory and the experimental results.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Lecture Notes in Computer Science. Volume 9493

ISBN

978-3-319-27676-2

ISSN

0302-9743

e-ISSN

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Počet stran výsledku

18

Strana od-do

107-123

Název nakladatele

Springer Verlag

Místo vydání

London

Místo konání akce

Lisabon

Datum konání akce

10. 1. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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