Normalised diffusion cosine similarity and its use for image segmentation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F15%3A86096742" target="_blank" >RIV/61989100:27240/15:86096742 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.scitepress.org/DigitalLibrary/PublicationsDetail.aspx?ID=L9hSRTARUnM%3d&t=1" target="_blank" >http://www.scitepress.org/DigitalLibrary/PublicationsDetail.aspx?ID=L9hSRTARUnM%3d&t=1</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5220/0005220601210129" target="_blank" >10.5220/0005220601210129</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Normalised diffusion cosine similarity and its use for image segmentation

Popis výsledku v původním jazyce

In many image-segmentation algorithms, measuring the distances is a key problem since the distance is often used to decide whether two image points belong to a single or, respectively, to two different image segments. The usual Euclidean distance need not be the best choice. Measuring the distances along the surface that is defined by the image function seems to be more relevant in more complicated images. Geodesic distance, i.e. the shortest path in the corresponding graph, or the k shortest paths canbe regarded as the simplest methods. It might seem that the diffusion distance should provide the properties that are better since all the paths (not only their limited number) are taken into account. In this paper, we firstly show that the diffusion distance has the properties that make it difficult to use it image segmentation, which extends the recent observations of some other authors. Afterwards, we propose a new measure called normalised diffusion cosine similarity that is more sui

Název v anglickém jazyce

Normalised diffusion cosine similarity and its use for image segmentation

Popis výsledku anglicky

In many image-segmentation algorithms, measuring the distances is a key problem since the distance is often used to decide whether two image points belong to a single or, respectively, to two different image segments. The usual Euclidean distance need not be the best choice. Measuring the distances along the surface that is defined by the image function seems to be more relevant in more complicated images. Geodesic distance, i.e. the shortest path in the corresponding graph, or the k shortest paths canbe regarded as the simplest methods. It might seem that the diffusion distance should provide the properties that are better since all the paths (not only their limited number) are taken into account. In this paper, we firstly show that the diffusion distance has the properties that make it difficult to use it image segmentation, which extends the recent observations of some other authors. Afterwards, we propose a new measure called normalised diffusion cosine similarity that is more sui

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

—

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

ICPRAM 2015 - 4th International Conference on Pattern Recognition Applications and Methods, Proceedings

ISBN

978-989-758-076-5

ISSN

—

e-ISSN

—

Počet stran výsledku

9

Strana od-do

121-129

Název nakladatele

INSTICC - Institute for Systems and Technologies of Information, Control and Communication

Místo vydání

Setubal

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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