Normalised diffusion cosine similarity and its use for image segmentation
Identifikátory výsledku
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>
Alternativní jazyky
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
Klasifikace
Druh
D - Stať ve sborníku
CEP obor
IN - Informatika
OECD FORD obor
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Návaznosti výsledku
Projekt
—
Návaznosti
S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2015
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název statě ve sborníku
ICPRAM 2015 - 4th International Conference on Pattern Recognition Applications and Methods, Proceedings
ISBN
978-989-758-076-5
ISSN
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e-ISSN
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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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