An Automatic Image Segmentation Algorithm Involving Shortest Path Basins

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F15%3A43926142" target="_blank" >RIV/49777513:23520/15:43926142 - isvavai.cz</a>

Výsledek na webu

<a href="http://link.springer.com/article/10.1134%2FS1054661815010162" target="_blank" >http://link.springer.com/article/10.1134%2FS1054661815010162</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1134/S1054661815010162" target="_blank" >10.1134/S1054661815010162</a>

Jazyk výsledku

angličtina

Název v původním jazyce

An Automatic Image Segmentation Algorithm Involving Shortest Path Basins

Popis výsledku v původním jazyce

Image segmentation is a process of partitioning input image into meaningful regions. It is a chal lenging task that is involved in almost every image processing system. Currently lot of methods for image seg mentation with different approaches was created. Between all of them the methods based on graph theory are more and more popular nowadays. Segmentation methods could be classified for example to interactive and automatic ones. The further class of methods benefits from a user interaction that provides valuable informa tion about a segmentation problem. The later class of methods doesn’t incorporate any user interaction. Nev ertheless fully automatic methods that are both precise and robust are still hard to find. In this paper a new method based on shortest path in a graph is presented. This method automatically places seed points that are further used for image segmentation in the sense of path basins. This method allows segment an input image to a predefined or to an undefined number of image segments. Derived seed points could also be used in other interactive methods instead of a user interaction. Experiments with this method show its potential for seg menting a general class of images.

Název v anglickém jazyce

An Automatic Image Segmentation Algorithm Involving Shortest Path Basins

Popis výsledku anglicky

Image segmentation is a process of partitioning input image into meaningful regions. It is a chal lenging task that is involved in almost every image processing system. Currently lot of methods for image seg mentation with different approaches was created. Between all of them the methods based on graph theory are more and more popular nowadays. Segmentation methods could be classified for example to interactive and automatic ones. The further class of methods benefits from a user interaction that provides valuable informa tion about a segmentation problem. The later class of methods doesn’t incorporate any user interaction. Nev ertheless fully automatic methods that are both precise and robust are still hard to find. In this paper a new method based on shortest path in a graph is presented. This method automatically places seed points that are further used for image segmentation in the sense of path basins. This method allows segment an input image to a predefined or to an undefined number of image segments. Derived seed points could also be used in other interactive methods instead of a user interaction. Experiments with this method show its potential for seg menting a general class of images.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

<a href="/cs/project/NT13326" target="_blank" >NT13326: Zvyšování resekability maligních ložiskových procesů pomocí metod zpřesňujících měření perfúzních parametrů zbytkového jaterního parenchymu - počítačem asistované diagnostiky a softwarového modelování</a><br>

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 periodika

Pattern Recognition and Image Analysis

ISSN

1054-6618

e-ISSN

—

Svazek periodika

25

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

RU - Ruská federace

Počet stran výsledku

7

Strana od-do

89-95

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-84924299998