Extraction of Homogeneous Fine-Grained Texture Segments in Visual Images

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F17%3A00478807" target="_blank" >RIV/67985807:_____/17:00478807 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.14311/NNW.2017.27.024" target="_blank" >http://dx.doi.org/10.14311/NNW.2017.27.024</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.14311/NNW.2017.27.024" target="_blank" >10.14311/NNW.2017.27.024</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Extraction of Homogeneous Fine-Grained Texture Segments in Visual Images

Popis výsledku v původním jazyce

A new heuristic algorithm is proposed for extraction of all homogeneous fine-grained texture segments present in any visual image. The segments extracted by this algorithm should comply with human understanding of homogeneous fine-grained areas. The algorithm sequentially extracts segments from more homogeneous to less homogeneous ones. The algorithm belongs to a region growing approach. So, for each segment, an initial seed point of this segment is found. Then, from this initial pixel, the segment begins to expand occupying its adjacent neighborhoods. This procedure of expansion of the segment continues till the segment reaches its borders. The algorithm examines neighboring pixels using texture features extracted in the image by means of a set of texture windows. The segmentation process terminates when the image contains no more sizable homogeneous segments. The segmentation procedure is fully unsupervised, i.e., it does not use a priori knowledge on either the type of textures or the number of texture segments in the image. Using black and white natural scenes, a series of experiments demonstrates efficiency of the algorithm in extraction of homogeneous fine-grained texture segments and the segmentation looks reasonable ”from a human point of view”.

Název v anglickém jazyce

Extraction of Homogeneous Fine-Grained Texture Segments in Visual Images

Popis výsledku anglicky

A new heuristic algorithm is proposed for extraction of all homogeneous fine-grained texture segments present in any visual image. The segments extracted by this algorithm should comply with human understanding of homogeneous fine-grained areas. The algorithm sequentially extracts segments from more homogeneous to less homogeneous ones. The algorithm belongs to a region growing approach. So, for each segment, an initial seed point of this segment is found. Then, from this initial pixel, the segment begins to expand occupying its adjacent neighborhoods. This procedure of expansion of the segment continues till the segment reaches its borders. The algorithm examines neighboring pixels using texture features extracted in the image by means of a set of texture windows. The segmentation process terminates when the image contains no more sizable homogeneous segments. The segmentation procedure is fully unsupervised, i.e., it does not use a priori knowledge on either the type of textures or the number of texture segments in the image. Using black and white natural scenes, a series of experiments demonstrates efficiency of the algorithm in extraction of homogeneous fine-grained texture segments and the segmentation looks reasonable ”from a human point of view”.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název periodika

Neural Network World

ISSN

1210-0552

e-ISSN

—

Svazek periodika

27

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

31

Strana od-do

447-477

Kód UT WoS článku

000416417400001

EID výsledku v databázi Scopus

2-s2.0-85033377165