Extraction of homogeneous fine-grained texture segments in visual images

Title:

Extraction of homogeneous fine-grained texture segments in visual images

Creator:

Goltsev, A., Gritsenko, V., and Húsek, D.

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:65876b32-2845-4d86-9994-294a4750e884
uuid:65876b32-2845-4d86-9994-294a4750e884
doi:10.14311/NNW.2017.27.024

Subject:

texture feature, texture window, homogeneous fine-grained texture segment, extraction of texture segment, texture segmentation, and ''float'' coding method

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

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''.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Coverage:

447-477

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2017 Volume:27 | Number:5

Harvested from:

CDK

Metadata only:

false