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Goltsev, A., Gritsenko, V., Kussul, E. and Baidyk, T. (2015) Finding the Texture Features Characterizing the Most Homogeneous Texture Segment in the Image. In: Rojas, I., Joya, G. and Catala, A., Eds., Advances in Computational Intelligence. IWANN 2015. Lecture Notes in Computer Science, Vol. 9094, Springer, Cham, 287-300.

has been cited by the following article:

• TITLE: Segmentation of Visual Images by Sequential Extracting Homogeneous Texture Areas

  AUTHORS: Alexander Goltsev, Vladimir Gritsenko, Dušan Húsek

  KEYWORDS: Texture Feature, Texture Window, Homogeneous Fine-Grained Texture Segment, Extraction of Texture Segment, Texture Segmentation

  JOURNAL NAME: Journal of Signal and Information Processing, Vol.11 No.4, November 10, 2020

  ABSTRACT: The purpose of the research is to develop a universal algorithm for partial texture segmentation of any visual images. The main peculiarity of the proposed segmentation procedure is the extraction of only homogeneous fine-grained texture segments present in the images. At first, an initial seed point is found for the largest and most homogeneous segment of the image. This initial seed point of the segment is expanded using a region growing method. Other texture segments of the image are extracted analogously in turn. At the second stage, the procedure of merging the extracted segments belonging to the same texture class is performed. Then, the detected texture segments are input to a neural network with competitive layers which accomplishes more accurate delineation of the shapes of the extracted texture segments. The proposed segmentation procedure is fully unsupervised, i.e., it does not use any a priori knowledge on either the type of textures or the number of texture segments in the image. The research results in development of the segmentation algorithm realized as a computer program tested in a series of experiments that demonstrate its efficiency on grayscale natural scenes.