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https://doi.org/10.17559/TV-20180327094750

Unsupervised Multi-Label Image and Texture Segmentation based on Optimal Feature Representation

Mehmet Cem Catalbas   ORCID icon orcid.org/0000-0002-9291-1180 ; OSTIM Technical University, Ankara, Turkey

Puni tekst: engleski, pdf (2 MB) str. 1275-1283 preuzimanja: 58* citiraj
APA 6th Edition
Catalbas, M.C. (2019). Unsupervised Multi-Label Image and Texture Segmentation based on Optimal Feature Representation. Tehnički vjesnik, 26 (5), 1275-1283. https://doi.org/10.17559/TV-20180327094750
MLA 8th Edition
Catalbas, Mehmet Cem. "Unsupervised Multi-Label Image and Texture Segmentation based on Optimal Feature Representation." Tehnički vjesnik, vol. 26, br. 5, 2019, str. 1275-1283. https://doi.org/10.17559/TV-20180327094750. Citirano 15.11.2019.
Chicago 17th Edition
Catalbas, Mehmet Cem. "Unsupervised Multi-Label Image and Texture Segmentation based on Optimal Feature Representation." Tehnički vjesnik 26, br. 5 (2019): 1275-1283. https://doi.org/10.17559/TV-20180327094750
Harvard
Catalbas, M.C. (2019). 'Unsupervised Multi-Label Image and Texture Segmentation based on Optimal Feature Representation', Tehnički vjesnik, 26(5), str. 1275-1283. https://doi.org/10.17559/TV-20180327094750
Vancouver
Catalbas MC. Unsupervised Multi-Label Image and Texture Segmentation based on Optimal Feature Representation. Tehnički vjesnik [Internet]. 2019 [pristupljeno 15.11.2019.];26(5):1275-1283. https://doi.org/10.17559/TV-20180327094750
IEEE
M.C. Catalbas, "Unsupervised Multi-Label Image and Texture Segmentation based on Optimal Feature Representation", Tehnički vjesnik, vol.26, br. 5, str. 1275-1283, 2019. [Online]. https://doi.org/10.17559/TV-20180327094750

Sažetak
In this paper, an adaptive and robust unsupervised texture segmentation algorithm is proposed. One of the novelties of this proposed algorithm is to determine optimal sub-image size by pattern analysis and another one is optimizing segmentation process by providing the most successful representation of pattern on images. The realization of the algorithm consists of various stages. Firstly, the parameters of regional minima and maxima for images are obtained to extract real pattern information about images. In the next stage, these outputs are combined to each other to calculate the centroid of distribution of patterns. The input image and several texture analysis features are analyzed in the light of this pattern information. Then, the dimension reduction process is performed by the PCA with Horn's parallel analysis method before the data clustering is applied to the dataset. This method allows optimal representation of data after dimension reduction process. The output image is improved by subjecting it to various morphological operations which are called an adaptive morphological mask and the performance of the proposed algorithm is compared with other texture segmentation algorithms.

Ključne riječi
Feature Extraction; Fuzzy C-Means; Image Texture Segmentation; Morphological Operations; Object Analysis; Pattern Analysis; Texture Analysis

Hrčak ID: 226009

URI
https://hrcak.srce.hr/226009

Posjeta: 114 *