ANÁLISE COMPARATIVA ENTRE K-MEANS, FCM E ALGORITMOS CKMEANS PARA SEGMENTAÇÃO DE IMAGEM

COMPARATIVE ANALYSIS BETWEEN K-MEANS, FCM, AND CKMEANS ALGORITHMS FOR IMAGE SEGMENTATION

Autores/as

DOI:

https://doi.org/10.17058/tecnolog.v26i1.17105

Resumen

  1. Clustering algorithms are often used for image segmentation, aiming to group pixels by their similarity and uniformity. This process is useful to detect and highlight important areas of an image, making its analysis easier in several applications such as remote sensing and medical diagnosis. This paper have the main objective to compare the K-Means hard clustering algorithm to the FCM and ckMeans fuzzy clustering algorithms in image segmentation applications, using the R statistical programming language for analysis and visualization of the results. Uncertainty in the clustering process is discussed via the use of the alpha-cut parameter. Two experiments were conducted, using an image from an open database and an aerial image of a Catarinense city. It was found that the three methods produced similar results, when crisp clusters were considered. Fuzzy membership results of FCM and ckMeans were also compared, and it was found that, although very similar, ckMeans produced slightly lower levels of uncertainty than FCM. It was found that K-Means presents the best computational performance among the algorithms compared, which is expected due to its crisp nature. Among the fuzzy algorithms compared, ckMeans presented better performance, and FCM required less memory.

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Biografía del autor/a

Alexandre Russini, Universidade Federal do Pampa

Professor Adjunto da Universidade Federal do Pampa, Campus Itaqui. Atua na área de Máquinas Agrícolas, Mecanização Agrícola e Agricultura de Precisão (AP)

Marcus Vinicius Pereira Saraiva, Pesquisador Assistente, Instituto de Pesquisa Econômica Aplicada - IPEA

Pesquisador Assistente, Instituto de Pesquisa Econômica Aplicada - IPEA

Rogério Rodrigues de Vargas, Universidade Federal do Pampa - Unipampa Campus Itaqui

Professor Adjunto na área de informática/programação da Universidade Federal do Pampa

Marcelo Silveira de Farias, Universidade Federal de Santa Maria

Professor Adjunto da Máquinas e Mecanização da Universidade Federal de Santa Maria -UFSM.

Catize Brandelero, Universidade Federal de Santa Maria

Professora Associada de Máquinas e Mecanização Florestal da Universidade Federal de Santa Maria -UFSM

Citas

Referências

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GOOGLE EARTH. Disponível em: < https://earth.google.com/web/@0,0,0a,22251752.77375655d,35y,0h,0t,0r> Acesso em: 20 jun. 2021.

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Publicado

2022-02-03

Cómo citar

Russini, A., Vinicius Pereira Saraiva, M., Rodrigues de Vargas, R., Silveira de Farias, M., & Brandelero, C. (2022). ANÁLISE COMPARATIVA ENTRE K-MEANS, FCM E ALGORITMOS CKMEANS PARA SEGMENTAÇÃO DE IMAGEM: COMPARATIVE ANALYSIS BETWEEN K-MEANS, FCM, AND CKMEANS ALGORITHMS FOR IMAGE SEGMENTATION. Tecno-Lógica, 26(1), 69-76. https://doi.org/10.17058/tecnolog.v26i1.17105

Número

Sección

Sistemas e Processos Industriais