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Clustered entropy for edge detection

Author

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  • Martínez-Aroza, J.
  • Gómez-Lopera, J.F.
  • Blanco-Navarro, D.
  • Rodríguez-Camacho, J.

Abstract

The quantity of information in images can be evaluated by means of the Shannon entropy. When dealing with natural images with a large scale of gray levels, as well as with images containing textures or suffering some degradation such as noise or blurring, this measure tends to saturate. That is, it reaches high values due to a large amount of irrelevant information, making it useless for measuring significant information. In this paper we present a new information measure, the clustered entropy. This information measure, based on clustering local histograms, has a zero value for quasi-homogeneous regions and reaches high values for regions containing edges. The clustered entropy is used in this paper as an edge detector, by centering a sliding window on every pixel of an image, and calculating the clustered entropy of the corresponding histogram. A search for local maxima throughout the resulting matrix of entropies provides the final image of edges. The mathematical properties of clustered entropy are studied, a comparison between the clustered and the normal entropy is done, and some comparative experiments of edge detection are shown in this paper.

Suggested Citation

  • Martínez-Aroza, J. & Gómez-Lopera, J.F. & Blanco-Navarro, D. & Rodríguez-Camacho, J., 2021. "Clustered entropy for edge detection," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 182(C), pages 620-645.
    • Handle: RePEc:eee:matcom:v:182:y:2021:i:c:p:620-645
    DOI: 10.1016/j.matcom.2020.11.021
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    References listed on IDEAS

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    1. Shao-Meng Qin & Hannu Verkasalo & Mikael Mohtaschemi & Tuomo Hartonen & Mikko Alava, 2012. "Patterns, Entropy, and Predictability of Human Mobility and Life," PLOS ONE, Public Library of Science, vol. 7(12), pages 1-8, December.
    2. Gómez-Lopera, J.F. & Martínez-Aroza, J. & Rodríguez-Valverde, M.A. & Cabrerizo-Vílchez, M.A. & Montes-Ruíz-Cabello, F.J., 2015. "Entropic image segmentation of sessile drops over patterned acetate," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 118(C), pages 239-247.
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