Clustered entropy for edge detection

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

Mathematics and Computers in Simulation (MATCOM), 2021, vol. 182, issue C, 620-645

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.

Keywords: Image segmentation; Histogram clustering; Entropic edge detection; Shannon entropy; Clustered entropy; Gray level quantization (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:182:y:2021:i:c:p:620-645

DOI: 10.1016/j.matcom.2020.11.021

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