4.7 Article

Comparison of three image-analysis-based visual texture calculation methods: energy, entropy, and texture change index

期刊

JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE
卷 102, 期 13, 页码 5984-5994

出版社

WILEY
DOI: 10.1002/jsfa.11951

关键词

Cyprinus carpio; scaled carp; mirror carp; image analysis; visual texture

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This study applied three image analysis methods to measure visual texture and compared the effect of image rotation on the results. The co-occurrence matrix method showed rotation invariance and could accurately distinguish between different texture levels by calculating the texture change index (TCI).
BACKGROUND Three image analysis methods to measure visual texture were applied to an image with much texture (scaled carp), and one with little texture (mirror carp). For each method, the effect of image rotation at 0 degrees, 10 degrees, 20 degrees, 30 degrees, 45 degrees, 60 degrees, 75 degrees and 90 degrees was evaluated. RESULTS Rotation did not change energy (E) and entropy (H) calculations using image histograms. Using co-occurrence matrices with different step size d (1-19 in increases of 2) and step angle theta (0 degrees, 45 degrees, 90 degrees and 135 degrees) showed that, as d increased, E decreased and H increased, and the number of legitimate pixel pairs decreased linearly. Averaging E and H at different theta values rendered the results rotation invariant. Theoretically, the 'texture primitives' method is not rotation independent. However, the variation in texture change index (TCI) with image rotation was negligible. Also, the increase in TCI between the less textured image and the more textured image was 5.3-11. In comparison, the E values from histograms for the images above were 0.0069-0.0081. For co-occurrence matrix-based calculations, at d = 1 and for all theta, E range was from 220 to 389 for scaled carp and from 232 to 412 for mirror carp. CONCLUSION The more than doubling of TCI for these images implies that it is a more precise method than energy and entropy to discern between visual texture levels. (c) 2022 Society of Chemical Industry.

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