Scanned leaves boundary detection based on the consistent one-class segmentation

D. V. Liakhov; N. S. Mityugov; I. A. Gracheva; A. V. Kopylov; O. S. Seredin; A. E. Semenishchev; I. N. Valkov; Kh. P. Tiras

doi:10.1117/12.2641160

26 October 2022 Scanned leaves boundary detection based on the consistent one-class segmentation

D. V. Liakhov, N. S. Mityugov, I. A. Gracheva, A. V. Kopylov, O. S. Seredin, A. E. Semenishchev, I. N. Valkov, Kh. P. Tiras

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Proceedings Volume 12267, Image and Signal Processing for Remote Sensing XXVIII; 1226716 (2022) https://doi.org/10.1117/12.2641160
Event: SPIE Remote Sensing, 2022, Berlin, Germany

Conference Poster

Abstract

The accurate segmentation of the leaf area on scanned digital images plays a crucial role in the automated evaluation of its morphological characteristics. We propose here a new algorithm for extracting leaf area from the digital images based on a combination of a parametric description of shadow and background areas in the color space by support vector data description (SVDD) and the structure transfer filtering method based on the gamma-normal probabilistic model. The combination of these methods allows us to consider color information as well as sharp changes in image intensity at the edges of a leaf.

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D. V. Liakhov, N. S. Mityugov, I. A. Gracheva, A. V. Kopylov, O. S. Seredin, A. E. Semenishchev, I. N. Valkov, and Kh. P. Tiras "Scanned leaves boundary detection based on the consistent one-class segmentation", Proc. SPIE 12267, Image and Signal Processing for Remote Sensing XXVIII, 1226716 (26 October 2022); https://doi.org/10.1117/12.2641160

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