Multi-modal gray-level histogram modeling and decomposition

Autor:	Jeng Horng Chang, Kuo Chin Fan, Yang-Lang Chang
Rok vydání:	2002
Předmět:	Optimal estimation business.industry Balanced histogram thresholding Estimation theory Feature vector Histogram matching Pattern recognition Thresholding Histogram Signal Processing Adaptive histogram equalization Computer Vision and Pattern Recognition Artificial intelligence business Mathematics
Zdroj:	Image and Vision Computing. 20:203-216
ISSN:	0262-8856
Popis:	In this paper, we present a novel multi-modal histogram thresholding method in which no a priori knowledge about the number of clusters to be extracted is needed. The proposed method combines regularization and statistical approaches. By converting the approaching histogram thresholding problem to the mixture Gaussian density modeling problem, threshold values can be estimated precisely according to the parameters belonging to each contiguous cluster. Computational complexity has been greatly reduced since our method does not employ conventional iterative parameter refinement. Instead, an optimal parameter estimation interval was defined before the estimation procedure. This predefined optimal estimation interval reduces time consumption while other histogram decomposition based methods search all feature space to locate an estimation interval for each candidate cluster. Experimental results with both simulated data and real images demonstrate the robustness of our method.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::379893a21455464fa3c091af6edc973f https://doi.org/10.1016/s0262-8856(01)00095-6 Zobrazit plný text záznamu Full Text from ScienceDirect