Multi-modal gray-level histogram modeling and decomposition

Jeng Horng Chang, Kuo Chin Fan, Yang Lang Chang

研究成果: 雜誌貢獻期刊論文同行評審

56 引文 斯高帕斯(Scopus)

摘要

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.

原文???core.languages.en_GB???
頁(從 - 到)203-216
頁數14
期刊Image and Vision Computing
20
發行號3
DOIs
出版狀態已出版 - 1 3月 2002

指紋

深入研究「Multi-modal gray-level histogram modeling and decomposition」主題。共同形成了獨特的指紋。

引用此