Self-Similar Texture Characterization Using Wavelet Generated Multi-resolution Gaussian Markov Random Fields

應用「波形多層高斯馬可夫隨機場域」於自我相似紋路之特徵分析

溫哲彥(C. Y. Wen)

《弘光學報》 30期 (1997/10) Pp. 145-159

https://doi.org/10.6615/HAR.199710.30.11

摘要

“高斯馬可夫隨機場域”已成功地被用來分析紋路。然而，對於“自我相似紋路”，它的分析效果並不理想。在本文中，我們使用“波形表示法”與“高斯馬可夫隨機場域”來分析與分類自我相似紋路，並且，我們證明“碎形布朗運動＂之“差異訊號”是“靜態(stationary)的”，且其“平均值(mean)＂是零。

關鍵字

高斯馬可夫隨機場域 ； 碎形布朗運動

並列摘要

Gaussian Markov Random Fields ( GMRF ) have been successfully used to model textures. However, they do not provide the best results for classifying self-similar textures. In this paper, we model self-similar textures using a wavelet representation. We show that the detail signal of a Fractional Brownian Motion ( FBM ) is zero mean stationary. The detail signal of the self-similar texture is modeled as a Gaussian Markov Random Field. Texture classification is performed using the parameters of the Gaussian Markov Random Field of the detail signal.

並列關鍵字

Gaussian Markov Random Fields ； Wavelets ； Fractional Brownian Motion

參考文獻

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R. Chellappa and S. Chatterjee, "Classification of textures using Gaussian Markov random fields," IEEE Transactions on Acoustics, Speech and Signal Processing, vol. ASSP-33, no. 4, pp. 959-963, 1985.

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