Fracture Images Classification Based on Fractional Cosine Transform and Markov Mode

Abstract:

Article Preview

Fracture images automatic classification and recognition is an important one of fracture failure intelligent diagnosis, and in which feature extraction is a key issue. In this paper, fractional cosine transform, which is a useful time-frequency analysis method, is used in feature extraction of fracture images, and then the classification of fatigue, dimples, intergranular and cleavage is performed by Hidden markov model (HMM). For metal fracture images classification, experiment shows that fractional cosine transform is better than the cosine transform in fracture images feature description, and the correct recognition rate can be achieved 98.8% based on HMM classification mode

Info:

Periodical:

Advanced Materials Research (Volumes 311-313)

Edited by:

Zhongning Guo

Pages:

970-973

DOI:

10.4028/www.scientific.net/AMR.311-313.970

Citation:

Y. L. Zhang et al., "Fracture Images Classification Based on Fractional Cosine Transform and Markov Mode", Advanced Materials Research, Vols. 311-313, pp. 970-973, 2011

Online since:

August 2011

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Price:

$35.00

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