A Novel Method for Logo Detection Based on Curvelet Transform Using GLCM Features

Author:

Naganjaneyulu G. V. S. S. K. R.,Sai Krishna Ch,Narasimhadhan A. V.

Publisher

Springer Singapore

Reference37 articles.

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2. Ahmed, Z., Fella, H.: Logos extraction on picture documents using shape and color density. In: ISIE IEEE International Symposium on Industrial Electronics, pp. 2492–2496. IEEE (2008)

3. Arivazhagan, S., Ganesan, L., Kumar, S.T.: Texture classification using curvelet statistical and co-occurrence features. In: 18th International Conference on Pattern Recognition, ICPR, vol. 2, pp. 938–941. IEEE (2006)

4. Candes, E., Demanet, L., Donoho, D., Ying, L.: Fast discrete curvelet transforms. Multiscale Modeling & Simulation 5(3), 861–899 (2006)

5. Candes, E.J., Donoho, D.L.: Curvelets: A surprisingly effective nonadaptive representation for objects with edges. FPO AE 09499-0039 (1999)

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3. Logo Detection Based on FCM Clustering Algorithm and Texture Features;Lecture Notes in Computer Science;2020

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