Evaluación de la calidad del papel reciclado por descriptores de texturas

José Orlando Maldonado Bautista; Manuel Graña Romay

doi:10.24054/rcta.v1i21.1894

Autores/as

José Orlando Maldonado Bautista Universidad de Pamplona
Manuel Graña Romay Universidad del País Vasco

DOI:

https://doi.org/10.24054/rcta.v1i21.1894

Palabras clave:

Análisis de texturas, calidad del papel, reconocimiento de patrones

Resumen

En el proceso de inspección de calidad del papel reciclado, un defecto que se aprecia ocasionalmente es la aparición de ondulaciones en las hojas de papel a escala macroscópica, lo cual puede surgir algún tiempo después de su fabricación. Se ha denominado a dicho fenómeno abollado. En este trabajo se explora la detección y medida de dicho fenómeno mediante técnicas de tratamiento de imágenes, específicamente análisis de texturas combinadas con métodos de reconocimiento de patrones.

Descargas

Los datos de descargas todavía no están disponibles.

Citas

Gabor D. (1946). Theory of communication. J. Inst. Electr. Eng., 93:429–457.

Mallat, Stephane G. (1989). A theory for multiresolution signal decomposition: The wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2:674–693.

Martinez-Alajarin J, J.D. Luis-Delgado, and L.M. Tomas-Balibrea. (2005). Automatic system for quality-based classification of marble textures. Systems, Man and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 35(4):488–497, Nov.

Calderon-Martinez P., J.A.; Campoy-Cervera. (2003). A convolutional neural architecture: an application for defects detection in continuous manufacturing systems. Circuits and Systems, 2003. ISCAS ’03. Proceedings of the 2003 International Symposium on, 5:V–749–V–752 vol.5, 25-28 May 2003.

Concia Aura and Claudia Belmiro Proença. (1999). A fractal image analysis system for fabric inspection based on a box-counting method. Computer Networks and ISDN Systems, 30:1887–1895.

Grigorescu, Simona E., Nicolai Petkov, and Peter Kruizinga. (2002). Comparison of texture features based on gabor filters. Image Processing, IEEE Transactions on, 11:1160_1167.

Sadonikov A, P. Salmela, L. Lensu, J.-K. Kamarainen, and H. Kälviäinen. (1995). Mottling assessment of solid printed areas and its correlation to perceived uniformity. In Proc. of the 14th Scandinavian Conf. of Image Processing (Joensuu, Finland).

Henry Y.T. Ngana, Grantham K.H. Panga, S.P. Yungb, and Michael K. Ngb. (2005). Wavelet based methods on patterned fabric defect detection. Pattern Recognition, 38:559–576.

Sari-Sarraf J.S. Jr, H.; Goddard. (1999). Vision system for on-loom fabric inspection. Industry Applications, IEEE Transactions on, 35(6):1252–1259, Nov/Dec.

Ahmed Abouelelaa, Hazem M. Abbasb, Hesham Eldeeb, Abdelmonem A. Wahdanb, and Salwa M. Nassara. (2004). Automated vision system for localizing structural defects in textile fabrics. Pattern Recognition Letters, 26:1435–1443.

Considine J.M, C.T. Scott, R. Gleisner, and J.Y. Zhu. (2005). Use of digital image correlation to study the local deformation field of paper and paperboard. In 13th Fundamental Research Symposium Conference, pages 613–630.

Duda R. O., P. E. Hart, and D. G. Stork. (2001). Pattern Classification. Wiley Interscience.

Funck J. W, Y. Zhong, D. A. Butler, C. C. Brunner, and J. B. Forrer. (2003). Image segmentation algorithms applied to wood defect detection. Computers and Electronics in Agriculture, 41:157–179.

Ian H. Witten and Eibe Frank. (2005). Data Mining: Practical machine learning tools and techniques", 2nd Edition.

Panchanathan J. Jr; S. Fahmy, G.; Black. (2006). Texture characterization for joint compression and classification based on human perception in the wavelet domain. Image Processing, IEEE Transactions on, 15(6):1389–1396, June.