This is an outdated version published on 2022-11-08. Read the most recent version.

EVALUACIÓN DE LA CALIDAD DEL PAPEL RECICLADO POR DESCRIPTORES DE TEXTURAS

Authors

  • José Orlando Maldonado Bautista
  • Manuel Graña Romay

DOI:

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

Keywords:

Análisis de texturas, calidad del papel, reconocimiento de patrones

Abstract

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.

Downloads

Download data is not yet available.

References

Ahmed Abouelelaa, Hazem M. Abbasb, Hesham

Eldeeba, Abdelmonem A. Wahdanb, and

Salwa M. Nassara. Automated vision system

for localizing structural defects in textile

fabrics. Pattern Recognition Letters, 26:1435–

, 2004.

Calderon-Martinez P., J.A.; Campoy-Cervera. A

convolutional neural architecture: an

application for defects detection in continuous

manufacturing systems. Circuits and Systems,

ISCAS ’03. Proceedings of the 2003

International Symposium on, 5:V–749–V–

vol.5, 25-28 May 2003.

Concia Aura and Claudia Belmiro Proença. A

fractal image analysis system for fabric

inspection based on a box-counting method.

Computer Networks and ISDN Systems,

:1887–1895, 1999.

Considine J.M, C.T. Scott, R. Gleisner, and J.Y.

Zhu. 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. Pattern

Classification. Wiley Interscience, 2001.

Funck J. W, Y. Zhong, D. A. Butler, C. C.

Brunner, and J. B. Forrer. Image

segmentation algorithms applied to wood

defect detection. Computers and Electronics

in Agriculture, 41:157–179, 2003.

Gabor D. Theory of communication. J. Inst. Electr.

Eng., 93:429–457, 1946.

Grigorescu, Simona E., Nicolai Petkov, and Peter

Kruizinga. Comparison of texture features

based on gabor filters. Image Processing,

IEEE Transactions on, 11:1160_1167, 2002.

Henry Y.T. Ngana, Grantham K.H. Panga, S.P.

Yungb, and Michael K. Ngb. Wavelet based

methods on patterned fabric defect detection.

Pattern Recognition, 38:559–576, 2005.

Ian H. Witten and Eibe Frank. Data Mining:

Practical machine learning tools and

techniques", 2nd Edition. 2005.

Mallat, Stephane G.. A theory for multiresolution

signal decomposition: The wavelet

representation. IEEE Transactions on Pattern

Analysis and Machine Intelligence, 2:674–

, 1989.

Martinez-Alajarin J, J.D. Luis-Delgado, and L.M.

Tomas-Balibrea. 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. 2005.

Panchanathan J. Jr; S. Fahmy, G.; Black. 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

Sadonikov A, P. Salmela, L. Lensu, J.-K.

Kamarainen, and H. Kälviäinen. Mottling

assessment of solid printed areas and its

correlation to perceived uniformity. In In

Proc. of the 14th Scandinavian Conf. of

Image Processing (Joensuu, Finland), 1995.

Sari-Sarraf J.S. Jr, H.; Goddard. Vision system for

on-loom fabric inspection. Industry

Applications, IEEE Transactions on,

(6):1252–1259, Nov/Dec 1999.

Published

2022-11-08

Versions

How to Cite

Maldonado Bautista, J. O., & Graña Romay, M. (2022). EVALUACIÓN DE LA CALIDAD DEL PAPEL RECICLADO POR DESCRIPTORES DE TEXTURAS. COLOMBIAN JOURNAL OF ADVANCED TECHNOLOGIES, 1(21). https://doi.org/10.24054/rcta.v1i21.1894