Chromatic Correction Applied to Outdoor Images
Corrección cromática aplicada a imágenes de exteriores
Hayde Peregrina–Barreto1, J. Gabriel Aviña–Cervantes1, Iván R. Terol–Villalobos2, José J. Rangel–Magdaleno1, and Ana M. Herrera–Navarro3
1 Universidad de Guanajuato, Guanajuato, Mexico. Correo: hperegrina@ieee.org, avina@ugto.mx, jjrangel@ieee.org
]]> 2 Centro de Investigación y Desarrollo Tecnológico en Electroquímica, Querétaro, Mexico. Correo: famter@ciateq.net.mx.3 Universidad Autónoma de Querétaro, Querétaro, Mexico. Correo: anaherreranavarro@gmail.com.
Article received on 10/03/2010.
Accepted on 17/12/2010.
Abstract
The color of an image may be affected by many factors such as illumination, complex and multi–spectral reflections, and even the acquisition device. Especially in outdoor scenes, these conditions cannot be controlled. In order to use the information of an image, the latter must present the information as closer as possible to the original scene. Sometimes images are affected by a dominant color (cast) that changes its chromatic information. In order to avoid this effect, a color correction must be done. In this work, a novel method for correcting the color of outdoor images is proposed. This method consists in a complete improvement process of three steps: cast detection, color correction, and color improvement.
Keywords: Cast detection, color correction, chromatic adaptation, natural outdoor images, color enhancement.
]]> Resumen
El color de una imagen puede ser alterado por muchos factores como iluminación, reflexiones complejas y multi–espectrales e incluso por el dispositivo de adquisición, especialmente en escenas en exteriores estas condiciones no pueden ser controladas. Con el fin de utilizar la información de una imagen, esta debe presentarse lo más cercano posible a la escena original. Algunas veces, las imágenes se ven afectadas por un color dominante (cast) que altera su información cromática. Para eliminar este efecto, se debe realizar una corrección de color. En este trabajo se presenta un novedoso método para corregir imágenes de exteriores. Este método consiste en un proceso de mejora completo de tres pasos: detección de matiz, corrección de color y mejora de color.
Palabras clave: Detección de matiz, corrección de color, adaptación cromática, imágenes naturales de exteriores, realce de color.
DESCARGAR ARTÍCULO EN FORMATO PDF
References
1. Agarwal, V., Abidi, B.R., Koschan, A., & Abidi, M.A. (2006). An overview of color constancy algorithms. Journal of Pattern Recognition Research, 1(1), 42–54. [ Links ]
2. Albers, J. (1975). Interaction of color: text of the original edition with revised plate section. New Haven: Yale University Press. [ Links ]
3. Angulo, J. & Serra, J. (2005). Segmentación de Imágenes en Color utilizando Histogramas Bi–Variables en Espacios Color Polares Luminancia/Saturación/Matiz. Computación y Sistemas, 8(4), 303–316. [ Links ]
4. Aufrere, R., Marion, V., Laneurit, J., Lewandowski, C., Morillon, J., & Chapuis, R. (2004). Road sides recognition in non–structured environments by vision. 2004 IEEE Intelligent Vehicles Symposium, Parma, Italy, 329–334. [ Links ]
5. Aviña–Cervantes, G., Devy, M., & Marin–Hernández, A. (2003). Lane Extraction and Tracking for robot navigation in agricultural applications. 11th International Conference on Advanced Robotics, Coimbra, Portugal, 816–821. [ Links ]
6. Aviña–Cervantes, J.G. & Devy, M. (2004). Scene Modeling by ICA and Color Segmentation. MICAI 2004: Advances in Artificial Intelligence, Lecture Notes in Computer Science, 2972, 574–583. [ Links ]
7. Bianco, G.M. & Rizzi, A. (2002). Chromatic adaptation for robust visual navigation. Advanced Robotics, 16(3), 217–232. [ Links ]
8. Buchsbaum, G. (1980). A spatial processor model for object colour perception. Journal of the Franklin Institute, 310(1), 1–26. [ Links ]
9. Cardei, V.C., Funt, B., & Barnard, K. (2002). Estimating the scene illumination chromaticity by using a neural network. Journal of the Optical Society of America. A, Optics, image science, and vision, 19(12) 2374–2386. [ Links ]
10. Cheung, H.K, Siu, W.C., Feng, D., & Wang, Z. (2008). Retinex based motion estimation for sequences with brightness variations and its application to H.264. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2008), Las Vegas, Nevada, U.S.A., 11611164. [ Links ]
11. Ciocca, G., Marini, D., Rizzi, A., Schettini, R., & Zuffi, S. (2003). Retinex preprocessing of uncalibrated images for color based image retrieval. Journal of Electronic Imaging, 12(1), 161–172. [ Links ]
12. Comaniciu, D. & Meer, P. (1997). Robust analysis of feature spaces: color image segmentation. 1997 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Juan, Puerto Rico, 750–755. [ Links ]
13. Deng, Y., Manjunath, B.S., & Shin, H. (1999). Color image segmentation. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'99), Fort Collins, Colorado, 2, 446–451. [ Links ]
14. Ebner, M. (2003). Combining white–patch retinex and the gray world assumption to achieve color constancy. Pattern recognition, Lecture Notes in Computer Science, 2781, 60–67. [ Links ]
15. Fairchild, M.D. (1996). Refinement of the RLAB color space. Color Research and Application, 21(5), 338–346. [ Links ]
16. Fairchild, M.D. (1998). Color Appearance Models. Reading, Mass.: Addison–Wesley Eds. [ Links ]
17. Finlayson, G.D., & Süsstrunk, S. (2000). Performance of a chromatic adaptation transform based on spectral sharpening. IS&T/SID 8th Color Imaging Conference, Scottsdale, AZ, USA, 8, 4955. [ Links ]
18. Finlayson, G.D., Hordley, S.D., & Hubel, P.M. (2001). Color by correlation: a simple, unifying framework for color constancy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(11), 1209–1221. [ Links ]
19. Finlayson, G., Hordley, S., Schaefer, G., & Tian, G.Y. (2005). Illuminant and device invariant colour using histogram equalization. Pattern recognition, 38(2), 179–190. [ Links ]
20. Funt, B. & Ciurea, F. (2004). Retinex in MATLAB. Electronic Imaging, 13(1), 48–57. [ Links ]
21. Gasparini, F. & Schettini, R. (2004). Color balancing of digital photos using simple image statistics. Pattern Recognition, 37(6), 1201–1217. [ Links ]
22. Gershon, R., Jepson, A.D., & Tsotsos, J.K. (1987). From [R,G,B] to surface reflectance: computing color constant descriptors in images. 10th International Joint Conference on Artificial Intelligence, Milan, Italy, 2, 755–758. [ Links ]
23. Gijsenij, A. & Gevers, T. (2007). Color constancy using natural image statistics. IEEE Conference on Computer Vision and Pattern Recognition (CVPR'07), Minneapolis, MN, USA, 1–8. [ Links ]
24. Hanbury, A. & Serra, J. (2002). Mathematical morphology in CIELab space. Image Analysis and Steoreology, 21(3), 201–206. [ Links ]
25. Hasler, D. & Süsstrunk, S. (2004). Mapping colour in image stitching applications. Journal of Visual Communication and Image Representation, 15(12), 65–90. [ Links ]
26. Helmholtz, H.V. (1962). Helmholtz's treatise on physiological optics. New York: Dover Publications. [ Links ]
27. Katoh, N. & Nakabayashi, K. (2001). Applying mixed adaptation to various chromatic adaptation transformation (CAT) models. Image Processing, Image Quality, Image Capture Systems Conference (PICS–01), Montréal, Canada, 299–305. [ Links ]
28. Kraft, J.M. & Brainard, D.H. (1999). Mechanisms of color constancy under nearly natural viewing. Proceedings of the National Academy of Sciences of the United States of America, 96(1), 307–312. [ Links ]
29. Land, E.H. & McCann, J.J. (1971). Lightness and retinex theory. Journal of the Optical Society of America, 61(1), 1–11. [ Links ]
30. Land, E.H. (1997). The retinex theory of color constancy. Scientific American, 237, 108–128. [ Links ]
31. Li, S., Kwok, J.T., Zhu, H., & Wang, Y. (2003). Texture classification using the support vector machines. Pattern recognition, 36(12), 2883–2893. [ Links ]
32. Marini, D. & Rizzi, A. (2000). A Computational Approach to Color Adaptation Effects. Image and Vision Computing, 18(3), 1005–1014. [ Links ]
33. Mateus, D., Avina, G., & Devy, M. (2005). Robot visual navigation in semi–structured outdoor environments. 2005 IEEE International Conference on Robotics and Automation, Barcelona, Spain, 4691–4696. [ Links ]
34. Rahman, Z., Jobson, D.J., & Woodell, G.A. (2004). Retinex processing for automatic image enhancement. Journal of Electronic Imaging, 13(1), 100–110. [ Links ]
35. Rasmussen, C. (2002). Combining Laser Range, Color and Texture Cues for Autonomous Road Following. IEEE International Conference on Robotics and Automation, Washington, D.C., 4, 4320–4325. [ Links ]
36. Rizzi, A., Gatta, C., & Marini, D. (2004). From retinex to automatic color equalization: issues in developing a new algorithm for unsupervised color equalization. Journal of Electronic Imaging, 13(1), 75–84. [ Links ]
37. Rosenberg, C., Hebert, M., & Thrun, S. (2001). Image Color Constancy Using KL–Divergence. Eighth IEEE International Conference on Computer Vision, Vancouver, Canada, 1, 239–246. [ Links ]
38. Süsstrunk, S., Holm, J., & Finlayson, G.D. (2001). Chromatic adaptation performance of different RGB sensors. Electronic Imaging: Device–Independent Color, Color Hardcopy, and Graphic Arts VI, 4300, 172–183. [ Links ]
39. Zhang, J. & Tan, T. (2002). Brief review of invariant texture analysis methods. Pattern Recognition, 35(3), 735–747. [ Links ]
]]>