Chromatic Correction Applied to Outdoor Images

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.

[1]  Dorin Comaniciu,et al.  Robust analysis of feature spaces: color image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[2]  Tieniu Tan,et al.  Brief review of invariant texture analysis methods , 2002, Pattern Recognit..

[3]  Graham D. Finlayson,et al.  Color by Correlation: A Simple, Unifying Framework for Color Constancy , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Mark D. Fairchild,et al.  Refinement of the RLAB Color Space , 1996 .

[5]  Jesús Angulo,et al.  Segmentación de Imágenes en Color Utilizando Histogramas Bi-Variables en Espacios Color Polares Luminancia/Saturación/Matiz , 2005, Computación y Sistemas.

[6]  Sabine Süsstrunk,et al.  Performance of a Chromatic Adaptation Transform based on Spectral Sharpening , 2000, CIC.

[7]  C. Munteanu,et al.  Color image enhancement using evolutionary principles and the Retinex theory of color constancy , 2001, Neural Networks for Signal Processing XI: Proceedings of the 2001 IEEE Signal Processing Society Workshop (IEEE Cat. No.01TH8584).

[8]  Yaonan Wang,et al.  Texture classification using the support vector machines , 2003, Pattern Recognit..

[9]  Raimondo Schettini,et al.  Color balancing of digital photos using simple image statistics , 2004, Pattern Recognit..

[10]  Michel Devy,et al.  Robot Visual Navigation in Semi-structured Outdoor Environments , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[11]  E. Land,et al.  Lightness and retinex theory. , 1971, Journal of the Optical Society of America.

[12]  Michael H. Brill,et al.  Color appearance models , 1998 .

[13]  Sabine Süsstrunk,et al.  Mapping colour in image stitching applications , 2004, J. Vis. Commun. Image Represent..

[14]  G. Avi,et al.  Lane Extraction and Tracking for Robot Navigation in Agricultural Applications , 2003 .

[15]  Kobus Barnard,et al.  Estimating the scene illumination chromaticity by using a neural network. , 2002, Journal of the Optical Society of America. A, Optics, image science, and vision.

[16]  Sabine Süsstrunk,et al.  Chromatic adaptation performance of different RGB sensors , 2000, IS&T/SPIE Electronic Imaging.

[17]  John K. Tsotsos,et al.  From [R, G, B] to Surface Reflectance: Computing Color Constant Descriptors in Images , 1987, IJCAI.

[18]  John J. McCann,et al.  Retinex in Matlab , 2000, CIC.

[19]  Michel Devy,et al.  Scene Modeling by ICA and Color Segmentation , 2004, MICAI.

[20]  Carlo Gatta,et al.  From Retinex to Automatic Color Equalization: issues in developing a new algorithm for unsupervised color equalization , 2004, J. Electronic Imaging.

[21]  A. Hanbury,et al.  MATHEMATICAL MORPHOLOGY IN THE CIELAB SPACE , 2011 .

[22]  Raimondo Schettini,et al.  Retinex preprocessing of uncalibrated images for color-based image retrieval , 2003, J. Electronic Imaging.

[23]  Theo Gevers,et al.  Color Constancy using Natural Image Statistics , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[24]  Gerald Schaefer,et al.  Illuminant and device invariant colour using histogram equalisation , 2005, Pattern Recognit..

[25]  Mark D. Fairchild,et al.  Color Appearance Models , 1997, Computer Vision, A Reference Guide.

[26]  M. Abidi,et al.  An Overview of Color Constancy Algorithms , 2006 .

[27]  Naoya Katoh,et al.  Applying Mixed Adaptation to Various Chromatic Adaptation Transformation (CAT) Models , 2001, PICS.

[28]  Martial Hebert,et al.  Color constancy using KL-divergence , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[29]  Alessandro Rizzi,et al.  Chromatic adaptation for robust visual navigation , 2002, Adv. Robotics.

[30]  W. Peddie,et al.  Helmholtz's Treatise on Physiological Optics , 1924, Nature.

[31]  Marc Ebner Combining White-Patch Retinex and the Gray World Assumption to Achieve Color Constancy for Multiple Illuminants , 2003, DAGM-Symposium.

[32]  B. S. Manjunath,et al.  Color image segmentation , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[33]  Wan-Chi Siu,et al.  Retinex based motion estimation for sequences with brightness variations and its application to H.264 , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[34]  R. Chapuis,et al.  Road sides recognition in non-structured environments by vision , 2004, IEEE Intelligent Vehicles Symposium, 2004.

[35]  Christopher Rasmussen,et al.  Combining laser range, color, and texture cues for autonomous road following , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[36]  D. Brainard,et al.  Mechanisms of color constancy under nearly natural viewing. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[37]  Alessandro Rizzi,et al.  A computational approach to color adaptation effects , 2000, Image Vis. Comput..

[38]  G. Buchsbaum A spatial processor model for object colour perception , 1980 .