Shadow-invariant classification for scenes illuminated by daylight.

A physics-based method for shadow compensation in scenes illuminated by daylight is proposed. If the daylight is represented by a simplified form of the blackbody law and the camera filters are of infinitely narrow bandwidth, the relationship between red/blue (rm) and green/blue (gm) ratios as the blackbody's temperature changes is a simple power law where the exponent is independent of the surface reflectivity. When the CIE daylight model is used instead of the blackbody and finite bandwidths for the camera are assumed, it is shown that the power law still holds with a slight change to the exponent. This means that images can be transformed into a map of rm/gmA and then thresholded to yield a shadow-independent classification. Exponent A can be precalculated from the CIE daylight model and the camera filter characteristics. Results are shown for four outdoor images that contain sunny and shadowed parts with vegetation and background. It is shown that the gray-level distributions of the pixels in the transformed images are quite similar for a given component whether or not it is in shadow. The transformation leads to bimodal histograms from which thresholds can easily be selected to give good classifications.

[1]  C. A. Shull.  A Spectrophotometric Study of Reflection of Light from Leaf Surfaces , 1929, Botanical Gazette.

[2]  W. D. Billings,et al.  REFLECTION OF VISIBLE AND INFRARED RADIATION FROM LEAVES OF DIFFERENT ECOLOGICAL GROUPS , 1951 .

[3]  D. B. Judd,et al.  Spectral Distribution of Typical Daylight as a Function of Correlated Color Temperature , 1964 .

[4]  Gunther Wyszecki,et al.  Color Science: Concepts and Methods, Quantitative Data and Formulae, 2nd Edition , 2000 .

[5]  J. Woolley.  Reflectance and transmittance of light by leaves. , 1971, Plant physiology.

[6]  G. O. Harries,et al.  A photoelectric sensor for distinguishing between plant material and soil , 1976 .

[7]  M. H. Brill,et al.  Necessary and sufficient conditions for Von Kries chromatic adaptation to give color constancy , 1982, Journal of mathematical biology.

[8]  Steven A. Shafer,et al.  Using color to separate reflection components , 1985 .

[9]  H C Lee,et al.  Method for computing the scene-illuminant chromaticity from specular highlights. , 1986, Journal of the Optical Society of America. A, Optics and image science.

[10]  John K. Tsotsos,et al.  Ambient illumination and the determination of material changes. , 1986, Journal of the Optical Society of America. A, Optics and image science.

[11]  Andrew Blake,et al.  Detecting Specular Reflections Using Lambertian Constraints , 1988, [1988 Proceedings] Second International Conference on Computer Vision.

[12]  S. Maas,et al.  Reflectance, Transmittance, and Absorptance of Light by Normal, Etiolated, and Albino Corn Leaves , 1989 .

[13]  B. Wandell,et al.  Standard surface-reflectance model and illuminant estimation , 1989 .

[14]  Hsien-Che Lee,et al.  Modeling Light Reflection for Computer Color Vision , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  J. Norman,et al.  Leaf Optical Properties , 1991 .

[16]  Charles E. Thorpe,et al.  SCARF: a color vision system that tracks roads and intersections , 1993, IEEE Trans. Robotics Autom..

[17]  G D Finlayson,et al.  Spectral sharpening: sensor transformations for improved color constancy. , 1994, Journal of the Optical Society of America. A, Optics, image science, and vision.

[18]  M. S. Drew,et al.  Color constancy - Generalized diagonal transforms suffice , 1994 .

[19]  J. A. Marchant,et al.  SEGMENTATION OF PLANTS AND WEEDS USING INFRARED IMAGES , 1996 .

[20]  Shoji Tominaga,et al.  MULTICHANNEL VISION SYSTEM FOR ESTIMATING SURFACE AND ILLUMINATION FUNCTIONS , 1996 .

[21]  Shigeki Nakauchi,et al.  A Computational Model for Color Constancy by Separating Reflectance and Illuminant Edges within a Scene , 1996, Neural Networks.

[22]  Stéphane Perrin,et al.  CCD camera modeling and simulation , 1996, J. Intell. Robotic Syst..

[23]  Andrew Blake,et al.  Learning Dynamics of Complex Motions from Image Sequences , 1996, ECCV.

[24]  Brian V. Funt,et al.  Color Constancy for Scenes with Varying Illumination , 1997, Comput. Vis. Image Underst..

[25]  Gian Luca Foresti,et al.  Object detection and tracking in time-varying and badly illuminated outdoor environments , 1998 .

[26]  N. D. Tillett,et al.  Computer-Vision-based Hoe Guidance for Cereals — an Initial Trial , 1999 .

[27]  Jie Wei,et al.  Illumination-invariant image retrieval and video segmentation , 1999, Pattern Recognit..

[28]  B. Duval.  Commission internationale de l’éclairage (CIE) , 2001, Optique Photonique.