Spectral reflectance estimation from camera responses by support vector regression and a composite model.

Regression methods are widely used to estimate the spectral reflectance of object surfaces from camera responses. These methods are under the same problem setting as that to build an estimation function for each sampled wavelength separately, which means that the accuracy of the spectral estimation will be reduced when the training set is small. To improve the spectral estimation accuracy, we propose a novel estimating approach based on the support vector regression method. The proposed approach utilizes a composite modeling scheme, which formulates the RGB values and the sampled wavelength together as the input term to make the most use of the information from the training samples. Experimental results show that the proposed method can improve the recovery accuracy when the training set is small.

[1]  Jon Y. Hardeberg,et al.  Filter Selection for Multispectral Color Image Acquisition , 2004, PICS.

[2]  M. Bertero,et al.  Ill-posed problems in early vision , 1988, Proc. IEEE.

[3]  Hui-Liang Shen,et al.  Estimation of spectral reflectance of object surfaces with the consideration of perceptual color space. , 2007, Optics letters.

[4]  N. Shimano,et al.  Recovery of spectral reflectances of objects being imaged without prior knowledge , 2006, IEEE Transactions on Image Processing.

[5]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[6]  Stéphane Canu,et al.  Frames, Reproducing Kernels, Regularization and Learning , 2005, J. Mach. Learn. Res..

[7]  Changjun Li,et al.  Characterization of trichromatic color cameras by using a new multispectral imaging technique. , 2005, Journal of the Optical Society of America. A, Optics, image science, and vision.

[8]  David Connah,et al.  Spectral recovery using polynomial models , 2005, IS&T/SPIE Electronic Imaging.

[9]  L. Maloney.  Evaluation of linear models of surface spectral reflectance with small numbers of parameters. , 1986, Journal of the Optical Society of America. A, Optics and image science.

[10]  Raimondo Schettini,et al.  A computational strategy exploiting genetic algorithms to recover color surface reflectance functions , 2006, Neural Computing and Applications.

[11]  Peyman Milanfar,et al.  Kernel Regression for Image Processing and Reconstruction , 2007, IEEE Transactions on Image Processing.

[12]  Ao Li,et al.  Missing value estimation for DNA microarray gene expression data by Support Vector Regression imputation and orthogonal coding scheme , 2006, BMC Bioinformatics.

[13]  H Haneishi,et al.  System design for accurately estimating the spectral reflectance of art paintings. , 2000, Applied optics.

[14]  Chih-Jen Lin,et al.  Working Set Selection Using Second Order Information for Training Support Vector Machines , 2005, J. Mach. Learn. Res..

[15]  Noriyuki Shimano,et al.  Recovery of spectral reflectances of objects being imaged by multispectral cameras. , 2007, Journal of the Optical Society of America. A, Optics, image science, and vision.

[16]  L. Maloney,et al.  Color constancy: a method for recovering surface spectral reflectance. , 1986, Journal of the Optical Society of America. A, Optics and image science.

[17]  Peter A. Rhodes,et al.  A study of digital camera colorimetric characterisation based on polynomial modelling , 2001 .

[18]  Ville Heikkinen,et al.  Regularized learning framework in the estimation of reflectance spectra from camera responses. , 2007, Journal of the Optical Society of America. A, Optics, image science, and vision.

[19]  Stephen Westland,et al.  A comparative study of the characterisation of colour cameras by means of neural networks and polynomial transforms , 2004 .

[20]  Tomaso A. Poggio,et al.  Regularization Networks and Support Vector Machines , 2000, Adv. Comput. Math..

[21]  Hui-Liang Shen,et al.  Reflectance reconstruction for multispectral imaging by adaptive Wiener estimation. , 2007, Optics express.

[22]  Francis Eng Hock Tay,et al.  Support vector machine with adaptive parameters in financial time series forecasting , 2003, IEEE Trans. Neural Networks.