Lecture 1, Friday 11.9.2009
Introduction
slides

Lecture 2, Wednesday 16.9.2009

Spectrum estimation from RGB-values
Related articles:

* Pratt, Mancill: Spectral estimation techniques for the spectral calibration of a color image scanner. Applied Optics, Vol. 15, No. 1, Jan 1976, pp. 73-75
* Solli et al.: Color Measurements with a Consumer Digital Camera Using Spectral Estimation Techniques. Lecture Notes in Computer Science Vol. 3540, Springer-Verlag, Berlin, 2005, pp. 105-114.
* Morovic, Finlayson: Metamer-set-based approach to estimating surface reflectance from camera RGB. Journal of Optical Society of America A, Vol. 23, No. 8, Aug 2006, pp. 1814-1822.
* Heikkinen et al.: Regularized learning framework in the estimation of reflectance spectra from camera responses. Journal of Optical Society of America A, Vol. 24, No. 9, Sep 2007, pp. 2673-2683.
* Shimano et al.: Recovery of spectral reflectances of objects being imaged by multispectral cameras. Journal of Optical Society of America A, Vol. 24, No. 10, Oct 2007, pp. 3211-3219.

Lecture 3, Friday 18.9.2009

Professor Tominaga, lecture slides

Lecture 4, Wednesday 23.9.2009

Professor Tominaga, lecture slides

Lecture 5, Friday 25.9.2009

Introduction to pair tasks 1 and 2
Practical example of Wiener estimation: RGB --> spectrum
* Stigell, Miyata, Hauta-Kasari: Wiener estimation method in estimating of spectral reflectance from RGB images. Pattern Recognition and Image Analysis, Vol. 17, Nr. 2, June 2007.

Lecture 6, Wednesday 30.9.2009

Principal Component Analysis
slides
* Parkkinen, Hallikainen, Jaaskelainen: Characteristic spectra of Munsell colors. Journal of Optical Society of America A, Vol. 6, Issue 2, pp.318-322, 1989.

Lecture 7, Friday 2.10.2009

Non-negative Matrix Factorization (NMF) and Non-negative Tensor Factorization (NTF)
slides
* Daniel D. Lee, H. Sebastian Seung: Learning the parts of objects by non-negative matrix factorization. Nature Vol. 401, Oct 1999, pp. 788-791.
* Stefan A. Robila, Lukasz G. Maciak: Considerations on Parellelizing Nonnegative Matrix Factorization for Hyperspectral Data Unmixing. IEEE Geoscience and Remote Sensing Letters, Vol. 6, No. 1, Jan 2009, pp. 57-61 (can be found at the IEEE Xplore database )
* T. Hazan, S. Polak, A. Shashua: Sparse Image Coding Using a 3D Non-Negative Tensor Factorization. Proceedings of the International Conference on Computer Vision (ICCV), pp. 50-57, 2005.

Lecture, Wednesday 7.10.2009

Work in pairs for practical exercise task. No lecture.

Lecture 8, Friday 9.10.2009

Deadline for the first two practical exercises, introduction to the next practical exercises.
Introduction to the color research in Joensuu.

Lecture 9, Wednesday 14.10.2009

Introduction to optimal selection of colors for reflectance reconstruction.
* Hui-Liang Shen, Hong-Gamg Zhang, John H. Xin, and Si-Jie Shao: Optimal selection of representative colors for spectral reflectance reconstruction in a multispectral imaging system. Applied Optics, Vol. 47, No. 13, May 2008, pp. 2494-2502.

Lecture 10, Friday 16.10.2009

Lecture, Wednesday 21.10.2009

no lecture

Lecture 11, Friday 23.10.2009

Computational approaches to the color naming.
slides
* G. Buchsbaum, O. Bloch: Color categories revealed by non-negative matrix factorization of Munsell color spectra. Vision Research, Vol. 42, 2002, pp. 559-563.