Therefore, the spectra are an overlap of different, and often unknown, reflectance spectra . To identify the material and their corresponding abundance automatically we use prior information based on the physics of the problem. We are currently studying the applicability of these spectral analysis methods to medical applications. Note almost perfect correspondence with targets. Points represent pixel intensities for two wavelengths.
Circles represent reflectance of the endmembers.
A typical pattern recognition task consists in deriving a classification rule from a set of example images. Rather than building an actual classifier one can also build a probability model for the example images. The advantage of such an approach is that the same models may also be used for compression or sampling, i.e. we can synthesize an images that are likely according to the examples used by the model. The synthesized examples have to look like the training examples, otherwise there is something wrong with the model. Motivated by much previous research and some careful math we have designed a probability model which can be considered a Hidden Markov model on a tree for the wavelet coefficients of an image pyramid.