Bibliography

(eng): Our paper addresses problem of multimodal data acquisition and data visualization for art analysis. Various types of modalities of digital images are used for art analysis. The data we can obtain differ in two ways. The images can differ by their mutual geometry and radiometric quality. These are the differences we would like to remove. The group of differences we are interested in are details or characteristics of an artwork, which are apparent just in the certain modality. The two groups represent two categories of image processing methods we have to deal with. The first one is represented by image preprocessing methods such as data enhancement and restoration, the second class includes effective ways how to combine the acquired information into one image - image fusion. In our paper we present image enhancement for microscopic multimodal data and their segmentation and recent results in data fusion and visualization for art analysis from the second category of methods.