Abstract: This lecture will review several instances where mathematics have helped art historians and art conservators in studying and understanding art works in the last decade or so. Some of them led (and are still leading) to interesting new challenges in signal and image analysis. In other applications, we can virtually rejuvenate art works, bringing a different understanding and experience of the art to museum visitors as well as to experts.
NB: PDF version of this announcement (suitable for posting).
Abstract: This talk gives an overview of wavelets: what they are, how they work, why they are useful for image analysis and image compression. Then it will go on to discuss how they have been used recently for the study of paintings by e.g. Van Gogh, Goossen van der Weyden, Gauguin and Giotto.
FYI (this particular talk can be given without a single mathematical formula, or at various levels of mathematical complexity: for undergrads, for graduate students, or very technical. It is also possible to give the wide-audience talk first, and then revisit it, pausing at various places to digress and explain what is going on mathematically.)
Abstract: The talk will present mathematical explorations motivated by the need of biological morphologists to compare different phenotypical structures. At present, scientists using physical traits to study evolutionary relationships among living and extinct animals analyze data extracted from carefully defined anatomical correspondence points (landmarks). Identifying and recording these landmarks is time consuming and can be done accurately only by trained morphologists. This necessity renders these studies inaccessible to non-morphologists and causes phenomics to lag behind genomics in elucidating evolutionary patterns.
Unlike other algorithms presented for morphological correspondences, the approach presented in the talk does not require any preliminary marking of special features or landmarks by the user. It also differs from other seminal work in computational geometry in that the algorithms are polynomial in nature and thus faster, making pairwise comparisons feasible for significantly larger numbers of digitized surfaces.
This approach has already been used by biologists to obtain new results. And there are many further avenues to be explored!