BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Pier Luigi Dragotti (Department of Electrical and Electronic Engin eering\, Imperial College\, London) DTSTART:20211209T170000Z DTEND:20211209T180000Z DTSTAMP:20260423T003253Z UID:MPML/61 DESCRIPTION:Title: Co mputational Imaging for Art investigation and for Neuroscience\nby Pie r Luigi Dragotti (Department of Electrical and Electronic Engineering\, Im perial College\, London) as part of Mathematics\, Physics and Machine Lear ning (IST\, Lisbon)\n\n\nAbstract\nThe revolution in sensing\, with the em ergence of many new imagingtechniques\, offers the possibility of gaining unprecedented access tothe physical world\, but this revolution can only b ear fruit through the skilful interplay between the physical and computati onal worlds. This is the domain of computational imaging which advocates t hat\, to develop effective imaging systems\, it will be necessary to go be yond the traditional decoupled imaging pipeline where device physics\, ima ge processing and the end-user application are considered separately. Inst ead\, we need to rethink imaging as an integrated sensing and inference mo del. In this talk we cover two research areas where computational imaging is likely to have an impact.\n\nWe first focus on the heritage sector whic h is experiencing a digital revolution driven in part by the increasing us e of non-invasive\, non-destructive imaging techniques. These new imaging methods provide a way to capture information about an entire painting and can give us information about features at or below the surface of the pain ting. We focus on Macro X-Ray Fluorescence (XRF) scanning which is a techn ique for the mapping of chemical elements in paintings. After describing i n broad terms the working of this device\, a method that can process XRF s canning data from paintings is introduced. The method is based on connecti ng the problem of extracting elemental maps in XRF data to Prony's method\ , a technique broadly used in engineering to estimate frequencies of a sum of sinusoids. The results presented show the ability of our method to det ect and separate weak signals related to hidden chemical elements in the p aintings. We then discuss results on the Leonardo's "The Virgin of the Roc ks" and show that our algorithm is able to reveal\, more clearly than ever before\, the hidden drawings of a previous composition that Leonardo then abandoned for the painting that we can now see.\n\nIn the second part of the talk\, we focus on two-photon microscopy and neuroscience. To understa nd how networks of neurons process information\, it is essential to monito r their activity in living tissue. Multi-photon microscopy is unparalleled in its ability to image cellular activity and neural circuits\, deep in l iving tissue\, at single-cell resolution. However\, in order to achieve st ep changes in our understanding of brain function\, large-scale imaging st udies of neural populations are needed and this can be achieved only by de veloping computational tools that can enhance the quality of the data acqu ired and can scan 3-D volumes quickly. In this talk we introduce light-fie ld microscopy and present a method to localize neurons in 3-D. The method is based on the use of proper sparsity priors\, novel optimization strateg ies and machine learning.\n\n\nThis is joint work with A. Foust\, P. Song\ , C. Howe\, H. Verinaz\, J. Huang and Y.Su from Imperial College London\, and C. Higgitt and N. Daly from The National Gallery in London\n LOCATION:https://researchseminars.org/talk/MPML/61/ END:VEVENT END:VCALENDAR