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SUMMARY:LLLS #18: Graph Multi-Cuts and Image Analysis
DTSTAMP:20230105T110000
ORGANIZER;CN=SCADS:MAILTO:webmaster@puls13.com
DTSTART:20230105T110000
DTEND:20230105T120000
CATEGORIES:Living Lab
URL;VALUE=URI:https://scads.ai/living-lab-en/online-lecture-series/graph-multi-cuts-and-image-analysis/
DESCRIPTION;ENCODING=QUOTED-PRINTABLE:In this lecture, we learn how graph theory and combinatorial optimization can be used to solve problems from the field of image analysis. We consider the challenging task of segmenting a three dimensional volume image of an electron microscopy scan of a mouse neocortex. The state of the art method for solving this segmentation problem consists of a convolutional neural network and a post-processing step, which involves solving the graph multi-cut problem. We will see how segmenting an image can be understood as a problem of cutting a graph into multiple components. The optimal multi-cut can be found by means of integer linear programming. This method, however, does not scale to large instances. To address this problem, we present a second algorithm that finds a good but not necessarily optimal solution efficiently. Finally, we will see that the multi-cut problem and algorithms have further applications beyond image segmentation. https://scads.ai/living-lab-en/online-lecture-series/graph-multi-cuts-and-image-analysis/
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