![]() ![]() Geodesic methods for Biomedical Image Segmentation I'll briefly demonstrate a way to detect repetitive patterns using this framework. ![]() Such energies facilitate a kind of "hierarchical MDL" criterion and should be useful in detecting multiple objects, motions, homographies, and much more. #Hyperplan vectoriel how to#I'll talk about how to do better using a "2-level MRF" defined over "super-labels." The second part of the talk will present some unpublished work on energies with a hierarchy of "label costs" and how to optimize them effectively. assumption is inappropriate for generating an image, then it is equally inappropriate for generating complex objects *within* the image (person, car), and yet this is what the standard setup entails. and this is simply not a good model for natural images. Everyone agrees that the MRF aspect is important, because without it the pixels are assumed i.i.d. Super-Labels and Hierarchical Label CostsĪndrew Delong - University of Western OntarioĪbstract: The first part of the talk will be about a simple segmentation functional similar to Boykov-Jolly / GrabCut, The standard setup includes a GMM for foreground label, another for the background, and an MRF for regularization. Taken together, these three pieces constitute the first system for truly "end-to-end" learning of image segmentation, where all parameters in the algorithm are adjusted to directly minimize segmentation error. I will also present new work in each of these areas: 1) a segmentation algorithm based on convolutional networks as boundary detectors, 2) the Rand index as a measure of segmentation quality, and 3) the MALIS algorithm, based on ultrametric learning, for training boundary detectors to optimize the Rand index segmentation measure. Such algorithms have three basic components: 1) a parametrized function for producing segmentations from images, 2) an objective function that quantifies the performance of a segmentation algorithm relative to ground truth, and 3) a means of searching the parameter space of the segmentation algorithms for an optimum of the objective function. Supervised machine learning is a powerful tool for creating accurate image segmentation algorithms that are well adapted to any dataset. In this talk, I will present a new machine learning method for the supervised learning of image segmentation. And high-throughput, highly-accurate, automatic image segmentation is the most important technology for the success of connectomics. ![]() It is still early days and much effort is still dedicated to developing good methods for mapping neural networks. Srini Turaga - Sebastian Seung's lab at MITĪbstract: Connectomics is a new research effort in neuroscience dedicated to mapping the connectivity of real biological neural networks in the brain. "End-to-end" machine learning of image segmentation (for Connectomics) LIGM - Equipe A3SI : seminaire de recherche Séminaire de recherche A3SI ![]()
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