Brian Matejek will present his MSE thesis "Learning Global Features for Neuron Reconstruction in EM Images" today, March 4, 2016 at 4:30pm in CS 401. Tom Funkhouser (adviser) and Sebastian Seung are his committee members. All are welcome to join. Please see thesis title and abstract below. Title: Learning Global Features for Neuron Reconstruction in EM Images Abstract: Connectomics, the study of the neural connections, has gained significant attention in the neuroscience community as a method to understand the complex workings of the brain. Recent advancements in neural image acquisition has created a surplus of data too expensive to manually annotate leading to an influx of automatic reconstruction methods. Most current methods employ hierarchical agglomeration techniques that focus on extracted local features. These techniques fail to utilize overall global structure of neurons and other valuable shape descriptors, leading to early errors which compound over time. This paper introduces novel global and local features for predicting merge candidates in oversegmented neural images. Testing on the SNEMI3D challenge dataset, these new features produce great improvement on traditional agglomeration algorithms.