Jonathan Zung will present his general exam on Wednesday, May 18, 2016 at 4pm in CS 402. The members of his committee are Sebastian Seung (adviser), Tom Funkhouser, and Szymon Rusinkiewicz. Everyone is invited to attend his talk, and those faculty wishing to remain for the oral exam following are welcome to do so. His abstract and reading list follow below. Bishop, Christopher M. "Pattern Recognition." Machine Learning (2006). Szeliski, Richard. Computer vision: algorithms and applications. Springer Science & Business Media, 2010. Arbelaez, Pablo, et al. "Contour detection and hierarchical image segmentation." Pattern Analysis and Machine Intelligence, IEEE Transactions on 33.5 (2011): 898-916. Wirjadi, Oliver. Survey of 3d image segmentation methods. Vol. 35. ITWM, 2007. Yang, Mingqiang, Kidiyo Kpalma, and Joseph Ronsin. "A survey of shape feature extraction techniques." Pattern recognition (2008): 43-90. Briggman, Kevin, et al. "Maximin affinity learning of image segmentation." Advances in Neural Information Processing Systems. 2009. Jain, Viren, et al. "Learning to agglomerate superpixel hierarchies." Advances in Neural Information Processing Systems. 2011. Bogovic, John A., Gary B. Huang, and Viren Jain. "Learned versus hand-designed feature representations for 3d agglomeration." arXiv preprint arXiv:1312.6159 (2013). Lee, Honglak, et al. "Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations." Proceedings of the 26th Annual International Conference on Machine Learning. ACM, 2009. Long, Jonathan, Evan Shelhamer, and Trevor Darrell. "Fully convolutional networks for semantic segmentation." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015. Noh, Hyeonwoo, Seunghoon Hong, and Bohyung Han. "Learning deconvolution network for semantic segmentation." Proceedings of the IEEE International Conference on Computer Vision. 2015. Kumar, M. Prema, P. H. S. Ton, and Andrew Zisserman. "Obj cut." Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on. Vol. 1. IEEE, 2005. Borenstein, Eran, and Shimon Ullman. "Combined top-down/bottom-up segmentation." Pattern Analysis and Machine Intelligence, IEEE Transactions on 30.12 (2008): 2109-2125. Yang, Yi, et al. "Layered object models for image segmentation." Pattern Analysis and Machine Intelligence, IEEE Transactions on 34.9 (2012): 1731-1743. Abstract: My research has been motivated by the problem of segmenting 3d electron microscopy images of brain tissue in order to reconstruct neural circuits. Such images require instance level segmentation (we want to label each individual neuron separately) and are extremely cluttered (the image is densely filled with cells). The gap between human and machine performance on this task seems to lie in the ability of humans to focus on individual objects and make use of priors on their shapes. State of the art techniques for this problem rely on supervised deep neural networks to perform boundary detection. I will present an approach to augmenting such networks with an attentional mechanism with the goal of forcing the network to focus on a single object at a time, thereby allowing better use of priors on the shapes of individual objects.