PPT-Knowledge Graph and Corpus Driven Segmentation

and Answer Inference for Telegraphic Entityseeking Queries EMNLP 2014 Mandar Joshi Uma Sawant Soumen Chakrabarti IBM Research IIT Bombay Yahoo Labs IIT Bombay mandarj90inibmcom umacseiitbacin. BootCaT. corpora: building and evaluating a corpus of academic course descriptions. BOTWU. BootCaTters of the world unite!. Erika Dalan (University of Bologna). Outline. Background. Methodology. Results. Dutch . lAnguage. Investigation. of Summarization . technologY. Katholieke. . Universiteit. Leuven. Rijksuniversiteit. Groningen. Q-go. DAISY on one slide. Segmentation. Rhetorical. classification. : . Multi-criterion representation . for scene . understanding. Moos . Hueting. ∗ . Aron. . Monszpart. ∗ . Nicolas . Mellado. University College London. https://www.imagestore.ucl.ac.uk/home/pcache/10002/aa/black_ad80e.jpg. - continuous and discrete approaches . 2 : . Exact . and approximate techniques. . - non-submodular and high-order problems. 3: Multi-region segmentation (Milan). - high-dimensional applications . Segmentation and Optical Flow. Inspiration from psychology. The Gestalt school: Grouping is key to visual perception. “The whole is greater than the sum of its parts”. http://en.wikipedia.org/wiki/Gestalt_psychology. Lecture 28: Advanced topics in Image Segmentation. Image courtesy: IEEE, IJCV. Recap of Lecture 27. Clustering based Image segmentation. Mean Shift. Kernel density estimation. Application of Mean shift: Filtering, Clustering, Segmentation. By: A’laa . Kryeem. Lecturer: . Hagit. Hel-Or. What is . Segmentation from . Examples. ?. Segment an image based on one (or more) correctly segmented image(s) assumed to be from the same . domain. - continuous and discrete approaches . 2 : . Exact . and approximate techniques. . - non-submodular and high-order problems. 3: Multi-region segmentation (Milan). - high-dimensional applications . geobodies. Adam Halpert. ExxonMobil CEES Visit. 12 November 2010. S. tanford. . E. xploration. . P. roject. Why automate?. Save time. Manual salt-picking is tedious, time-consuming. Major bottleneck for iterative imaging/model-building. Roman Pryamonosov. INM RAS. , . MIPT . Work group of circulatory and vascular diseases modeling (INM RAS). RSF.  14-31-00024 (. mew laboratories. ). Outline. Definitions and instruments. Anatomical background. IEEE Transaction on pattern analysis and machine intelligence, November 2006. Leo Grady, Member, IEEE. Outline. Introduction. Algorithm. Dirichlet. Problem. Behavioral Properties. Result--Demo. 2. Introduction. Answer Inference for Telegraphic Entity-seeking . Queries. EMNLP 2014. Mandar Joshi. Uma Sawant. Soumen Chakrabarti. IBM Research. IIT Bombay, Yahoo Labs. IIT Bombay. mandarj90@in.ibm.com. uma@cse.iitb.ac.in. Mahalanobis. distance. MASTERS THESIS. By: . Rahul. Suresh. COMMITTEE MEMBERS. Dr.Stan. . Birchfield. Dr.Adam. Hoover. Dr.Brian. Dean. Introduction. Related work. Background theory: . Image as a graph. R. Garcia is supported by an NSF Bridge to the Doctorate Fellowships. .. The biological imaging group is supported by MH-086994, NSF-1039620, and NSF-0964114.. . Abstract. Automating segmentation of individual neurons in electron microscopic (EM) images is a crucial step in the acquisition and analysis of connectomes. It is commonly thought that approaches which use contextual information from distant parts of the image to make local decisions, should be computationally infeasible. Combined with the topological complexity of three-dimensional (3D) space, this belief has been deterring the development of algorithms that work genuinely in 3D. .