PPT-Graph cut
Author : luanne-stotts | Published Date : 2016-03-08
Chien chi Chen 1 Outline 2 Introduction Interactive segmentation Related work Graph cut Concept of graph cut Hard and smooth constrains Min cutMax flow Extensive
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Graph cut: Transcript
Chien chi Chen 1 Outline 2 Introduction Interactive segmentation Related work Graph cut Concept of graph cut Hard and smooth constrains Min cutMax flow Extensive of Graph cut Grab cut. Why graph clustering is useful?. Distance matrices are graphs . as useful as any other clustering. Identification of communities in social networks. Webpage clustering for better data management of web data. P. artitioning. a. nd. . Clustering for. Community . Detection. . Presented By: Group One. 1. Outline. Introduction: . Hong . Hande. Graph Partitioning: . Muthu. Kumar . C . and . Xie . Shudong. . Liangliang. (Leon) . Nan. 1. , . Andrei . Sharf. 2. , . . Ke. . Xie. 1. , . . Tien-Tsin. . Wong. 3. . Oliver . Deussen. 4. , . Daniel . Cohen-Or. 5. , . . Baoquan. . Chen. 1 . Why graph clustering is useful?. Distance matrices are graphs . as useful as any other clustering. Identification of communities in social networks. Webpage clustering for better data management of web data. Isabelle Stanton, UC Berkeley. Gabriel . Kliot. , Microsoft Research XCG. Modern graph datasets are huge. The web graph had over a trillion links in 2011. Now?. . facebook. has “more than 901 million users with average degree 130”. 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. C. HEN. , J. IAXIN. S. HI. , Y. ANZHE. C. HEN. , H. AIBO. C. HEN. Institute of Parallel and Distributed Systems. Shanghai Jiao Tong University, China. H. EuroSys 2015. L. Differentiate. d. . Graph Computation and Partitioning on Skewed Graphs. Graph Partitioning (cuts, spectral clustering, density), . Community evolution. 1. Introduction. modules, cluster, communities, groups, . partitions (more on this today). 2. Summary of Part I. PART . Agenda. History of the problem. Graph cut background. Compute graph cut. Extensions. State . of the art. Continued Work. Agenda. History of the problem. Graph cut background. Compute graph cut. Extensions. Distance matrices are graphs . as useful as any other clustering. Identification of communities in social networks. Webpage clustering for better data management of web data. Outline. Min s-t cut problem. Minimum cut. Recall that a . cut. in a graph is a partition of its vertices into two non-empty subsets A, B. The . size. of a cut is the number of edges from vertices in A to vertices in B. I.e., the number of edges . cutty. ”. This means taking out unnecessary pauses between actors’ delivery of dialogue lines. Sometimes it . means . tightening the gaps within dialogue sentences through the use of carefully placed cutaways. It may also mean losing redundant lines of dialogue, after the director has reviewed your cut.. dpc. = [ 9Bc / 2vi (p- g)]0.5 . where . dpc. or [. dp. ]cut = cut diameter . = viscosity, lb/ft . s (Pa . s) . N = effective number of turns (5–10 for the common cyclone) . vi = inlet gas velocity, ft/s (m/s) .
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