PPT-Laplacian

Matrices of Graphs Algorithms and Applications ICML June 21 2016 Daniel A Spielman Laplacians Interpolation on graphs Spring networks Clustering Isotonic regression Sparsification. ca Abstract The problem of determining the eigenvalues and eigenvectors for linear operators acting on 64257nite dimensional vector spaces is a problem known to every student of linear algebra This problem has a wide range of applications and is one Computational Photography. Connelly Barnes. Many slides from . James Hays, Alexei . Efros. Blending + Compositing. Previously:. Color perception in humans, cameras. Bayer mosaic. Color spaces (L*a*b*, RGB, HSV). Feature detection with . s. cale selection. We want to extract features with characteristic scale that is . covariant. with the image transformation. Blob detection: Basic idea. To detect blobs, convolve the image with a “blob filter” at multiple scales and look for . Classification Outline. Introduction, Overview. Classification using Graphs. Graph classification – Direct Product Kernel. Predictive Toxicology example dataset. Vertex classification – . Laplacian. Computer Vision Lecture 9: Edge Detection II. 1. Laplacian Filters. Idea:. . Smooth . the image,. compute the . second derivative. Surfaces. 2D/3D Shape Manipulation,. 3D Printing. CS 6501. Slides from Olga . Sorkine. , . Eitan. . Grinspun. Surfaces, Parametric Form. Continuous surface. Tangent plane at point . p. (. u,v. ). is spanned by. Begue. ). The Heat Equation on Fractals and other Discrete Domains. Outline. Introduction to . Fractals. Contractions maps and the self-similar identity. The Graph Laplacian. The Heat Equation. The Cycle . c. t. r. a. l. methods. © Alexander & Michael Bronstein, 2006-2009. © Michael Bronstein, 2010. tosca.cs.technion.ac.il/book. 048921 Advanced topics in vision. Processing and Analysis of Geometric Shapes. keypoint. detection. D. Lowe, . Distinctive . image features from scale-invariant . keypoints. ,. . IJCV. 60 (2), pp. 91-110, 2004. . Keypoint. detection with . s. cale selection. We want to extract . Edges = jumps in brightness/color. Brightness jumps marked in white. Edges. Edges = jumps in brightness/color. Important!. Give object outlines and . shapes. Brightness jumps marked in white. Edges. Edges = jumps in brightness/color. r. s. t. u. v. w. x. y. z. Origin. x. y. Image f (x, y). e. processed. = . v. *e + . . r. *a + . s. *b + . t. *c + . . u. *d + . w. *f + . . x. *g + . y. *h + . z. *. i. Filter. Simple 3*3. Neighbourhood. Sushmita Roy. sroy@biostat.wisc.edu. Computational Network Biology. Biostatistics & Medical Informatics 826. Computer Sciences 838. https://compnetbiocourse.discovery.wisc.edu. Nov 3. rd. , Nov 10. Motion Editing. Manmyung. Kim, . Kyunglyul. Hyun, . Jongmin. Kim, . Jehee. Lee. Seoul National University. Multi-character Interaction. : synchronization in space and time . Cumbersome to Maintain Synchronization. Jacob Beal. Social Concepts in Self-Adaptive and Self-Organising Systems. IEEE SASO. September, 2013. Asymmetry trades robustness for speed. Spanning Tree. Consensus. Laplacian. Consensus. O(diameter).