PPT-CSE 554 Lecture 9: Laplacian Deformation

Fall 2016 Review Alignment Registering source to target by rotation and translation Rigidbody transformations Methods Aligning principle directions PCA Aligning corresponding points SVD Iterative improvement ICP . 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 . Computer Vision, winter 2012-13. CS Department, . Technion. Topics. The Gaussian Pyramid. The . Laplacian. Pyramid. Applications:. Pattern Matching. Coding (Compression). Enhancement. Blending. Gaussian Pyramid. Achieving scale covariance. Goal: independently detect corresponding regions in scaled versions of the same image. Need . scale selection. mechanism for finding characteristic region size that is . covariant. Lecture 10: Extrinsic Deformations. Fall . 2015. Review. Non-rigid deformation. Intrinsic. methods: deforming the boundary points. An optimization problem. Minimize shape distortion. Maximize fit. Example: . Lecture 9: Laplacian Deformation. Fall . 2015. Review. Alignment. Registering source to target by rotation and translation. Rigid-body transformations. Methods. Aligning principle directions (PCA). Aligning corresponding points (SVD). 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 . 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 . Kinematic analysis of deformation. B. Natalin. Stress, strain, and deformation. The . stress . (σ) acting on a plane is the force per unit area of the plane (σ = . F. /area).. The. deformation . refers to changes in shape, position, or orientation of a body resulting from the application of a differential stress (i.e., a state in which the magnitude of stress is not the same in all directions).. M. Zollhöfer, E. Sert, G. Greiner and J. Süßmuth. Computer Graphics Group, University Erlangen-Nuremberg, Germany. Motivation/Requirements. Intuitive modeling. Handle-based. Direct manipulation. 2. mailing . list:. http. ://www.wisdom.weizmann.ac.il/~. vision/courses/2017_1/intro_to_vision/index.html. (or just google . “Weizmann Vision”).. 2D Image. Fourier Spectrum. Convolution. Good for:. Matrices of Graphs:. Algorithms and Applications. ICML, June 21, 2016. Daniel A. Spielman. Laplacians. . Interpolation on graphs. Spring networks. . Clustering. . Isotonic regression. Sparsification. Kazhdan. [. Taubin. , 1995] . A Signal Processing Approach to Fair Surface . Design. [. Desbrun. , . et al.. , 1999] Implicit Fairing of Arbitrary Meshes…. [. Vallet. and Levy, 2008] . Spectral Geometry Processing with Manifold . Classical plate theory (CPT), of which classical lamination theory (CLT) assume that there is no shear deformation.. Strains vary linearly through the thickness and normal remain normal (. Kirchoff. -Love assumptions, 1888).. . Parameter. on . Reaction. Cross . Section. Ismail . Hakki. SARPUN. Abdullah AYDIN. Mahmut. BOYUKATA. Objective. Deformation parameter of the nuclei is one of the important arguments for the nuclear structure studies. .