Computing a Family of Skeletons of Volumetric Models for Shape Description Tao Ju Washington University in St. Louis.

Slides:



Advertisements
Similar presentations
Department of Computer Science and Engineering Defining and Computing Curve-skeletons with Medial Geodesic Function Tamal K. Dey and Jian Sun The Ohio.
Advertisements

CSE554Cell ComplexesSlide 1 CSE 554 Lecture 3: Skeleton and Thinning (Part II) Fall 2013.
CSE554Cell ComplexesSlide 1 CSE 554 Lecture 3: Shape Analysis (Part II) Fall 2014.
Bio-CAD M. Ramanathan Bio-CAD. Molecular surfaces Bio-CAD.
1/20 Using M-Reps to include a-priori Shape Knowledge into the Mumford-Shah Segmentation Functional FWF - Forschungsschwerpunkt S092 Subproject 7 „Pattern.
Course Syllabus 1.Color 2.Camera models, camera calibration 3.Advanced image pre-processing Line detection Corner detection Maximally stable extremal regions.
Extended Gaussian Images
CDS 301 Fall, 2009 Image Visualization Chap. 9 November 5, 2009 Jie Zhang Copyright ©
Chapter 9: Morphological Image Processing
Course Syllabus 1.Color 2.Camera models, camera calibration 3.Advanced image pre-processing Line detection Corner detection Maximally stable extremal regions.
Discrete Geometry Tutorial 2 1
A Simple and Robust Thinning Algorithm on Cell Complexes
Computer Graphics Group Alexander Hornung Alexander Hornung and Leif Kobbelt RWTH Aachen Robust Reconstruction of Watertight 3D Models from Non-uniformly.
The generic model of a modular machine vision system
Provides mathematical tools for shape analysis in both binary and grayscale images Chapter 13 – Mathematical Morphology Usages: (i)Image pre-processing.
Atomic Volumes for Mesh Completion Joshua Podolak Szymon Rusinkiewicz Princeton University.
Image Segmentation some examples Zhiqiang wang
CSE554ContouringSlide 1 CSE 554 Lecture 4: Contouring Fall 2013.
1 Minimum Ratio Contours For Meshes Andrew Clements Hao Zhang gruvi graphics + usability + visualization.
1 Processing & Analysis of Geometric Shapes Shortest path problems Shortest path problems The discrete way © Alexander & Michael Bronstein, ©
Morphology Structural processing of images Image Processing and Computer Vision: 33 Morphological Transformations Set theoretic methods of extracting.
Chapter 9 Morphological Image Processing. Preview Morphology: denotes a branch of biology that deals with the form and structure of animals and planets.
Data Structures For Image Analysis
International Workshop on Computer Vision - Institute for Studies in Theoretical Physics and Mathematics, April , Tehran 1 II SIZE FUNCTIONS:
Randomized Cuts for 3D Mesh Analysis
Visualization and graphics research group CIPIC January 30, 2003Multiresolution (ECS 289L) - Winter MAPS – Multiresolution Adaptive Parameterization.
A Global Geometric Framework for Nonlinear Dimensionality Reduction Joshua B. Tenenbaum, Vin de Silva, John C. Langford Presented by Napat Triroj.
Continuous Morphology and Distance Maps Ron Kimmel Computer Science Department Technion-Israel Institute of Technology Geometric.
3D Thinning on Cell Complexes for Computing Curve and Surface Skeletons Lu Liu Advisor: Tao Ju Master Thesis Defense Dec 18 th, 2008.
Introduction --Classification Shape ContourRegion Structural Syntactic Graph Tree Model-driven Data-driven Perimeter Compactness Eccentricity.
Lecture 5. Morphological Image Processing. 10/6/20152 Introduction ► ► Morphology: a branch of biology that deals with the form and structure of animals.
A lightweight approach to repairing digitized polygon meshes Marco Attene IMATI-GE / CNR 2010 Presented by Naitsat Alexander.
Shape Modeling and Matching in Protein Structure Identification Sasakthi Abeysinghe, Tao Ju Washington University, St. Louis, USA Matthew Baker, Wah Chiu.
Digital Image Processing Chapter 9: Morphological Image Processing 5 September 2007 Digital Image Processing Chapter 9: Morphological Image Processing.
Extended Grassfire Transform on Medial Axes of 2D Shapes
Chapter 3 cont’d. Binary Image Analysis. Binary image morphology (nonlinear image processing)
Vision-based human motion analysis: An overview Computer Vision and Image Understanding(2007)
Roee Litman, Alexander Bronstein, Michael Bronstein
Introduction --Classification Shape ContourRegion Structural Syntactic Graph Tree Model-driven Data-driven Perimeter Compactness Eccentricity.
CSE554SkeletonsSlide 1 CSE 554 Lecture 2: Shape Analysis (Part I) Fall 2015.
Thinning & Distance Field Advisor : Ku-Yaw Chang Speaker : Jhen-Yu Yang.
CSE554ContouringSlide 1 CSE 554 Lecture 4: Contouring Fall 2015.
Blood Vessel Modeling using 2D/3D Level Set Method
Outline Introduction Research Project Findings / Results
Methods for 3D Shape Matching and Retrieval
References Books: Chapter 11, Image Processing, Analysis, and Machine Vision, Sonka et al Chapter 9, Digital Image Processing, Gonzalez & Woods.
CS654: Digital Image Analysis
CDS 301 Fall, 2008 Image Visualization Chap. 9 November 11, 2008 Jie Zhang Copyright ©
EE 4780 Morphological Image Processing. Bahadir K. Gunturk2 Example Two semiconductor wafer images are given. You are supposed to determine the defects.
Morphological Image Processing Robotics. 2/22/2016Introduction to Machine Vision Remember from Lecture 12: GRAY LEVEL THRESHOLDING Objects Set threshold.
ECE472/572 - Lecture 14 Morphological Image Processing 11/17/11.
Announcements Final is Thursday, March 18, 10:30-12:20 –MGH 287 Sample final out today.
Lecture(s) 3-4. Morphological Image Processing. 3/13/20162 Introduction ► ► Morphology: a branch of biology that deals with the form and structure of.
Chapter 6 Skeleton & Morphological Operation. Image Processing for Pattern Recognition Feature Extraction Acquisition Preprocessing Classification Post.
Sheng-Fang Huang Chapter 11 part I.  After the image is segmented into regions, how to represent and describe these regions? ◦ In terms of its external.
Digital Image Processing Lecture 15: Morphological Algorithms April 27, 2005 Prof. Charlene Tsai.
CSE 554 Lecture 2: Shape Analysis (Part I)
Mathematical Morphology
CSE 554 Lecture 1: Binary Pictures
Qualitative Curve Descriptions
Predicting ligand binding sites on protein surface
CS Digital Image Processing Lecture 5
Neutrosophic Mathematical Morphology for Medical Image
Morphological Image Processing
Digital Image Processing Lecture 15: Morphological Algorithms
CSE 554 Lecture 3: Shape Analysis (Part II)
Handwritten Characters Recognition Based on an HMM Model
Contextual connections in shape model
ECE 692 – Advanced Topics in Computer Vision
Filtering An image as a function Digital vs. continuous images
Presentation transcript:

Computing a Family of Skeletons of Volumetric Models for Shape Description Tao Ju Washington University in St. Louis

Skeleton A medial representation of an object – Thin (dimension reduction) – Preserving shape and topology

Where Skeletons Are Used Animating characters – Skeletal animation Shape analysis – Shape comparison – Character recognition Medical applications – Colon unwinding – Modeling blood vessels

New Application – Protein Modeling Identifying tubular and plate-like shapes is the key in locating α-helices and β-sheets in Cryo-EM protein maps Atomic Model Secondary Structures Cryo-EM map at intermediate resolution α β Tube Plate

Curvature Descriptors Depicting surface properties – Principle curvatures, shape index [Koenderink 92] – Cons: Easily disrupted by a bumpy surface Min Curvature Max Curvature Shape Index

Intuition Represent tubes and plates as skeleton curves and surfaces. = =   Skeleton

Thinning Classical method for computing skeleton of a discrete image V. Iterative process – At each iteration, remove boundary points from V – Retain non-simple boundary points Topology preservation [Bertrand 94] – Retain curve-end or surface-end boundary points Shape preservation [Tsao 81] [Gong 90] [Lee 94] [Bertrand 94] [Bertrand 95] Curve thinning or surface thinning Result in curve skeleton or surface skeleton

Problems Curve skeleton: containing mostly 1D edges Surface skeleton: contains mostly 2D faces Volume Image Curve Skeleton Surface Skeleton

Goal Compute simple and descriptive skeletons – Consists of curves and surfaces corresponding to tubes and plates Solution – Alternate thinning and pruning

Method Overview – Step 1 Surface Thinning Surface Pruning

Method Overview – Step 2 Curve Thinning Curve Pruning

End Points – A Geometric Definition Curves and surfaces – Consists of edges and faces Curve-end and surface-end points – Points not contained in any 1-manifold or 2-manifold 1-manifold2-manifold

Theorem Let V be the set of object points. x is a curve-end point if and only if: x is a surface-end point if and only if: = 0 N k (x,V)=N k (x)  V

Pruning Coupling erosion and dilation – Erosion: removes all curve-end (surface-end) points. – Dilation: extends discrete 1-manifold (2-manifold) from curve- end (surface-end) points. – d rounds of erosion followed by d rounds of dilation Erode Dilate

Surface Pruning Example d = 4 d = 7 d = 10

Curve Pruning Example d = 5 d = 10 d = 20 [Mekada and Toriwaki 02] [Svensson and Sanniti di Baja 03]

Results – 3D Models Original[Bertrand 95][Ju et al. 06]

Results – 3D Models OriginalSkeletons with different pruning parameters

Results – Protein Data Cryo-EM[Bertrand 95][Ju et al. 06]Actual Structure

Visualization: UCSF Chimera Cryo-EMSkeletonActual StructureOverlay

Collaboration and Outlook Future work – Descriptive skeleton of grayscale images – Descriptive skeleton on adaptive grids (octrees) – Protein model building Finding connectivity among α/β elements Using graph matching (Skeleton vs. protein sequence) Collaboration – National Center of Macromolecular Imaging (NCMI), Houston (M. Baker, S. Ludtke, W. Chiu)NCMI

Thinning Example Original[Bertrand 95] Surface thinning Curve thinning