Continuous Morphology and Distance Maps Ron Kimmel www.cs.technion.ac.il/~ron Computer Science Department Technion-Israel Institute of Technology Geometric.

Slides:



Advertisements
Similar presentations
Disk Bezier curves Slides made by:- Mrigen Negi Instructor:- Prof. Milind Sohoni.
Advertisements

Fast Marching on Triangulated Domains
Problem Set 2, Problem # 2 Ellen Dickerson. Problem Set 2, Problem #2 Find the equations of the lines that pass through the point (1,3) and are tangent.
電腦視覺 Computer and Robot Vision I
Developable Surface Fitting to Point Clouds Martin Peternell Computer Aided Geometric Design 21(2004) Reporter: Xingwang Zhang June 19, 2005.
Recovery of affine and metric properties from images in 2D Projective space Ko Dae-Won.
Geometry Primer Lines and rays Planes Spheres Frustums Triangles Polygon Polyhedron.
Course Syllabus 1.Color 2.Camera models, camera calibration 3.Advanced image pre-processing Line detection Corner detection Maximally stable extremal regions.
Image Segmentation Image segmentation (segmentace obrazu) –division or separation of the image into segments (connected regions) of similar properties.
Course Syllabus 1.Color 2.Camera models, camera calibration 3.Advanced image pre-processing Line detection Corner detection Maximally stable extremal regions.
Morphology – Chapter 10. Binary image processing Often it is advantageous to reduce an image from gray level (multiple bits/pixel) to binary (1 bit/pixel)
Each pixel is 0 or 1, background or foreground Image processing to
Medical Imaging Mohammad Dawood Department of Computer Science University of Münster Germany.
Chapter 9 Morphological Image Processing. Preview Morphology: denotes a branch of biology that deals with the form and structure of animals and planets.
Level Set Formulation for Curve Evolution Ron Kimmel Computer Science Department Technion-Israel Institute of Technology Geometric.
Offset of curves. Alina Shaikhet (CS, Technion)
Multiple View Geometry Projective Geometry & Transformations of 2D Vladimir Nedović Intelligent Systems Lab Amsterdam (ISLA) Informatics Institute,
Numerical Geometry of Images: Shape Reconstruction Ron Kimmel Geometric Image Processing Lab Computer Science Department Technion-Israel.
Numerical geometry of non-rigid shapes
1 Preprocessing for JPEG Compression Elad Davidson & Lilach Schwartz Project Supervisor: Ari Shenhar SPRING 2000 TECHNION - ISRAEL INSTITUTE of TECHNOLOGY.
1 GEOMETRIE Geometrie in der Technik H. Pottmann TU Wien SS 2007.
EE663 Image Processing Edge Detection 2 Dr. Samir H. Abdul-Jauwad Electrical Engineering Department King Fahd University of Petroleum & Minerals.
Introduction to Calculus of Variations Ron Kimmel Computer Science Department Technion-Israel Institute of Technology Geometric.
Introduction to Differential Geometry
Scale Invariant Feature Transform (SIFT)
1 Numerical geometry of non-rigid shapes A journey to non-rigid world objects Numerical methods non-rigid Alexander Bronstein Michael Bronstein Numerical.
1 Precise Voronoi Cell Extraction of Free-form Rational Planar Closed Curves Iddo Hanniel, Ramanathan Muthuganapathy, Gershon Elber Department of Computer.
Fat Curves and Representation of Planar Figures L.M. Mestetskii Department of Information Technologies, Tver’ State University, Tver, Russia Computers.
Planar Curve Evolution Ron Kimmel Computer Science Department Technion-Israel Institute of Technology Geometric Image Processing.
CS 376b Introduction to Computer Vision 04 / 15 / 2008 Instructor: Michael Eckmann.
Starter Activity Write the equation of a circle with a center of
The Planar-Reflective Symmetry Transform Princeton University.
Solve Systems of Linear Equations in Three Variables Chapter 3.4.
October 14, 2014Computer Vision Lecture 11: Image Segmentation I 1Contours How should we represent contours? A good contour representation should meet.
Curves.
Horizontal Curves Chapter 24.
Under Supervision of Dr. Kamel A. Arram Eng. Lamiaa Said Wed
Generalized Hough Transform
Lecture 6 : Level Set Method
Line detection Assume there is a binary image, we use F(ά,X)=0 as the parametric equation of a curve with a vector of parameters ά=[α 1, …, α m ] and X=[x.
Course 13 Curves and Surfaces. Course 13 Curves and Surface Surface Representation Representation Interpolation Approximation Surface Segmentation.
CVPR 2003 Tutorial Recognition and Matching Based on Local Invariant Features David Lowe Computer Science Department University of British Columbia.
Equations Reducible to Quadratic
1 Lecture #6 Variational Approaches and Image Segmentation Lecture #6 Hossam Abdelmunim 1 & Aly A. Farag 2 1 Computer & Systems Engineering Department,
1) What does x have to be for 3x = 0? 1) What does x have to be for 3(x -2) = 0 2) What does x have to be for (x–2) (x+3) = 0.
Circles in the Coordinate Plane I can identify and understand equations for circles.
Section 2.4 – Circles Circle – a set of points in a plane that are equidistant from a fixed point.
Geometric Modeling with Conical Meshes and Developable Surfaces SIGGRAPH 2006 Yang Liu, Helmut Pottmann, Johannes Wallner, Yong-Liang Yang and Wenping.
2.1 – Linear and Quadratic Equations Linear Equations.
References Books: Chapter 11, Image Processing, Analysis, and Machine Vision, Sonka et al Chapter 9, Digital Image Processing, Gonzalez & Woods.
Warm-Up What is the distance between the two trees? If you wanted to meet a friend halfway, where would you meet.
6-2 Conic Sections: Circles Geometric definition: A circle is formed by cutting a circular cone with a plane perpendicular to the symmetry axis of the.
The Circle. Examples (1) – (5) Determine the center and radius of the circle having the given equation. Identify four points of the circle.
3.3 Separation of Variables 3.4 Multipole Expansion
 A cylinder has two identical flat ends that are circular and one curved side.  Volume is the amount of space inside a shape, measured in cubic units.
Image Features (I) Dr. Chang Shu COMP 4900C Winter 2008.
Digital Image Processing, Spring ECES 682 Digital Image Processing Week 8 Oleh Tretiak ECE Department Drexel University.
Computer Graphics CC416 Lecture 04: Bresenham Line Algorithm & Mid-point circle algorithm Dr. Manal Helal – Fall 2014.
Equation of Circle Midpoint and Endpoint Distance Slope
Plane and Space Curves Curvature-based Features
VORONOI DIAGRAMS FOR PARALLEL HALFLINES IN 3D
A quadratic equation is written in the Standard Form,
Warm-up Solve using the quadratic formula: 2x2 + x – 5 =0
Solving Quadratic Equations by Factoring
Quadratic Equations.
Nur Hasan Mahmud Shahen Lecturer of Mathematics. Department of Computer Science & Engineering (CSE). University- Institute of Science Trade & Technology,
Page 12 Directions: C’ B B’ C A A’
Solving Simultaneous equations by the Graphical Method
Antonio Plaza University of Extremadura. Caceres, Spain
Find the following limit. {image}
Presentation transcript:

Continuous Morphology and Distance Maps Ron Kimmel Computer Science Department Technion-Israel Institute of Technology Geometric Image Processing Lab

Given a closed planar curve Define the distance map Distance map

Distance Map Properties Almost everywhere The level sets of, given by are the offsets of C

Distance Map Properties By Huygens principle a level sets of, is given by the envelope of all disks of radius c centered on the curve C. The new shape is also known as `dilation’ with a circular `structuring element’ of the shape.

Distance Map Properties The distance map represents the set, generated by the curve evolution with the right `entropy condition’

Distance Map Properties The vector is pointing to the closest point on the zero set

How to Compute? Accuracy/Efficiency Q1: How to compute the distance from a single `source point’? Given T(k,l)=0, find Solution: So ???

How to Compute? Q2: How to compute the distance from two `source points’? Given T(k,l)=0, find Solution: Complexity:

How to Compute? Q3: can it be computed in O(N) ? Solution: Danielson algorithm. 4 scans algorithm with alternating directions (up/down left/right) Ask your 4 neighbors their coordinate offset to the closest detected source point. Compute your offset to that point and decide if you change your choice of closest source point Initial offset is

Danielson algorithm O(N)

Alternative solution We can also use a numeric scheme to solve the ‘eikonal’ equation Initialize all source points and all non-source points Solve the quadratic equation for given by the upwind monotone approximation of the eikonal equation Again, use the 4 scans with alternating directions

3D/4D/…nD All these methods can be extended to higher dimensions. 1D->2 2D->4 3D->8 4D->16

Continuous Morphology Structuring element

? Morphology and dual spaces

Gray Scale Erosion and Dilation Level set by level set: E.g. Cylinder (level set circle)

1.Segment at local curvature maxima 2.Compute distance map from each segment 3.Find intersection sets of the distance functions 4.Prune the tails Skeletons and level sets