Optic flow from dynamic anchor point attributes a feasibility study Bart Janssen.

Slides:



Advertisements
Similar presentations
Spike Based Visual Encoding Activity level (a m ) Visual encoder implemented in the NEF as network of 1024 laterally inhibiting neural columns Network.
Advertisements

Bayesian Belief Propagation
Andrew Cosand ECE CVRR CSE
Ter Haar Romeny, ICPR 2010 Introduction to Scale-Space and Deep Structure.
Topology-Caching for Dynamic Particle Volume Raycasting Jens Orthmann, Maik Keller and Andreas Kolb, University of Siegen.
What is vision Aristotle - vision is knowing what is where by looking.
Generalized Mosaics Yoav Y. Schechner, Shree Nayar Department of Computer Science Columbia University.
Optimization & Learning for Registration of Moving Dynamic Textures Junzhou Huang 1, Xiaolei Huang 2, Dimitris Metaxas 1 Rutgers University 1, Lehigh University.
Or where do the pixels move? Alon Gat. Given: two or more frames of an image sequence Wanted: Displacement field between two consecutive frames  optical.
Bayesian Robust Principal Component Analysis Presenter: Raghu Ranganathan ECE / CMR Tennessee Technological University January 21, 2011 Reading Group (Xinghao.
Computer Vision Optical Flow
A New Block Based Motion Estimation with True Region Motion Field Jozef Huska & Peter Kulla EUROCON 2007 The International Conference on “Computer as a.
“Mimicking cortical responses in the visual cortex” Presentation 27 May 2004 Florie Daniels Lotte Verbunt.
Feature tracking. Identify features and track them over video –Small difference between frames –potential large difference overall Standard approach:
Enhanced images watermarking based on amplitude modulation Image and Vision Computing, 24 (2006), 111 – 119 Authors :T. Amornraksa, K. Janthawongwilai.
Optical Flow Methods 2007/8/9.
Probabilistic video stabilization using Kalman filtering and mosaicking.
Ter Haar Romeny, FEV Nuclei of fungus cell Paramecium Caudatum Spatial gradient Illumination spectrum -invariant gradient Color RGB original Color-Scale.
Feature matching and tracking Class 5 Read Section 4.1 of course notes Read Shi and Tomasi’s paper on.
Static Image Mosaicing
CSc83029 – 3-D Computer Vision/ Ioannis Stamos 3-D Computational Vision CSc Optical Flow & Motion The Factorization Method.
Optical Flow
Combining Laser Scans Yong Joo Kil 1, Boris Mederos 2, and Nina Amenta 1 1 Department of Computer Science, University of California at Davis 2 Instituto.
Motion Computing in Image Analysis
Augmented Reality: Object Tracking and Active Appearance Model
Motion Field and Optical Flow. Outline Motion Field and Optical Flow Definition, Example, Relation Optical Flow Constraint Equation Assumptions & Derivation,
KLT tracker & triangulation Class 6 Read Shi and Tomasi’s paper on good features to track
Motion from normal flow. Optical flow difficulties The aperture problemDepth discontinuities.
3D Computer Vision and Video Computing 3D Vision Topic 8 of Part 2 Visual Motion (II) CSC I6716 Spring 2004 Zhigang Zhu, NAC 8/203A
Optical flow (motion vector) computation Course: Computer Graphics and Image Processing Semester:Fall 2002 Presenter:Nilesh Ghubade
Measuring and Calculating the Dynamic Response of Simple Structures AOE 3054 E. Nikolaidis.
Mohammed Rizwan Adil, Chidambaram Alagappan., and Swathi Dumpala Basaveswara.
MASKS © 2004 Invitation to 3D vision Lecture 3 Image Primitives andCorrespondence.
When Does a Camera See Rain? Kshitiz Garg, Shree K. Nayar ICCV’ 05.
Exploiting video information for Meeting Structuring ….
1 Robust estimation techniques in real-time robot vision Ezio Malis, Eric Marchand INRIA Sophia, projet ICARE INRIA Rennes, projet Lagadic.
Top-points in Image Matching Bram Platel Evgenya Balmashnova Luc Florack Bart ter Haar Romeny.
Optical Flow Donald Tanguay June 12, Outline Description of optical flow General techniques Specific methods –Horn and Schunck (regularization)
Linear Image Reconstruction Bart Janssen 13-11, 2007 Eindhoven.
Image Processing Jitendra Malik. Different kinds of images Radiance images, where a pixel value corresponds to the radiance from some point in the scene.
A General-Purpose Platform for 3-D Reconstruction from Sequence of Images Ahmed Eid, Sherif Rashad, and Aly Farag Computer Vision and Image Processing.
Danny Ruijters 17 April D-3D intra-interventional registration of coronary arteries.
Efficient Color Boundary Detection with Color-opponent Mechanisms CVPR2013 Posters.
Features, Feature descriptors, Matching Jana Kosecka George Mason University.
Motion / Optical Flow II Estimation of Motion Field Avneesh Sud.
Introduction to Scale Space and Deep Structure. Importance of Scale Painting by Dali Objects exist at certain ranges of scale. It is not known a priory.
Extremum seeking without external dithering and its application to plasma RF heating on FTU University of Rome “Tor Vergata” Luca Zaccarian, Daniele Carnevale,
MASKS © 2004 Invitation to 3D vision Lecture 3 Image Primitives andCorrespondence.
Kalman Filter and Data Streaming Presented By :- Ankur Jain Department of Computer Science 7/21/03.
A. M. R. R. Bandara & L. Ranathunga
Motion and Optical Flow
Computer Vision, Robotics, Machine Learning and Control Lab
Chapter 8 An Improvement of Bottom-Up Variable-Sized Block Matching Technique for Video Compression.
CSE 577 Image and Video Analysis
Optical Flow Estimation and Segmentation of Moving Dynamic Textures
Image Primitives and Correspondence
Contents Abstract Introduction VRD (Virtual Retinal Display) Basics
Smoothing the intensities
Outline: 5.1 INTRODUCTION
A New Approach in Processing Atomistic Simulations
Nonlinear processing in LGN neurons
SUSPICIOUS ACTIVITY DETECTION
IMAGE SAMPLING AND IMAGE QUANTIZATION 1. Introduction
Filtering Things to take away from this lecture An image as a function
IEEE/ASME TRANSACTIONS ON MECHATRONICS, VOL. 10, NO. 4, AUGUST 2005
Outline: 5.1 INTRODUCTION
Differentiate the function:    {image} .
Coupled Horn-Schunck and Lukas-Kanade for image processing
Open Topics.
Outline Human Visual Information Processing – cont.
Presentation transcript:

Optic flow from dynamic anchor point attributes a feasibility study Bart Janssen

Optic Flow Medical Image Registration Video Processing / Coding Robotics

The notion of scale: Objects live at different scales Solution? Look at all scales simultaniously

Scale Space in Human Vision The human visual system is a multi-scale sampling device The retina contains receptive fields; groups of receptors assembled in such a way that they form a set of apertures of widely varying size.

Gaussian Scale Space Scale x y

The Aperture problem: Constant brightness assumption is insufficient

The Aperture problem: Constant brightness assumption is insufficient Solution? Use points were the apperture problem is non- existent

Proposed Method

Outline Introduction Anchor Points Stationary Reconstruction Flow Reconstruction

Anchor points Singular Points of a Gaussian scale space image Fold catastrophe

Static Properties Differential structure at the location of a Singular Point by innerproduct of the image with derivatives of the Gaussian

Dynamic Properties

Outline Introduction Anchor Points Stationary Reconstruction Flow Reconstruction

Stationary reconstruction

Optic flow reconstruction

Conclusions

Differential Structure

Error Measure

Feedback: Privacy Policy Feedback
Do Not Sell My Personal Information
About project: SlidePlayer Terms of Service

© 2025 SlidePlayer.com. Inc. All rights reserved.