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81.3% Affine invariant feature detector and image correlation Martin Bujňák © 2004 Martin Bujňák,
Previous work… (simple and fast) Moravec detector –using small window –determining the average changes of image intensity –big-change implies feature © 2004 Martin Bujňák,
Previous work… (simple and fast) Harris, Stephens –analytically described and enhanced Moravec detector –correlation with neighbors replaced with simple calculations - fast © 2004 Martin Bujňák,
Works fine but,… good results, but –region is described by one point –additional information about local shape can be obtained by principal component analysis would help, but only for small very changes even though usable but –many feature points must be detected and robust brute force algorithm for image correspondence must be used (RANSAC…) © 2004 Martin Bujňák,
Idea Let the feature point be rotation, scale and translation invariant (or other similarity transformations)… –not the miracle, but we get more stable features –let the feature by intersection of "bigger or stronger" edge (figure 1) figure 1. Features in edge intersection. © 2004 Martin Bujňák,
Idea Build graph –feature is graph node –two features are connected if there exists direct (or long A-B are connected) edge between them. Edges are not oriented. A B X1 X2 X3 © 2004 Martin Bujňák,
Idea Feature information –neighborhood with other vertices – ordered by angle –distance ration to neighbor for each connection (marked red) A n1n2 n3 φ2φ2 φ1φ1 φ3φ3 φ4φ4 Φ = 0 © 2004 Martin Bujňák,
Advanced – pie – slice correlation get elliptical cut bounded by two edges and elliptical arc (and call it pie slice) A n1 n3 φ2φ2 φ1φ1 φ3φ3 φ4φ4 Φ = 0 n3-n1 pie slice Feature © 2004 Martin Bujňák,
Advanced – pie – slice correlation Correlation process –two features are at the first rotated to best fit incidence information –correlated each pie-slice separately (using standard similarity or dissimilarity measures) © 2004 Martin Bujňák,
Advanced – pie – slice correlation Results –more stable for affine and also for projective transformation –good “corners” will remain –feature don’t need to well correlate in all slices connectivity information helps graph topology speeds up RANSAC elimination pie-slice correlation give better matching probability than standard one. © 2004 Martin Bujňák,
H.P. Moravec, Visual mapping by a robot rover, Proc. of the 6th International Joint Conference on Artificial Intelligence, pp , 1979 C. Harris - M. Stephens, A combined corner and edge detector, Fourth Alvey Vision Conference, pp , 1988 R. Deriche - G. Giraudon, "A computational approach for corner and vertex detection", International Journal of Computer Vision, 1(2): , 1993 S.M. Smith - J.M. Brady. "SUSAN - a new approach to low level image processing". Int. Journal of Computer Vision, Vol.23, Nr.1, pp.45-78, 1997 K. Mikolajczyk - S.M. Smith. "Scale & affine invariant interest point detectors". INRIA Rhne-Alpes GRAVIR-CNRS, 655 av. de l’Europe, Montbonnot, France T. Kadir - A. Zisserman - M.Brady. "An affinne invariant salient region detector". Department of Engineering Science, University of Oxford, UK. M. Pollefeys - L. Van Gool - M. Vergauwen - F. Verbiest - K. Cornelis - J. Tops - R. Koch, Visual modeling with a hand-held camera, International Journal of Computer Vision 59(3), , 2004 References © 2004 Martin Bujňák,
Feature extraction: Corners 9300 Harris Corners Pkwy, Charlotte, NC.
1 Interest Operator Lectures lecture topics –Interest points 1 (Linda) interest points, descriptors, Harris corners, correlation matching –Interest points.
1 Interest Operators Find “interesting” pieces of the image –e.g. corners, salient regions –Focus attention of algorithms –Speed up computation Many possible.
Feature Extraction and Matching Feature Tracking Sudipta N Sinha Sep 19, 2006.
1 Interest Operators Harris Corner Detector: the first and most basic interest operator Kadir Entropy Detector and its use in object recognition SIFT interest.
CS654: Digital Image Analysis Lecture 37: Feature Representation and Matching Slide adapted from: Kristen Grauman, Derek Hoiem, A. Efros.
Features Jan-Michael Frahm. Feature extraction: Corners 9300 Harris Corners Pkwy, Charlotte, NC.
776 Computer Vision Jan-Michael Frahm, Enrique Dunn Spring 2013.
Automatic Matching of Multi-View Images Ed Bremer University of Rochester.
© 2005 Martin Bujňák, Martin Bujňák Supervisor : RNDr.
Keypoint extraction: Corners 9300 Harris Corners Pkwy, Charlotte, NC.
CSE 185 Introduction to Computer Vision Local Invariant Features.
Object Recognition Using Distinctive Image Feature From Scale-Invariant Key point D. Lowe, IJCV 2004 Presenting – Anat Kaspi.
Vision and SLAM Ingeniería de Sistemas Integrados Departamento de Tecnología Electrónica Universidad de Málaga (Spain) Acción Integrada –’Visual-based.
Phase Congruency Detects Corners and Edges Peter Kovesi School of Computer Science & Software Engineering The University of Western Australia.
1 Interest Operators Find “interesting” pieces of the image Multiple possible uses –image matching stereo pairs tracking in videos creating panoramas –object.
Interest points CSE P 576 Ali Farhadi Many slides from Steve Seitz, Larry Zitnick.
Computational Photography Prof. Feng Liu Spring /22/2015.
Instructor: Mircea Nicolescu Lecture 10 CS 485 / 685 Computer Vision.
Interest points CSE P 576 Larry Zitnick Many slides courtesy of Steve Seitz.
Course Syllabus 1.Color 2.Camera models, camera calibration 3.Advanced image pre-processing Line detection Corner detection Maximally stable extremal regions.
Instructor: Mircea Nicolescu Lecture 15 CS 485 / 685 Computer Vision.
Recognizing specific objects Matching with SIFT Original suggestion Lowe, 1999,2004.
CSCE 643 Computer Vision: Extractions of Image Features Jinxiang Chai.
CS4670: Computer Vision Kavita Bala Lecture 7: Harris Corner Detection.
Overview Harris interest points Comparing interest points (SSD, ZNCC, SIFT) Scale & affine invariant interest points Evaluation and comparison of different.
Feature extraction: Corners and blobs. Why extract features? Motivation: panorama stitching We have two images – how do we combine them?
Distinctive Image Features from Scale-Invariant Keypoints.
Lecture 7: Features Part 2 CS4670/5670: Computer Vision Noah Snavely.
Scale & Affine Invariant Interest Point Detectors Mikolajczyk & Schmid presented by Dustin Lennon.
Feature Detection. Description Localization More Points Robust to occlusion Works with less texture More Repeatable Robust detection Precise localization.
11 Scale Invariant Feature Transform (SIFT) David G. Lowe University of British Columbia.
Lecture 6: Feature matching CS4670: Computer Vision Noah Snavely.
Scale-Invariant Feature Transform (SIFT) Jinxiang Chai.
CS4670: Computer Vision Kavita Bala Lecture 8: Scale invariance.
Distinctive Image Feature from Scale-Invariant KeyPoints David G. Lowe, 2004.
Overview Introduction to local features Harris interest points + SSD, ZNCC, SIFT Scale & affine invariant interest point detectors Evaluation and comparison.
Feature Based Image Mosaicing Satya Prakash Mallick.
Local invariant features Cordelia Schmid INRIA, Grenoble.
Notes on the Harris Detector from Rick Szeliski’s lecture notes, CSE576, Spring 05.
Distinctive Image Features from Scale-Invariant Keypoints Ronnie Bajwa Sameer Pawar * * Adapted from slides found online by Michael Kowalski, Lehigh University.
Instructor: Mircea Nicolescu Lecture 13 CS 485 / 685 Computer Vision.
Summary of Previous Lecture A homography transforms one 3d plane to another 3d plane, under perspective projections. Those planes can be camera imaging.
Complex Networks for Representation and Characterization of Images For CS790g Project Bingdong Li 9/23/2009.
Automatic Image Alignment (feature-based) : Computational Photography Alexei Efros, CMU, Fall 2006 with a lot of slides stolen from Steve Seitz and.
Correspondence search using Delaunay triangulation Rishu Gupta
Multiple Object Class Detection with a Generative Model K. Mikolajczyk, B. Leibe and B. Schiele Carolina Galleguillos.
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