feature tracking class 5
DESCRIPTION
Feature tracking Class 5. Read Section 4.1 of course notes http://www.cs.unc.edu/~marc/tutorial/node49.html Read Shi and Tomasi’s paper on good features to track http://www.unc.edu/courses/2004fall/comp/290/089/papers/shi-tomasi-good-features-cvpr1994.pdf Read Lowe’s paper on SIFT features - PowerPoint PPT PresentationTRANSCRIPT
Feature trackingClass 5
Read Section 4.1 of course noteshttp://www.cs.unc.edu/~marc/tutorial/node49.html
Read Shi and Tomasi’s paper on good features to track
http://www.unc.edu/courses/2004fall/comp/290/089/papers/shi-tomasi-good-features-cvpr1994.pdf
Read Lowe’s paper on SIFT featureshttp://www.unc.edu/courses/2004fall/comp/290/089/papers/Lowe_ijcv03.pdf
Don’t forget: Assignment 1(due by next Tuesday before class)
• Find a camera• Calibration approach 1
• Build/use calibration grid (2 orthogonal planes)• Perform calibration using (a) DLT and (b) complete
gold standard algorithm (assume error only in images, model radial distortion, ok to click points by hand)
• Calibration approach 2• Build/use planar calibration pattern• Use Bouguet’s matlab calibration toolbox (≈Zhang’s
approach)http://www.vision.caltech.edu/bouguetj/calib_doc/
(or implement it yourself for extra points)• Compare results of approach 1(a),1(b) and 2• Make short report of findings and be ready to
discuss in class
Single view metrology
• Allows to relate height of point to height of camera
Single view metrology
• Allows to transfer point from one plane to another
Feature trackingClass 5
Read Section 4.1 of course noteshttp://www.cs.unc.edu/~marc/tutorial/node49.html
Read Shi and Tomasi’s paper on good features to track
http://www.unc.edu/courses/2004fall/comp/290/089/papers/shi-tomasi-good-features-cvpr1994.pdf
Read Lowe’s paper on SIFT featureshttp://www.unc.edu/courses/2004fall/comp/290/089/papers/Lowe_ijcv03.pdf
Feature matching vs. tracking
Extract features independently and then match by comparing descriptors
Extract features in first images and then try to find same feature back in next view
What is a good feature?
Image-to-image correspondences are key to passive triangulation-based 3D reconstruction
Compare intensities pixel-by-pixel
Comparing image regions
I(x,y) I´(x,y)
Sum of Square Differences
Dissimilarity measures
Compare intensities pixel-by-pixel
Comparing image regions
I(x,y) I´(x,y)
Zero-mean Normalized Cross Correlation
Similarity measures
Feature points
• Required properties:• Well-defined
(i.e. neigboring points should all be different)
• Stable across views(i.e. same 3D point should be extracted as feature for neighboring viewpoints)
Feature point extraction
homogeneous
edge
corner
Find points that differ as much as possible from all neighboring points
Feature point extraction
• Approximate SSD for small displacement Δ• Image difference, square difference for
pixel• SSD for window
Feature point extraction
homogeneous
edge
corner
Find points for which the following is maximum
i.e. maximize smallest eigenvalue of M
Harris corner detector
• Only use local maxima, subpixel accuracy through second order surface fitting
• Select strongest features over whole image and over each tile (e.g. 1000/image, 2/tile)
• Use small local window:• Maximize „cornerness“:
Simple matching• for each corner in image 1 find the corner
in image 2 that is most similar (using SSD or NCC) and vice-versa
• Only compare geometrically compatible points
• Keep mutual best matches
What transformations does this work for?
Feature matching: example
0.96 -0.40
-0.16
-0.39
0.19
-0.05
0.75 -0.47
0.51 0.72
-0.18
-0.39
0.73 0.15 -0.75
-0.27
0.49 0.16 0.79 0.21
0.08 0.50 -0.45
0.28 0.99
1 5
24
3
1 5
24
3
What transformations does this work for? What level of transformation do we need?
Wide baseline matching • Requirement to cope with larger
variations between images• Translation, rotation, scaling• Foreshortening• Non-diffuse reflections• Illumination
geometric transformations
photometric changes
Wide-baseline matching example
(Tuytelaars and Van Gool BMVC 2000)
Lowe’s SIFT features
Recover features with position, orientation and scale
(Lowe, ICCV99)
Position
• Look for strong responses of DOG filter (Difference-Of-Gaussian)
• Only consider local maxima
Scale• Look for strong responses of DOG
filter (Difference-Of-Gaussian) over scale space
• Only consider local maxima in both position and scale
• Fit quadratic around maxima for subpixel
Orientation
• Create histogram of local gradient directions computed at selected scale
• Assign canonical orientation at peak of smoothed histogram
• Each key specifies stable 2D coordinates (x, y, scale, orientation) 0 2
Minimum contrast and “cornerness”
SIFT descriptor• Thresholded image gradients are sampled
over 16x16 array of locations in scale space• Create array of orientation histograms• 8 orientations x 4x4 histogram array = 128
dimensions
Matas et al.’s maximally stable regions
• Look for extremal regions
http://cmp.felk.cvut.cz/~matas/papers/matas-bmvc02.pdf
Mikolaczyk and Schmid LoG Features
Feature tracking
• Identify features and track them over video• Small difference between frames• potential large difference overall
• Standard approach: KLT (Kanade-Lukas-Tomasi)
Good features to track
• Use same window in feature selection as for tracking itself
• Compute motion assuming it is small
Affine is also possible, but a bit harder (6x6 in stead of 2x2)
Example
Example
Synthetic example
Good features to keep tracking
Perform affine alignment between first and last frameStop tracking features with too large errors
Live demo
• OpenCV (try it out!)LKdemo
Next class: triangulation and reconstruction
C1m1
L1
m2
L2
M
C2
Triangulation- calibration- correspondences