Transcript 7-2-Kaffine
Comparing NARF and SIFT Key
Point Extraction Algorithms
Chris Kaffine
Second Annual MIT PRIMES Conference, May
20th, 2012
Range Sensors
Purpose: collect distance information
Advantage over cameras: 3D
Methods:
Stereo Imagery
LiDAR
Structured Light
Representing Range Data
Point Clouds:
3D-coordinates
Geometrically
understandable
Range Images:
2D-image with pixel values representing depth
Similar to
sensor functioning
Allows border
extraction
Correspondences
Goal: Find points in two images which are
equivalent
With matched points, differences between
images can be calculated
Key Points and Descriptors
Find correspondences in two steps: find key
points, calculate descriptors
Key Points- Distinguishable, stable locations
in a scene
Descriptors- Numerical
description of a point
and its underlying
surface
Points with similar
descriptors are correspondences
NARF
Normally Aligned Radial Features
Uses range images
Uses borders and change in distance (pixel)
values to identify key points
Key points are invariant to scale, susceptible
to camera orientation
Support Size:
indicates how
detailed the search
should be
SIFT
Scale Invariant Feature Transform
Uses point clouds
Finds key points that are invariant to scale
Utilizes full, 3D geometry
Scale Size: indicates how close to “zoom in”
Evaluating the Algorithms
Use data with known sensor location
Within chronologically adjacent frames,
search for nearby key points
Points within a certain distance are
considered true matches
Count number of frames each point lasts for
Repeat, using different algorithms with
different parameter values and different
distance thresholds
Evaluating the Algorithms
Metrics for evaluation:
Number of key points identified
Persistence/Stability of key points
Density of key points, with relation to
distance threshold
Due to limitations in persistence algorithm,
two persistence metrics were used:
Measure 1:Average persistence of all key
points
Measure 2: Number of key points with
persistence greater than 1
Results- Measures of Success
Measure 1: Smoother, NARF exceeds SIFT in parts
Overall, similar trends, though distinct metrics
Results- Measures of Success
Measure 1: Smoother, NARF exceeds SIFT in parts
Overall, similar trends so overestimation most likely
did not have a strong effect
Results- Measures of Success
At low parameter values, SIFT key point numbers and
density rise dramatically, NARF values rise steadily
Indicates that as parameter values decrease,
superfluous key points are detected
Results
Best parameter values for each algorithm displayed
Metric used: #key points * persistence / density
SIFT almost always superior
Scale size .07 better in general, 0.1 possibly better in
some cases
Acknowledgements
MIT PRIMES
Professor Seth Teller
Jon Brookshire – Mentor