Comparing NARF and SIFT Key Point Extraction Algorithms

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Chris Kaffine Second Annual MIT PRIMES Conference, May 20 th , 2012. Comparing NARF and SIFT Key Point Extraction Algorithms. Range Sensors. Purpose: collect distance information Advantage over cameras: 3D Methods: Stereo Imagery LiDAR Structured Light. Representing Range Data.

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Comparing NARF and SIFT Key Point Extraction Algorithms

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Presentation Transcript
Chris Kaffine

Second Annual MIT PRIMES Conference, May 20th, 2012

Comparing NARF and SIFT Key Point Extraction Algorithms
Range Sensors
• Purpose: collect distance information
• 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
• 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