1 / 20

Comparing NARF and SIFT Key Point Extraction Algorithms

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.

wgilliard
Download Presentation

Comparing NARF and SIFT Key Point Extraction Algorithms

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Chris Kaffine Second Annual MIT PRIMES Conference, May 20th, 2012 Comparing NARF and SIFT Key Point Extraction Algorithms

  2. Range Sensors • Purpose: collect distance information • Advantage over cameras: 3D • Methods: • Stereo Imagery • LiDAR • Structured Light

  3. 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

  4. Correspondences • Goal: Find points in two images which are equivalent • With matched points, differences between images can be calculated

  5. 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

  6. 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

  7. 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”

  8. 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

  9. 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

  10. Results- Measures of Success • Measure 1: Smoother, NARF exceeds SIFT in parts • Overall, similar trends, though distinct metrics

  11. 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

  12. 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

  13. 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

  14. Acknowledgements • MIT PRIMES • Professor Seth Teller • Jon Brookshire – Mentor

More Related