1 / 14

Geometric Blur Descriptors for Robust Point Correspondence in Computer Vision

This project investigates the development of geometric blur descriptors for point correspondence—a vital aspect of various vision applications such as image alignment, 3D scene reconstruction, and object recognition. We introduce a new technique leveraging a spatially varying kernel that enhances robustness against affine transforms. Initial results indicate mixed performance compared to traditional methods, such as SSD. Future work will explore threshold adjustments and test alternative descriptors like SIFT to improve descriptor efficacy. This research aims to advance techniques in computational photography.

tarak
Télécharger la présentation

Geometric Blur Descriptors for Robust Point Correspondence in Computer Vision

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. Geometric Blur Descriptors for Point Correspondence Nisarg Vyas Computational Photography (15862) Final Project, Carnegie Mellon University

  2. Motivation • Point Correspondences are used in many vision applications • Image Alignment • 3D reconstruction of scene from multiple views • Object Recognition • Vehicle path Navigation • Structure from Motion

  3. Point Correspondence Basic Approaches: SSD, NCC •  ,Do not work well under affine transfoms

  4. Blurred Descriptors • MOPS • Geometric Blur

  5. Geometric Blur: Introduction • A “Spatially varying” Kernel which smoothes Instead of Kx(y) = Gσ(y), Kx(y) = Gα|x|(y)

  6. Comparison: Geometric Blur & Gaussian Blur

  7. Geometric Blur “Descriptor” • Take signed Gradient of input image in both directions, we will now be with 4 channels

  8. Geometric Blur “Descriptor” Take a feature point, calculate Blur Descriptor for all 4 gradient channels, Subsampled in concentric circles

  9. Results

  10. Results

  11. Results

  12. Status so far & Plans for final submission • Done implementing Geometric Blur Descriptor • Results are not as good as expected, sometimes simple SSD does even better !! • Have to try changing the thresholds which varies the sigma • Trying Other interesting descriptors (SIFT,C1), If time permits

  13. References [1] Geometric Blur for Template Matching A.C. Berg and J. Malik, CVPR, 2001 [2] Shape Matching and Object Recognition using Low-distortion Correspondences, A.C. Berg, T.L. Berg and J. Malik, CVPR, 2005 [3] Comparing Visual Features for Morphing Based Recognition, J.J. Wu, MIT CSAIL Technical report, 2005 (TR-2005-035)

  14. Questions and Suggestions?

More Related