Geometric Blur Descriptors for Point Correspondence

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Presentation transcript:

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

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

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

Blurred Descriptors MOPS Geometric Blur

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

Comparison: Geometric Blur & Gaussian Blur

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

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

Results

Results

Results

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

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)

Questions and Suggestions?