admin管理员组

文章数量:1622628

Detection of Scale-space Extrema

Detection location that are invariant to scale: search for stable features across all possible scales.

L(x,y,σ) = G(x,y,σ) ∗ I(x,y): L function of scale space of image, G a variable scale Gaussian, I input image


Difference of Gaussian: s: interval in DOG, s+2 layers need for extrema finding process, s+3 layers need for Gaussian, k = 2^(1/s)

Find local extrema -> Determine the accuracy location by interpolation -> Eliminate low contrast points -> Eliminate points on edges (using Hessian Matrix)

Local Image Descriptor

rotation invariant, invariant to affine changes in illumination

Descriptor vector size: r^n^2  r: number of orientation histogram bins, n: size of grids

本文标签： distinctiveImagecomputerVisionInvariant

版权声明：本文标题：[Computer Vision 4] Distinctive Image Features from Scale-Invariant Keypoints 内容由热心网友自发贡献，该文观点仅代表作者本人， 转载请联系作者并注明出处：https://www.elefans.com/xitong/1728870547a1177227.html， 本站仅提供信息存储空间服务，不拥有所有权，不承担相关法律责任。如发现本站有涉嫌抄袭侵权/违法违规的内容，一经查实，本站将立刻删除。