Affine Differential Invariants for Invariant Feature Point Detection

03/05/2018
by   Stanley L. Tuznik, et al.
0

Image feature points are detected as pixels which locally maximize a detector function, two commonly used examples of which are the (Euclidean) image gradient and the Harris-Stephens corner detector. A major limitation of these feature detectors are that they are only Euclidean-invariant. In this work we demonstrate the application of a 2D affine-invariant image feature point detector based on differential invariants as derived through the equivariant method of moving frames. The fundamental equi-affine differential invariants for 3D image volumes are also computed.

READ FULL TEXT

page 2

page 7

page 8

page 9

page 15

page 16

page 17

page 18

research
11/13/2019

Image Differential Invariants

Inspired by the methods of systematic derivation of image moment invaria...
research
07/23/2020

Silhouette Vectorization by Affine Scale-space

Silhouettes or 2D planar shapes are extremely important in human communi...
research
12/29/2010

Affine-invariant geodesic geometry of deformable 3D shapes

Natural objects can be subject to various transformations yet still pres...
research
08/26/1999

A Differential Invariant for Zooming

This paper presents an invariant under scaling and linear brightness cha...
research
04/20/2023

Feature point detection in HDR images based on coefficient of variation

Feature point (FP) detection is a fundamental step of many computer visi...
research
04/08/2011

Gaussian Affine Feature Detector

A new method is proposed to get image features' geometric information. U...
research
08/07/2014

Low-rank SIFT: An Affine Invariant Feature for Place Recognition

In this paper, we present a novel affine-invariant feature based on SIFT...

Please sign up or login with your details

Forgot password? Click here to reset