尺度不变特征变换(SIFT算法)Matlab程序入门教程_sift教程matlab - CSDN文库

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Exercise 1: The Scale-Invariant Feature

Transform

August 9, 2010

1 Introduction

The Scale-Invariant Feature Transform (SIFT) is a method for the detection and de-

scription of interest points developed by David Lowe [Lowe, 2004]. The method has

been successfully applied in many computer- and machine-vision applications, such as

object recognition, image retrieval, and robotic localization and mapping.

In this exercise, we will use SIFT for the representation of the environment of a robot.

We will use the detector to ﬁnd interest points in the environment, and we will describe

those interest points with the SIFT descriptor, so that we will be able to store and

recognize the points. The main goal of this exercise is to play around with the different

parameters of the detector and descriptor to get a better grasp on the method.

2 Exercises

The detector is based on the ﬁltering of the image with Difference of Gaussians:

D(σ) = G(σ) − G(kσ), a so-called Mexican-hat function. Lets visualize the Differ-

ence of Gaussian:

2.1 DIfference-of-Gaussian

• Start matlab

• Change directory to exercise_1.

• Create a Gaussian ﬁlter kernel of size 101 × 101 and σ

1

= 20.0 with

G1 = fspecial(’gaussian’, [101 101], 20.0);

Mind that the size of the kernel is much bigger than is actually used in the SIFT

method, but this is for displaying purposes.

1

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