SIFT算法理解：基于局部尺度不变特征的对象识别

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"sift由来的引用的文件，包含SIFT（尺度不变特征转换）算法的详细描述，用于理解和实现对象识别" SIFT（尺度不变特征转换）算法是计算机视觉领域中的一个关键技术，由David G. Lowe在1999年的论文《Object Recognition from Local Scale-Invariant Features》中提出。该算法的核心目标是通过提取图像中的局部特征来实现对象的识别，这些特征对图像的缩放、平移、旋转以及部分光照变化和仿射或3D投影具有不变性。 1. 尺度空间稳定点检测： SIFT算法首先通过构建尺度空间来寻找图像中的兴趣点，这些兴趣点在不同尺度下保持稳定。通过多尺度高斯金字塔，SIFT算法能够检测到图像中各种大小的特征，从而克服了尺度变化的影响。 2. 关键点定位与描述符生成：在找到稳定点后，SIFT算法会对每个关键点进行精确定位，并计算其主方向。接着，通过在多个方向上采样模糊图像梯度，生成具有方向信息的关键点描述符。这些描述符是旋转不变的，能有效描述关键点周围的局部特征。 3. 特征匹配：为了进行对象识别，SIFT算法采用最近邻索引方法将不同图像中的关键点描述符进行匹配。这种匹配方法能够在一定程度上容忍图像的局部变形和噪声。 4. 几何验证与残差最小化：匹配后的候选对象需要经过几何验证，这通常通过求解未知模型参数的低残差最小二乘问题来实现。通过比较不同匹配组合的残差，可以确定最佳的匹配对，从而提高识别的准确性。 5. 实验结果与应用：实验结果显示，SIFT算法在面对光照变化、部分遮挡、图像噪声等挑战时，仍能实现稳健的对象识别。因此，SIFT被广泛应用于图像匹配、物体识别、3D重建等领域，成为计算机视觉研究和应用中的基础工具。 SIFT算法通过其独特的特征提取和匹配策略，有效地解决了图像处理中的尺度、旋转、光照等问题，为对象识别提供了可靠的基础。这篇论文不仅阐述了算法原理，还为实际应用提供了指导，对于理解和实现SIFT算法具有极高的价值。

Object Recognition from Local Scale-Invariant Features

David G. Lowe

Computer Science Department

University of British Columbia

Vancouver, B.C., V6T 1Z4, Canada

lowe@cs.ubc.ca

Abstract

An object recognitionsystem has been developed that uses a

newclass of localimagefeatures. The featuresare invariant

to image scaling, translation,and rotation, and partiallyin-

variant to illuminationchanges and afﬁne or 3D projection.

These features share similar properties with neurons in in-

ferior temporal cortex that are used for object recognition

in primate vision. Features are efﬁciently detected through

a staged ﬁltering approach that identiﬁes stable points in

scale space. Image keys are created that allow for local ge-

ometric deformations by representing blurred image gradi-

ents in multiple orientation planes and at multiple scales.

The keys are used as input to a nearest-neighbor indexing

method that identiﬁescandidateobject matches. Final veri-

ﬁcation of each match is achieved by ﬁnding a low-residual

least-squares solution for the unknown model parameters.

Experimental results show that robust object recognition

can be achieved in cluttered partially-occluded images with

a computation time of under 2 seconds.

1. Introduction

Object recognition in cluttered real-world scenes requires

local image features that are unaffected by nearby clutter or

partial occlusion. The features must be at least partially in-

variant to illumination, 3D projective transforms, and com-

mon object variations. On the other hand, the features must

also be sufﬁciently distinctive to identify speciﬁc objects

among many alternatives. The difﬁculty of the objectrecog-

nition problem is due in large part to the lack of success in

ﬁnding such image features. However, recent research on

the use of dense local features (e.g., Schmid & Mohr [19])

has shown that efﬁcient recognition can often be achieved

by using local image descriptors sampled at a large number

of repeatable locations.

This paper presents a new method for image feature gen-

eration called the Scale Invariant Feature Transform (SIFT).

This approach transforms an image into a large collection

of local feature vectors, each of which is invariant to image

translation, scaling, and rotation, and partially invariant to

illumination changes and afﬁne or 3D projection. Previous

approaches to local feature generation lacked invariance to

scale and were more sensitive to projective distortion and

illumination change. The SIFT features share a number of

properties in common with the responses of neurons in infe-

rior temporal (IT) cortex in primate vision. This paper also

describes improved approaches to indexing and model ver-

iﬁcation.

The scale-invariant features are efﬁciently identiﬁed by

using a staged ﬁltering approach. The ﬁrst stage identiﬁes

key locations in scale space by looking for locations that

are maxima or minima of a difference-of-Gaussian function.

Each point is used to generate a feature vector that describes

the local image region sampled relative to its scale-space co-

ordinate frame. The features achieve partial invariance to

local variations, such as afﬁne or 3D projections, by blur-

ring image gradient locations. This approach is based on a

model of the behavior of complex cells in the cerebral cor-

tex of mammalian vision. The resulting feature vectors are

called SIFT keys. In the current implementation, each im-

age generates on the order of 1000SIFT keys, a process that

requires less than 1 second of computation time.

The SIFT keys derived from an image are used in a

nearest-neighbour approach to indexing to identify candi-

date object models. Collections of keys that agree on a po-

tentialmodel pose are ﬁrst identiﬁedthrougha Hough trans-

formhash table, and thenthrougha least-squares ﬁt to a ﬁnal

estimate of model parameters. When at least 3 keys agree

on the model parameters with low residual, there is strong

evidence for the presence of the object. Since there may be

dozens of SIFT keys in the image of a typical object, it is

possible to have substantial levels of occlusion in the image

and yet retain high levels of reliability.

The current object models are represented as 2D loca-

tions of SIFT keys that can undergo afﬁne projection. Suf-

ﬁcient variation in feature location is allowed to recognize

perspective projection of planar shapes at up to a 60 degree

rotationaway from the camera or to allowup to a 20 degree

rotation of a 3D object.

Authorized licensed use limited to: CHONGQING UNIV OF POST AND TELECOM. Downloaded on November 13, 2008 at 21:08 from IEEE Xplore. Restrictions apply.

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