图像配准方法：一项综合调查

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"图像配准方法：一项调查" 这篇由Barbara Zitova和Jan Flusser撰写的综述文章全面介绍了图像配准领域的最新进展和经典方法。图像配准是将不同时间、不同视角或不同传感器拍摄的同一场景的图像进行叠加对齐的过程。这一技术的关键在于几何上校准两幅图像（参考图像与感测图像）。文章首先对所审查的方法进行了分类，主要分为基于区域的方法和基于特征的方法。基于区域的方法依赖于图像像素级别的相似性度量，而基于特征的方法则侧重于识别和匹配图像中的显著结构或模式。在图像配准的四个基本步骤中，文章详细阐述了： 1. 特征检测：这是寻找图像中关键点或结构的过程，如边缘、角点或兴趣点。 2. 特征匹配：在这一步骤，算法试图在不同图像之间找到对应点，以建立它们之间的关系。 3. 映射函数设计：确定如何通过变换将一个图像的坐标映射到另一个图像的坐标，这通常涉及多项式、仿射、透视或其他复杂的几何变换模型。 4. 图像变换和重采样：应用设计的映射函数来实际移动像素，使图像对齐，并可能需要对新坐标下的像素进行重采样以保持图像质量。文章中提到了每种方法的主要贡献、优点和缺点。例如，基于区域的方法可能在全局一致性方面表现良好，但对噪声和光照变化敏感；而基于特征的方法可能更稳定，但在特征稀疏或复杂背景中可能遇到挑战。此外，作者还讨论了图像配准中的问题和未来研究方向，如鲁棒性、效率、自动化程度以及处理多模态图像的配准等挑战。随着计算机视觉和机器学习的发展，图像配准已成为许多应用的基础，如医学影像分析、遥感、视频处理和3D重建等。这篇文章为研究图像配准的学者提供了丰富的信息，帮助他们理解当前的技术趋势，并为未来的研究提供启示。对于这个领域的专业人士来说，它无疑是一个宝贵的参考资料。

degradation present in this type of data). They applied

elastic contour extraction. The comparison of different

operators for the feature edge detection and the ridge

detection in multimodal medical images is presented by

Maintz et al. [121,122].

Point features. The point features group consists of

methods working with line intersections [175,198], road

crossings [79,161], centroids of water regions, oil and gas

pads [190], high variance points [45], local curvature

discontinuities detected using the Gabor wavelets [125,

219], inﬂection points of curves [3,11], local extrema of

wavelet transform [58,90], the most distinctive points with

respect to a speciﬁed measure of similarity [115], and

corners [20,92,204].

The core algorithms of feature detectors in most cases

follow the deﬁnitions of the ‘point’ as line intersection,

centroid of closed-boundary region or local modulus

maxima of the wavelet transform. Corners form speciﬁc

class of features, because ‘to-be-a-corner’ property is hard

to deﬁne mathematically (intuitively, corners are under-

stood as points of high curvature on the region boundaries).

Much effort has been spent in developing precise, robust,

and fast method for corner detection. A survey of corner

detectors can be found in Refs. [155,172,220] and the most

up-to-date and exhaustive in Ref. [156]. The latter also

analyzes localization properties of the detectors. Corners are

widely used as CPs mainly because of their invariance to

imaging geometry and because they are well perceived by a

human observer.

Kitchen and Rosenfeld [101] proposed to exploit the

second-order partial derivatives of the image function for

corner detection. Dreschler and Nagel [43] searched for the

local extrema of the Gaussian curvature. However, corner

detectors based on the second-order derivatives of the image

function are sensitive to noise. Thus Fo

rstner [62] developed

a more robust, although time consuming, corner detector,

which is based on the ﬁrst-order derivatives only. The

reputable Harris detector (also referred to as the Plessey

detector) [135] is in fact its inverse. The application of the

rstner detector is described in Ref. [107], where it is used

for the registration of dental implants images.

More intuitive approach was chosen by Smith and Brady

[173] in their robust SUSAN method. As the criterion they

used the size of the area of the same color as that of the

central pixel. Trajkovic and Hedley [192] designed their

operator using the idea that the change of the image

intensity at the corners should be high in all directions.

Recently, Zitova

et al. [224] proposed a parametric corner

detector, which does not employ any derivatives and which

was designed to handle blurred and noisy data. Rohr et al.

designed corner detectors, even for 3D data, allowing user

interaction [158].

The number of detected points can be very high, which

increases the computational time necessary for the regis-

tration. Several authors proposed methods for an efﬁcient

selection of a subset of points (better than random) which

does not degrade the quality of the resulting registration.

Goshtasby [71] used only points belonging to a convex hull

of the whole set. Lavine [104] proposed to use points

forming the minimum spanning trees of sets. Ehlers [45]

merged points into ‘clumps’—large dense clusters.

3.3. Summary

To summarize, the use of feature-based methods is

recommended if the images contain enough distinctive and

easily detectable objects. This is usually the case of

applications in remote sensing and computer vision. The

typical images contain a lot of details (towns, rivers, roads,

forests, room facilities, etc). On the other hand, medical

images are not so rich in such details and thus area-based

methods are usually employed here. Sometimes, the lack of

distinctive objects in medical images is solved by the

interactive selection done by an expert or by introducing

extrinsic features, rigidly positioned with respect to the

patient (skin markers, screw markers, dental adapters, etc.)

[123]. The applicability of area-based and feature-based

methods for images with various contrast and sharpness is

analyzed in Ref. [151]. Recently, registration methods using

simultaneously both area-based and feature-based

approaches have started to appear [85].

4. Feature matching

The detected features in the reference and sensed images

can be matched by means of the image intensity values in

their close neighborhoods, the feature spatial distribution, or

the feature symbolic description. Some methods, while

looking for the feature correspondence, simultaneously

estimate the parameters of mapping functions and thus

merge the second and third registration steps.

In the following paragraphs, the two major categories

(area-based and feature-based methods, respectively), are

retained and further classiﬁed into subcategories according

to the basic ideas of the matching methods.

4.1. Area-based methods

Area-based methods, sometimes called correlation-like

methods or template matching [59] merge the feature

detection step with the matching part. These methods deal

with the images without attempting to detect salient objects.

Windows of predeﬁned size or even entire images are used

for the correspondence estimation during the second

registration step, [4,12,145].

The limitations of the area-based methods originate in

their basic idea. Firstly, the rectangular window, which is

most often used, suits the registration of images which

locally differ only by a translation. If images are deformed

by more complex transformations, this type of the window

is not able to cover the same parts of the scene in

B. Zitova

, J. Flusser / Image and Vision Computing 21 (2003) 977–1000 981

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