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Proceedings ArticleDOI

Image Registration Using Composite Algorithms

TL;DR: Four image registration techniques namely geometric transformation, phase correlation, log polar transform and adaptive polar transform are analyzed and in depth analysis of all the techniques are given to observe which gives best mapping results with the two images, and also find parameters which gives relevant information which helps to medical experts.
Abstract: Image registration, a process of mapping two or more images having different alignment but sharing the same information, is important step in many applications. These applications require perceptible data from different images for comparison, integration or analysis. Image registration plays important role in medical imaging as most of the applications is related to clinical prognosis. Here, four image registration techniques namely geometric transformation, phase correlation, $\text{log}$ polar transform and adaptive polar transform are analyzed. Registration algorithms measure transformation to set correspondence between more than two images. The purpose of this proposal is to give in depth analysis of all the techniques and observe which gives best mapping results with the two images, and also find parameters which gives relevant information which helps to medical experts. Rotation and scaling factor are obtained by log polar transform. But because of non-uniform sampling, LPT is not proper approach for some applications. Therefore, APT is used which maintain the robustness to scale and rotation. For translation property, phase correlation help. However, limitations of all these techniques are still exit.
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Journal ArticleDOI
TL;DR: A review of recent as well as classic image registration methods to provide a comprehensive reference source for the researchers involved in image registration, regardless of particular application areas.

6,842 citations


"Image Registration Using Composite ..." refers background in this paper

  • ...INTRODUCTION Image registration is a process of aligning two or more images that share similar information but most taken from different geometric frame of reference point, at various time, or by various image sensing element [1]....

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Journal ArticleDOI
TL;DR: This paper organizes this material by establishing the relationship between the variations in the images and the type of registration techniques which can most appropriately be applied, and establishing a framework for understanding the merits and relationships between the wide variety of existing techniques.
Abstract: Registration is a fundamental task in image processing used to match two or more pictures taken, for example, at different times, from different sensors, or from different viewpoints. Virtually all large systems which evaluate images require the registration of images, or a closely related operation, as an intermediate step. Specific examples of systems where image registration is a significant component include matching a target with a real-time image of a scene for target recognition, monitoring global land usage using satellite images, matching stereo images to recover shape for autonomous navigation, and aligning images from different medical modalities for diagnosis.Over the years, a broad range of techniques has been developed for various types of data and problems. These techniques have been independently studied for several different applications, resulting in a large body of research. This paper organizes this material by establishing the relationship between the variations in the images and the type of registration techniques which can most appropriately be applied. Three major types of variations are distinguished. The first type are the variations due to the differences in acquisition which cause the images to be misaligned. To register images, a spatial transformation is found which will remove these variations. The class of transformations which must be searched to find the optimal transformation is determined by knowledge about the variations of this type. The transformation class in turn influences the general technique that should be taken. The second type of variations are those which are also due to differences in acquisition, but cannot be modeled easily such as lighting and atmospheric conditions. This type usually effects intensity values, but they may also be spatial, such as perspective distortions. The third type of variations are differences in the images that are of interest such as object movements, growths, or other scene changes. Variations of the second and third type are not directly removed by registration, but they make registration more difficult since an exact match is no longer possible. In particular, it is critical that variations of the third type are not removed. Knowledge about the characteristics of each type of variation effect the choice of feature space, similarity measure, search space, and search strategy which will make up the final technique. All registration techniques can be viewed as different combinations of these choices. This framework is useful for understanding the merits and relationships between the wide variety of existing techniques and for assisting in the selection of the most suitable technique for a specific problem.

4,769 citations

Journal ArticleDOI
TL;DR: This correspondence discusses an extension of the well-known phase correlation technique to cover translation, rotation, and scaling, which shows excellent robustness against random noise.
Abstract: This correspondence discusses an extension of the well-known phase correlation technique to cover translation, rotation, and scaling. Fourier scaling properties and Fourier rotational properties are used to find scale and rotational movement. The phase correlation technique determines the translational movement. This method shows excellent robustness against random noise.

1,939 citations


"Image Registration Using Composite ..." refers methods in this paper

  • ...The Fourier Mellin [7-8] method is proposed as an advanced area-based method by combining the phase correlation method with the log-polar transform (LPT)....

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Journal ArticleDOI
TL;DR: A new method to match a 2D image to a translated, rotated and scaled reference image using symmetric phase-only matched filtering to the FMI descriptors, which guarantees high discriminating power and excellent robustness in the presence of noise.
Abstract: Presents a new method to match a 2D image to a translated, rotated and scaled reference image. The approach consists of two steps: the calculation of a Fourier-Mellin invariant (FMI) descriptor for each image to be matched, and the matching of the FMI descriptors. The FMI descriptor is translation invariant, and represents rotation and scaling as translations in parameter space. The matching of the FMI descriptors is achieved using symmetric phase-only matched filtering (SPOMF). The performance of the FMI-SPOMF algorithm is the same or similar to that of phase-only matched filtering when dealing with image translations. The significant advantage of the new technique is its capability to match rotated and scaled images accurately and efficiently. The innovation is the application of SPOMF to the FMI descriptors, which guarantees high discriminating power and excellent robustness in the presence of noise. This paper describes the principle of the new method and its discrete implementation for either image detection problems or image registration problems. Practical results are presented for various applications in medical imaging, remote sensing, fingerprint recognition and multiobject identification. >

685 citations


"Image Registration Using Composite ..." refers methods in this paper

  • ...The Fourier Mellin [7-8] method is proposed as an advanced area-based method by combining the phase correlation method with the log-polar transform (LPT)....

    [...]

Journal ArticleDOI
TL;DR: An extension to the basic concept of correlation detection as a means of image registration is developed that utilizes the spatial correlation within each image and greatly improves the detectability of image misregistration.
Abstract: An extension to the basic concept of correlation detection as a means of image registration is developed. The technique involves linear spatial preprocessing of the inages to be registered prior to the application of a correlation measure. This preprocessing operation utilizes the spatial correlation within each image and greatly improves the detectability of image misregistration. An analysis of the computational aspects of the algorithm is given. Also, results of a computer simulation to evaluate the technique are given.

366 citations


"Image Registration Using Composite ..." refers methods in this paper

  • ...In area based approach, Estimation is done using correlation which ultimately depends on selective area [3]....

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