Classification of Image Registration Techniques and Algorithms in Digital Image Processing – A Research Survey

G, Sindhu Madhuri

doi:10.14445/22312803/ijctt-v15p118

Cited by 14 publications

(3 citation statements)

References 8 publications

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“…The new image registration technique that emerged with Radon and Slant transformation, and by considering Cameraman, Lena and VegCrop image sets with Rotation, Translation and Scaling as geometrical transformations [4,5,6,7]. The performance of new image registration technique is evaluated with RMSE, PSNR and Entropy measures, and tested for a specific application.…”

Section: Development Of New Image Registration Techniquementioning

confidence: 99%

Development of New Image Registration Techniques – A Research Framework

Madhuri

2018

IJARCSSE

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Image is a basic and fundamental data source for the digital image processing. This image data source is required to be processed into information or intelligence and further to knowledge levels where it is required to understand and migrate into knowledge economy systems. Image registration is one of such key and most important process already identified in the digital image processing domain. Image registration is a process of bringing the reference image and sensed image into a common co-ordinate system, and application of complex transformation techniques for necessary comparison of reference with sensed images obtained from different -views, times, spaces, etc., in order to extract the valuable information and intelligence embedded in them. Due to the complexity of overall image registration process, it is difficult to suggest a single transformation technique even for a specific application. In addition, it is highly impossible to suggest one single transformation technique for comparison of various sensed images with a reference image during the image registration process. This research gap calls for the development of new image registration techniques for the application of more than one transformation technique during the image registration process for the necessary comparisons with reference image & sensed images, those are obtained from the available heterogeneous sources or sensors, based on the requirement. In addition, it is a basic need to attempt for the measurement of effectiveness of the image registration process also. Therefore, a research framework is developed for image registration process and attempted for the measurement of its effectiveness also. This new research area is a novel idea, and is expected to emerge as a provision for the knowledge computations with creative thinking through the embedded intelligence extraction during the complex image registration process.

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Section: Development Of New Image Registration Techniquementioning

confidence: 99%

Development of New Image Registration Techniques – A Research Framework

Madhuri

2018

IJARCSSE

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“…In this section, the theoretical ap images that differ by rotation, tran other based on the FFT properties and scaling), is explained. Image image processing operation, given more images and produce a more i originals [22]. In this work, the FFT of corneal sub-basal images so ro detected using a phase correlation t by applying the transformation m spectrum to one of the input ima image with the second image translation information using the p in the log-polar space.…”

Section: The Proposed Mementioning

confidence: 99%

An Automatic Corneal Subbasal Nerve Registration System Using FFT and Phase Correlation Techniques for an Accurate DPN Diagnosis

Al-Fahdawi

Qahwaji

Al-Waisy

et al. 2015

2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable,

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Confocal microscopy is employed as a fast and non-invasive way to capture a sequence of images from different layers and membranes of the cornea. The captured images are used to extract useful and helpful clinical information for early diagnosis of corneal diseases such as, Diabetic Peripheral Neuropathy (DPN). In this paper, an automatic corneal subbasal nerve registration system is proposed. The main aim of the proposed system is to produce a new informative corneal image that contains structural and functional information. In addition a colour coded corneal image map is produced by overlaying a sequence of Cornea Confocal Microscopy (CCM) images that differ in their displacement, illumination, scaling, and rotation to each other. An automatic image registration method is proposed based on combining the advantages of Fast Fourier Transform (FFT) and phase correlation techniques. The proposed registration algorithm searches for the best common features between a number of sequenced CCM images in the frequency domain to produce the formative image map. In this generated image map, each colour represents the severity level of a specific clinical feature that can be used to give ophthalmologists a clear and precise representation of the extracted clinical features from each nerve in the image map. Moreover, successful implementation of the proposed system and the availability of the required datasets opens the door for other interesting ideas; for instance, it can be used to give ophthalmologists a summarized and objective description about a diabetic patient's health status using a sequence of CCM images that have been captured from different imaging devices and/or at different times.

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“…With the developments and advances in technology, photogrammetric, and remote sensing applications are fast gaining global acceptance in the geographical observation and monitoring of the earth surface (Tsai & Lin, 2017;Kleissl, 2013;Olaleye et al, 2015;Rittavee et al, 2009;Sindhu, 2014;Ting & Herng, 2016). These applications provide pictorial representations of an area and they give an overview of a geographical scene of interest in the form of images which afford the observer or image analyst the opportunity to acquire accurate spatial or geometric information about the sphere of interest.…”

Section: Introductionmentioning

confidence: 99%

Accuracy Assessment of the Effect of Different Feature Descriptors on the Automatic Co-Registration of Overlapping Images

Ajayi,

Nwadialor

2024

Geodesy and Cartography

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This research seeks to assess the effect of different selected feature descriptors on the accuracy of an automatic image registration scheme. Three different feature descriptors were selected based on their peculiar characteristics, and implemented in the process of developing the image registration scheme. These feature descriptors (Modified Harris and Stephens corner detector (MHCD), the Scale Invariant Feature Transform (SIFT) and the Speeded Up Robust Feature (SURF)) were used to automatically extract the conjugate points common to the overlapping image pairs used for the registration. Random Sampling Consensus (RANSAC) algorithm was used to exclude outliers and to fit the matched correspondences, Sum of Absolute Differences (SAD) which is a correlation-based feature matching metric was used for the feature match, while projective transformation function was used for the computation of the transformation matrix (T). The obtained overall result proved that the SURF algorithm outperforms the other two feature descriptors with an accuracy measure of -0.0009 pixels, while SIFT with a cumulative signed distance of 0.0328 pixels also proved to be more accurate than MHCD with a cumulative signed distance of 0.0457 pixels. The findings affirmed the importance of choosing the right feature descriptor in the overall accuracy of an automatic image registration scheme.

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Classification of Image Registration Techniques and Algorithms in Digital Image Processing – A Research Survey

Cited by 14 publications

References 8 publications

Development of New Image Registration Techniques – A Research Framework

Development of New Image Registration Techniques – A Research Framework

An Automatic Corneal Subbasal Nerve Registration System Using FFT and Phase Correlation Techniques for an Accurate DPN Diagnosis

Accuracy Assessment of the Effect of Different Feature Descriptors on the Automatic Co-Registration of Overlapping Images

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