• Korean Journal of Remote Sensing
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• v.36 no.4
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• pp.545-555
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• 2020

This paper focuses on automatic image registration between EO (Electro-Optical) and IR (InfraRed) satellite images with different spectral properties using block-based approach and simple preprocessing technique to enhance the performance of feature matching. If unpreprocessed EO and IR images from Kompsat-3A satellite were applied to local feature matching algorithms(Scale Invariant Feature Transform, Speed-Up Robust Feature, etc.), image registration algorithm generally failed because of few detected feature points or mismatched pairs despite of many detected feature points. In this paper, we proposed a new image registration method which improved the performance of feature matching with block-based registration process on 9-divided image and pre-processing technique based on adaptive histogram equalization. The proposed method showed better performance than without our proposed technique on visual inspection and I-RMSE. This study can be used for automatic image registration between various images acquired from different sensors.

• Journal of the Korea Institute of Military Science and Technology
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• v.12 no.4
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• pp.524-531
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• 2009

In this paper, we propose an automatic image registration method for multi-sensor image fusion such as visible and infrared images. The registration is achieved by finding corresponding feature points in both input images. In general, the global statistical correlation is not guaranteed between multi-sensor images, which bring out difficulties on the image registration for multi-sensor images. To cope with this problem, mutual information is adopted to measure correspondence of features and to select faithful points. An update algorithm for projective transform is also proposed. Experimental results show that the proposed method provides robust and accurate registration results.

• Journal of Korean Dental Science
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• v.13 no.1
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• pp.28-34
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• 2020

Purpose: The purpose of this study was to investigate the influence of different implant computer software on the accuracy of image registration between radiographic and optical scan data. Materials and Methods: Cone-beam computed tomography and optical scan data of a partially edentulous jaw were collected and transferred to three different computer softwares: Blue Sky Plan (Blue Sky Bio), Implant Studio (3M Shape), and Geomagic DesignX (3D systems). In each software, the two image sets were aligned using a point-based automatic image registration algorithm. Image matching error was evaluated by measuring the linear discrepancies between the two images at the anterior and posterior area in the direction of the x-, y-, and z-axes. Kruskal-Wallis test and a post hoc Mann-Whitney U-test with Bonferroni correction were used for statistical analyses. The significance level was set at 0.05. Result: Overall discrepancy values ranged from 0.08 to 0.30 ㎛. The image registration accuracy among the software was significantly different in the x- and z-axes (P=0.009 and <0.001, respectively), but not different in the y-axis (P=0.064). Conclusion: The image registration accuracy performed by a point-based automatic image matching could be different depending on the computer software used.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2010.04a
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• pp.221-224
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• 2010

When the image registration methods which were generally used to the low medium resolution satellite images is applied to the high spatial resolution images, some matching errors or limitations might be occurred because of the local distortions in the images. This paper, therefore, proposed the automatic image-to-image registration of high resolution satellite images using local properties of control points to improve the registration result.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.28 no.3
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• pp.271-280
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• 2004

Assessment of image registration for Pressure Sensitive Paint (PSP) was performed. A 16 bit camera and LED lamp were used with Uni-FIB paint (ISSI). Because of model displacement and deformation at 'wind-on' condition, a large error of the intensity ratio was induced between 'wind-on' and' wind-off images. To correct the error, many kinds of image registrations were tested. At first, control points were marked on the model surface to find the coefficients of polynomial transform functions between the 'wind-off' 'wind-on' images. The 2nd-order polynomial function was sufficient for representing the model displacement and deformation. An automatic detection scheme was introduced to find the exact coordinates of the control points. The present automatic detection algorithm showed more accurate and user-friendly than the manual detection algorithm. Since the coordinates of transformed pixel were not integer, five interpolation methods were applied to get the exact pixel intensity after transforming the 'wind-on' image. Among these methods, the cubic convolution interpolation scheme gave the best result.

• The Journal of Korean Academy of Prosthodontics
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• v.59 no.3
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• pp.299-304
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• 2021

Purpose. The purpose of the present study was to investigate the effects of image matching experience of operators on the accuracy and working time of image registration between radiographic and optical scan images. Materials and methods. Computed tomography and optical scan of a dentate dental arch were obtained. Image matching between the computed tomography and the optical scan (IDC S1, Amann Girrbach, Koblah, Austria) was performed using the point-based automatic registration method in planning software programs (Implant Studio, 3Shape, Copenhagen, Denmark) using two different experience conditions on image registration: experienced group and inexperienced group (n = 15 per group, N = 30). The accuracy of image registration in each group was evaluated by measuring linear discrepancies between matched images, and working time was recorded. Independent t test was used to statistically analyze the result data (α = .05). Results. In the linear deviation, no statistically significant difference was found between the experienced and inexperienced groups. Meanwhile, the working time for image registration was significantly shorter in the experienced group than in the inexperienced group (P = .007). Conclusion. Difference in the image matching experience may not influence the accuracy of image registration of optical scan to computed tomography when the point-based automatic registration was used, but affect the working time for the image registration.

• Korean Journal of Remote Sensing
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• v.26 no.3
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• pp.325-333
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• 2010

Image registration involves overlapping images of an identical region and assigning the data into one coordinate system. Image registration has proved important in remote sensing, enabling registered satellite imagery to be used in various applications such as image fusion, change detection and the generation of digital maps. The image descriptor, which extracts matching points from each image, is necessary for automatic registration of remotely sensed data. Using contrast enhancement algorithms such as histogram equalization and image stretching, the normalized data are applied to the image descriptor. Drawing on the different spectral characteristics of high resolution satellite imagery based on sensor type and acquisition date, the applied normalization method can be used to change the results of matching interest point descriptors. In this paper, the matching points by scale invariant feature transformation (SIFT) are extracted using various contrast enhancement algorithms and injection of Gaussian noise. The results of the extracted matching points are compared with the number of correct matching points and matching rates for each point.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.3
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• pp.353-359
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• 2010

In this paper, we propose the automatic image-to-image registration of high resolution satellite images using local properties of tie points to improve the registration accuracy. A spatial distance between interest points of reference and sensed images extracted by Scale Invariant Feature Transform(SIFT) is additionally used to extract tie points. Coefficients of affine transform between images are extracted by invariant descriptor based matching, and interest points of sensed image are transformed to the reference coordinate system using these coefficients. The spatial distance between interest points of sensed image which have been transformed to the reference coordinates and interest points of reference image is calculated for secondary matching. The piecewise linear function is applied to the matched tie points for automatic registration of high resolution images. The proposed method can extract spatially well-distributed tie points compared with SIFT based method.

• Radiation Oncology Journal
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• v.35 no.2
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• pp.101-111
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• 2017

The number of imaging data sets has significantly increased during radiation treatment after introducing a diverse range of advanced techniques into the field of radiation oncology. As a consequence, there have been many studies proposing meaningful applications of imaging data set use. These applications commonly require a method to align the data sets at a reference. Deformable image registration (DIR) is a process which satisfies this requirement by locally registering image data sets into a reference image set. DIR identifies the spatial correspondence in order to minimize the differences between two or among multiple sets of images. This article describes clinical applications, validation, and algorithms of DIR techniques. Applications of DIR in radiation treatment include dose accumulation, mathematical modeling, automatic segmentation, and functional imaging. Validation methods discussed are based on anatomical landmarks, physical phantoms, digital phantoms, and per application purpose. DIR algorithms are also briefly reviewed with respect to two algorithmic components: similarity index and deformation models.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.6
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• pp.485-494
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• 2017

This study focuses on automatic image registration between multiple IR images using simple preprocessing method and modified local feature extraction algorithm. The input images were preprocessed by using the median and absolute value after histogram equalization, and it could be effectively applied to reduce the brightness difference value between images by applying the similarity of extracted features to the concept of angle instead of distance. The results were evaluated using visual and inverse RMSE methods. The features that could not be achieved by the existing local feature extraction technique showed high image matching reliability and application convenience. It is expected that this method can be used as one of the automatic registration methods between multi-sensor images under specific conditions.