• Aerospace Engineering and Technology
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• v.1 no.2
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• pp.66-74
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• 2002

The characteristics of the satellite image quality parameters driven by satellite imaging instrument are investigated. Since the satellite image is directly produced by the satellite imaging instrument, the satellite image quality depends on the imager performance highly. This is why the imager performance parameters are considered as an important part of the satellite image quality parameters. The imager performance parameters consist of spectral band parameters, ground sample distance(GSD) parameters, swath width parameters, imager Modulation Transfer Function (MTF), imager Signal to Noise Ratio(SNR), radiometric response characteristics parameters, pixel registration, and imager calibration.

• Journal of the Institute of Convergence Signal Processing
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• v.3 no.4
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• pp.29-34
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• 2002

In this paper, we present a new geometric active contour model based on level set methods introduced by Osher and Sethian for detection of object boundaries or shape and we adopt anisotropic diffusion filtering method for removing noise from original image. In order to minimize the processing time, we use the narrow band method which allows us to perform calculations in the neighborhood of the contour and not in the whole image. Using anisotropic diffusion filtering for each slice, we have the result with reduced noise and extracted exact shape. Volume rendering operates on three-dimensional data, processes it, and transforms it into a simple two-dimensional image.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.4
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• pp.229-240
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• 2019

3D geo-spatial model have been widely used in the field of Civil Engineering, Medical, Computer Graphics, Urban Management and many other. Especially, the demand for high quality 3D spatial information such as precise road map construction has explosively increased, MMS and UAV techniques have been actively used to acquire them more easily and conveniently in surveying and geo-spatial field. However, in order to perform 3D modeling by integrating the two data set from MMS and UAV, its so needed an proper registration method is required to efficiently correct the difference between the raw data acquisition sensor, the point cloud data generation method, and the observation accuracy occurred when the two techniques are applied. In this study, we obtained UAV point colud data in Yeouido area as the study area in order to determine the automatic registration performance between MMS and UAV point cloud data using ICP(Iterative Closet Point) method. MMS observations was then performed in the study area by dividing 4 zones according to the level of overlap ratio and observation noise with based on UAV data. After we manually registered the MMS data to the UAV data, then compared the results which automatic registered using ICP method. In conclusion, the higher the overlap ratio and the lower the noise level, can bring the more accurate results in the automatic registration using ICP method.

• Journal of KIISE:Software and Applications
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• v.32 no.7
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• pp.612-624
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• 2005

In this paper, we propose a surface-based registration using a gaussian weighted distance map for PET-CT brain image fusion. Our method is composed of three main steps: the extraction of feature points, the generation of gaussian weighted distance map, and the measure of similarities based on weight. First, we segment head using the inverse region growing and remove noise segmented with head using region growing-based labeling in PET and CT images, respectively. And then, we extract the feature points of the head using sharpening filter. Second, a gaussian weighted distance map is generated from the feature points in CT images. Thus it leads feature points to robustly converge on the optimal location in a large geometrical displacement. Third, weight-based cross-correlation searches for the optimal location using a gaussian weighted distance map of CT images corresponding to the feature points extracted from PET images. In our experiment, we generate software phantom dataset for evaluating accuracy and robustness of our method, and use clinical dataset for computation time and visual inspection. The accuracy test is performed by evaluating root-mean-square-error using arbitrary transformed software phantom dataset. The robustness test is evaluated whether weight-based cross-correlation achieves maximum at optimal location in software phantom dataset with a large geometrical displacement and noise. Experimental results showed that our method gives more accuracy and robust convergence than the conventional surface-based registration.

• Journal of Broadcast Engineering
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• v.26 no.1
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• pp.26-38
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• 2021

In this paper, DLKF (Deep Learning Keypoint Filtering), the deep learning-based keypoint filtering method for the rapidization of the image registration method for remote sensing images is proposed. The complexity of the conventional feature-based image registration method arises during the feature matching step. To reduce this complexity, this paper proposes to filter only the keypoints detected in the artificial structure among the keypoints detected in the keypoint detector by ensuring that the feature matching is matched with the keypoints detected in the artificial structure of the image. For reducing the number of keypoints points as preserving essential keypoints, we preserve keypoints adjacent to the boundaries of the artificial structure, and use reduced images, and crop image patches overlapping to eliminate noise from the patch boundary as a result of the image segmentation method. the proposed method improves the speed and accuracy of registration. To verify the performance of DLKF, the speed and accuracy of the conventional keypoints extraction method were compared using the remote sensing image of KOMPSAT-3 satellite. Based on the SIFT-based registration method, which is commonly used in households, the SURF-based registration method, which improved the speed of the SIFT method, improved the speed by 2.6 times while reducing the number of keypoints by about 18%, but the accuracy decreased from 3.42 to 5.43. Became. However, when the proposed method, DLKF, was used, the number of keypoints was reduced by about 82%, improving the speed by about 20.5 times, while reducing the accuracy to 4.51.

• Journal of the Korea Computer Graphics Society
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• v.7 no.1
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• pp.37-49
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• 2001

We propose an optimization of mutual information for multiresolution image registration to represent useful information as integrated form obtaining from complementary information of multi modality images. The method applies mutual information as cost function to measure the statistical dependency or information redundancy between the image intensities of corresponding pixels in both images, which is assumed to be maximal if the images are geometrically aligned. As experimental results we validate visual inspection for accuracy, changning initial condition and addictive noise for robustness. Since our method uses the native image rather than prior feature extraction, few user interaction is required to perform the registration. In addition it leads to robust density estimation and convergence as applying non-parametric density estimation and stochastic multiresolution optimization.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.8
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• pp.1785-1791
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• 2003

This paper presents the mosaic method that the video sequence with shift and rotation information after Mellin Transform. The results are used to compute the projection matrix for each image registration. So before registration, we process camera calibration in order to reduce the image warp by camera and then compute the global projection matrix for image registration for reducing errors from rut image to last image. This paper describes the mosaic method that compute duplication and movement information quickly and robust noise using projection matrix on Mellin Transform.

• Progress in Medical Physics
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• v.22 no.1
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• pp.28-34
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• 2011

To perform the Adaptive Radiation Therapy (ART), a high degree of deformable registration accuracy is essential. The purpose of this study is to identify whether the change of MV CBCT intensity can improve registration accuracy using predefined modification level and filtering process. To obtain modification level, the cheese phantom images was acquired from both kilovoltage CT (kV CT), megavoltage cone-beam CT (MV CBCT). From the cheese phantom images, the modification level of MV CBCT was defined from the relationship between Hounsfield Units (HUs) of kV CT and MV CBCT images. 'Gaussian smoothing filter' was added to reduce the noise of the MV CBCT images. The intensity of MV CBCT image was changed to the intensity of the kV CT image to make the two images have the same intensity range as if they were obtained from the same modality. The demon deformable registration which was efficient and easy to perform the deformable registration was applied. The deformable lung phantom which was intentionally created in the laboratory to imitate the changes of the breathing period was acquired from kV CT and MV CBCT. And then the deformable lung phantom images were applied to the proposed method. As a result of deformable image registration, the similarity of the correlation coefficient was used for a quantitative evaluation of the result was increased by 6.07% in the cheese phantom, and 18% in the deformable lung phantom. For the additional evaluation of the registration of the deformable lung phantom, the centric coordinates of the mark which was inserted into the inner part of the phantom were measured to calculate the vector difference. The vector differences from the result were 2.23, 1.39 mm with/without modification of intensity of MV CBCT images, respectively. In summary, our method has quantitatively improved the accuracy of deformable registration and could be a useful solution to improve the image registration accuracy. A further study was also suggested in this paper.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.05a
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• pp.736-741
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• 2004

In high-capacity disk drives with ever-growing track density, the allowable level of position error signal (PES) is becoming smaller and smaller. In order to achieve the high TPI disk drive, it is necessary to improve the writing accuracy during the STW(servo track writing) process through the reduction of TMR sources. Among the main contributors of the NRRO(Non-Repeatable Run-out) PES, the disk vibration and the HSA(head-stack assembly) vibration is considered to be one of the most significant factors. Also the most contributors of RRO(Repeatable Run- out) come from the contributors of NRRO which is written-in at the time of STW(servo track writing) process. In this paper, the experimental test result shows that the effect of NRRO on servo written-in RRO effectively can be reduced through a STW process under low dense medium condition such as semi-vacuum.

• The Journal of the Society of Korean Medicine Diagnostics
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• v.20 no.1
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• pp.37-44
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• 2016

Ultrasonic imaging is the most widely modality among modern imaging device for medical diagnosis. Nevertheless, medical ultrasound images suffer from speckle noise and low contrast. In this paper, we propose probabilistic edge map for ultrasound image edge enhancement using automatic alien algorithm. The proposed method used applied speckle reduced ultrasound imaging for edge improvement using sequentially acquired ultrasound imaging. To evaluate the performance of method, the similarity between the reference and edge enhanced image was measured by quantity analysis. The experimental results show that the proposed method considerably improves the image quality with region edge enhancement.