• The Transactions of the Korean Institute of Electrical Engineers
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• v.40 no.4
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• pp.394-401
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• 1991

B-spline function is generally used for an image interpolation because of its smoothness and continuity, but it accompanies a large amount of blurring effect. In this paper, a space-variant B-spline interpolation function is proposed through deblurring process followed by de-aliasing process. The proposed function has parametric expression and performs smoothing and edge-enhancement adaptively in the interpolation process according to local property of the image. Application of this function to image enlargement, rotation, and curve representation producted good results. Even in the presence of noise, noise smoothing effect as well as edge-enhancement were observed in the image interpolation process.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.1167-1187
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• 2020

With the development of medical imaging technology, image registration has been widely used in the field of disease diagnosis. The registration between different modal images of brain magnetic resonance (MR) is particularly important for the diagnosis of brain diseases. However, previous registration methods don't take advantage of the prior knowledge of bilateral brain symmetry. Moreover, the difference in gray scale information of different modal images increases the difficulty of registration. In this paper, a multimodal medical image registration method based on image segmentation and symmetric self-similarity is proposed. This method uses modal independent self-similar information and modal consistency information to register images. More particularly, we propose two novel symmetric self-similarity constraint operators to constrain the segmented medical images and convert each modal medical image into a unified modal for multimodal image registration. The experimental results show that the proposed method can effectively reduce the error rate of brain MR multimodal medical image registration with rotation and translation transformations (average 0.43mm and 0.60mm) respectively, whose accuracy is better compared to state-of-the-art image registration methods.

• Progress in Medical Physics
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• v.14 no.3
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• pp.189-195
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• 2003

The comparative analysis of a portal image and a simulation image is a very important process in radiotherapy for verifying the accuracy of an actual treatment field. In this study, we applied a chamfer-matching algorithm to compare a portal image with a simulation image and verified the accuracy of the algorithm to analyze the field matching error in the portal image. We also developed an analysis program that could analyze the two images more effectively with a chamfer-matching method and demonstrated its efficacy through a feasibility study. With virtual portal images, the accuracy of the analysis algorithm were acceptable considering the average error of shift (0.64 mm), rotation (0.32$^{\circ}$), and scale (1.61%). When the portal images of a head and neck phantom were analyzed, the accuracy and suitability of the developed analysis program was proven considering the acceptable average error of shift (1.55 mm), rotation (0.80$^{\circ}$), and scale (1.72%). We verified the applicability of a chamfer-matching algorithm to the comparative analysis of a portal image with a simulation image. The analysis program developed in this study was a practical tool to calculate the quantitative error of the treatment field in a portal image.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.5
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• pp.449-457
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• 2011

As automated image processing techniques have been required in multi-temporal/multi-sensor geospatial image applications, use of automated but highly invariant image matching technique has been a critical ingredient. Note that there is high possibility of geometric and spectral differences between multi-temporal/multi-sensor geospatial images due to differences in sensor, acquisition geometry, season, and weather, etc. Among many image matching techniques, the SIFT (Scale Invariant Feature Transform) is a popular method since it has been recognized to be very robust to diverse imaging conditions. Therefore, the SIFT has high potential for the geospatial image processing. This paper presents a performance test results of the SIFT on geospatial imagery by simulating various image differences such as shear, scale, rotation, intensity, noise, and spectral differences. Since a geospatial image application often requires a number of good matching points over the images, the number of matching points was analyzed with its matching positional accuracy. The test results show that the SIFT is highly invariant but could not overcome significant image differences. In addition, it guarantees no outlier-free matching such that it is highly recommended to use outlier removal techniques such as RANSAC (RANdom SAmple Consensus).

• Journal of Korea Multimedia Society
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• v.18 no.12
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• pp.1453-1461
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• 2015

Since digital images and videos are rapidly increasing in the internet with the spread of mobile computers and smartphones, research on image retrieval has gained tremendous momentum. Color, shape, and texture are major features used in image retrieval. Especially, color information has been widely used in image retrieval, because it is robust in translation, rotation, and a small change of camera view. This paper proposes a new method for histogram refinement based on local color difference. Firstly, the proposed method converts a RGB color image into a HSV color image. Secondly, it reduces the size of color space from 256³ to 32. It classifies pixels in the 32-color image into three groups according to the color difference between a central pixel and its neighbors in a 3x3 local region. Finally, it makes a color difference vector(CDV) representing three refined color histograms, then image retrieval is performed by the CDV matching. The experimental results using public image database show that the proposed method has higher retrieval accuracy than other conventional ones. They also show that the proposed method can be effectively applied to search low resolution images such as thumbnail images.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1984.10a
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• pp.101-103
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• 1984

The proposed seal identification procedure consists of the thresholding smoothing, rotation, thinning, and matching techniques. The weighted map is constructed by ditance weighted correlation CK is computed. The CK is compared with the dicision constant Cs or Cd for the purpose of seal identification.

• Journal of Platform Technology
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• v.6 no.4
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• pp.26-33
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• 2018

In this paper, we propose an image retrieval algorithm for real-time processing and design it as hardware. The proposed method is based on the classification of BoWs(Bag of Words) algorithm and proposes an image search algorithm using bit stream. K-fold cross validation is used for the verification of the algorithm. Data is classified into seven classes, each class has seven images and a total of 49 images are tested. The test has two kinds of accuracy measurement and speed measurement. The accuracy of the image classification was 86.2% for the BoWs algorithm and 83.7% the proposed hardware-accelerated software implementation algorithm, and the BoWs algorithm was 2.5% higher. The image retrieval processing speed of BoWs is 7.89s and our algorithm is 1.55s. Our algorithm is 5.09 times faster than BoWs algorithm. The algorithm is largely divided into software and hardware parts. In the software structure, C-language is used. The Scale Invariant Feature Transform algorithm is used to extract feature points that are invariant to size and rotation from the image. Bit streams are generated from the extracted feature point. In the hardware architecture, the proposed image retrieval algorithm is written in Verilog HDL and designed and verified by FPGA and Design Compiler. The generated bit streams are stored, the clustering step is performed, and a searcher image databases or an input image databases are generated and matched. Using the proposed algorithm, we can improve convenience and satisfaction of the user in terms of speed if we search using database matching method which represents each object.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.3
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• pp.643-650
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• 2014

A hardware implementation of phase calculator for extracting 3D depth image from TOF(Time-Of-Flight) sensor is described. The designed phase calculator adopts redundant binary number systems and a pipelined architecture to improve throughput and speed. It performs arctangent operation using vectoring mode of DCORDIC(Differential COordinate Rotation DIgital Computer) algorithm. Fixed-point MATLAB simulations are carried out to determine the optimal bit-widths and number of iteration. The phase calculator has ben verified by FPGA-in-the-loop verification using MATLAB/Simulink. A test chip has been fabricated using a TSMC $0.18-{\mu}m$ CMOS process, and test results show that the chip functions correctly. It has 82,000 gates and the estimated throughput is 400 MS/s at 400Mhz@1.8V.

• Journal of Internet Computing and Services
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• v.24 no.4
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• pp.85-92
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• 2023

Deep learning applications are increasingly being leveraged for disease detection tasks in medical imaging modalities such as X-ray, Computed Tomography (CT), and Magnetic Resonance Imaging (MRI). Most data-centric deep learning challenges necessitate the use of supervised learning methodologies to attain high accuracy and to facilitate performance evaluation through comparison with the ground truth. Supervised learning mandates a substantial amount of image and label sets, however, procuring an adequate volume of medical imaging data for training is a formidable task. Various data augmentation strategies can mitigate the underfitting issue inherent in supervised learning-based models that are trained on limited medical image and label sets. This research investigates the enhancement of a deep learning-based rib fracture segmentation model and the efficacy of data augmentation techniques such as left-right flipping, rotation, and scaling. Augmented dataset with L/R flipping and rotations(30°, 60°) increased model performance, however, dataset with rotation(90°) and ⨯0.5 rescaling decreased model performance. This indicates the usage of appropriate data augmentation methods depending on datasets and tasks.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1996.06a
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• pp.181-185
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• 1996

Color image is largely corrupted by various ambient illumination. However, human perceives always white color as white under any illumination because of a characteristic of human vision, called color constancy. In the conventional algorithm which applied the constancy effect, after the RGB color space is transformed to the IHS(Intensity, Hue, and Saturation) color space, then the hue is preserved and the intensity or the saturation is properly enhanced. Then the enhanced IHS color is reversely transformed to the RGB color space. In this process, the color distortion is included due to the color gamut error. But in the proposed algorithm, there is not transformation. In that, the RGB color is considered as 3 dimensional color vector and we assume that white color is the natural daylight. As the color vector of the illumination can be calculated as the average vector of R, G, and B image, we can achieve the constancy effect by simply rotating the illumination vector to the white color vector. The simulation results show the efficiency of the vector rotating process for color image enhancement.