• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.740-745
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• 2003

Motion tracking and object segmentation are the most fundamental and critical problems in vision tasks suck as motion analysis. An active contour model, snake, was developed as a useful segmenting and tracking tool for rigid or non-rigid objects. Snake is designed no the basis of snake energies. Segmenting and tracking can be executed successfully by energy minimization. In this research, two new paradigms for segmentation and tracking are suggested. First, because the conventional method uses only intensity information, it is difficult to separate an object from its complex background. Therefore, a new energy and design schemes should be proposed for the better segmentation of objects. Second, conventional snake can be applied in situations where the change between images is small. If a fast moving object exists in successive images, conventional snake will not operate well because the moving object may have large differences in its position or shape, between successive images. Snakes's nodes may also fall into the local minima in their motion to the new positions of the target object in the succeeding image. For robust tracking, the condensation algorithm was adopted to control the parameters of the proposed snake model called "adaptive color snake model(SCSM)". The effectiveness of the ACSM is verified by appropriate simulations and experiments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.6
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• pp.3208-3229
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• 2017

Rectification is an essential procedure for simplifying the disparity extraction of stereo matching algorithms by removing vertical mismatches between left and right images. To support real-time stereo matching, studies have introduced several look-up table (LUT)- and computational logic (CL)-based rectification approaches. However, to support high-resolution images, the LUT-based approach requires considerable memory resources, and the CL-based approach requires numerous hardware resources for its circuit implementation. Thus, this paper proposes a multi-level accumulation-based rectification method as a simple CL-based method and its circuit implementation. The proposed method, which includes distortion correction, reduces addition operations by 29%, and removes multiplication operations by replacing the complex matrix computations and high-degree polynomial calculations of the conventional rectification with simple multi-level accumulations. The proposed rectification circuit can rectify $1,280{\times}720$ stereo images at a frame rate of 135 fps at a clock frequency of 125 MHz. Because the circuit is fully pipelined, it continuously generates a pair of left and right rectified pixels every cycle after 13-cycle latency plus initial image buffering time. Experimental results show that the proposed method requires significantly fewer hardware resources than the conventional method while the differences between the results of the proposed and conventional full rectifications are negligible.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.1
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• pp.395-413
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• 2016

Image authentication is a technique aiming at protecting the integrity of digital images. Reversible image authentication has attracted much attention of researcher because it allows to authenticate tampered regions in the image and to reconstruct the stego image to its original version losslessly. In this paper, we propose a new, reversible image authentication scheme based on adaptive prediction error expansion (PEE) technique. In the proposed scheme, each image block is classified into smooth or complex regions. Then, according to the characteristic of each block, the authentication code is embedded adaptively to achieve high performance of tamper detection. The experimental results demonstrated that the proposed scheme achieves good quality of stego images. In addition, the proposed scheme has ability to reconstruct the stego image to its original version, if no modification is performed on it. Also demonstrated in the experimental results, the proposed scheme provides higher accuracy of tamper detection than state-of-the-art schemes.

• Journal of Multimedia Information System
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• v.6 no.4
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• pp.209-216
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• 2019

Over the past decade, researchers were able to solve complex medical problems as well as acquire deeper understanding of entire issue due to the availability of machine learning techniques, particularly predictive algorithms and automatic recognition of patterns in medical imaging. In this study, a technique called transfer learning has been utilized to classify Magnetic Resonance (MR) images by a pre-trained Convolutional Neural Network (CNN). Rather than training an entire model from scratch, transfer learning approach uses the CNN model by fine-tuning them, to classify MR images into Alzheimer's disease (AD), mild cognitive impairment (MCI) and normal control (NC). The performance of this method has been evaluated over Alzheimer's Disease Neuroimaging (ADNI) dataset by changing the learning rate of the model. Moreover, in this study, in order to demonstrate the transfer learning approach we utilize different pre-trained deep learning models such as GoogLeNet, VGG-16, AlexNet and ResNet-18, and compare their efficiency to classify AD. The overall classification accuracy resulted by GoogLeNet for training and testing was 99.84% and 98.25% respectively, which was exceptionally more than other models training and testing accuracies.

• Journal of the Optical Society of Korea
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• v.19 no.1
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• pp.102-111
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• 2015

This paper proposes an algorithm for recovering the colors of objects from multiple near-infrared (near-IR) images. The International Commission on Illumination (CIE) color coordinates of objects are recovered from a series of gray images captured under multiple spectral near-IR illuminations using polynomial regression. The feasibility of the proposed algorithm is tested experimentally by using 24 color patches of the Color Rendition Chart. The experimental apparatus is composed of a monochrome digital camera without an IR cut-off filter and a custom-designed LED illuminator emitting multiple spectral near-IR illuminations, with peak wavelengths near the red edge of the visible band, namely at 700, 740, 780, and 860 nm. The average color difference between the original and the recovered colors for all 24 patches was found to be 11.1. However, if some particular patches with high value are disregarded, the average color difference is reduced to 4.2, and this value is within the acceptability tolerance for complex image on the display.

• Journal of Trauma and Injury
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• v.19 no.1
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• pp.28-34
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• 2006

Purpose: In maxillofacial surgery, proper preoperative diagnosis is very important in achieving good postoperative results. Although conventional CT scans are useful for visual representations of fractures, they cannot provide direct guidance for reconstructing facial bone fractures. However, the recent technology of multislice scanning has brought many clinical benefits to CT images. Direct correlations can be made between preoperative imaging data and operative planning. The aim of the current study is to evaluate the differences between conventional CT and multidetective three-dimensional CT(3D MDCT) measurements in craniofacial deformities. Methods: From January 2005 to November 2005, MDCT scans of 41 patients were evaluated by comparing them with conventional CT scans. The 3D MDCT images were assessed and reviewed by using a simple scoring system. Results: The 3D MDCT scans offered easy interpretation, facilitated surgical planning, and clarified postoperative results in malar complex fractures, mandibular fractures, and extensive maxillofacial fractures and cranioplasty. However, 3D MDCT images were not superior to conventional CT scans in the diagnosis of blowout fractures. Conclusion: In spite of its limitations, the 3D MDCT provided additional and more comprehensive information than the conventional CT for preoperative assessment of craniofacial deformities. Therefore, the 3D MDCT can be a useful tool for diagnosis and systematic treatment planning in craniofacial skeletal deformities.

• Journal of Korean Elementary Science Education
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• v.30 no.2
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• pp.162-177
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• 2011

The purpose of this qualitative case study was to investigate the 'structure' of an elementary school teacher's practical knowledge concerning science experiment lessons. A female elementary teacher in the early career years participated in the study, and video recordings of her science experiment lessons as well as audio-taped interviews with her were analyzed by means of Elbaz's framework. The teacher expressed six images of science experiment lessons: 'Science is difficult', 'Experiments are dangerous', 'Experiments are accurate', 'A science experiment takes a long time', 'Science experiments are interesting', and 'Children are little scientists.' These images were supported by several principles and rules, most of which were clearly described. Among the images, principles, and rules, there were complex relationships with some working in synergy and some conflicting. In case of the image 'Children are little scientists', its subordinate principles and rules were not fully realized in the classroom. Implications for science teaching reform and science education research were discussed.

• The KIPS Transactions:PartB
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• v.13B no.3 s.106
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• pp.239-244
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• 2006

In this paper, we present a snake-based scheme for contour tracking of objects in stereo image sequences. We address the problem by managing the insertion of new points and deletion of unnecessary points to better describe and track the object's boundary. In particular, our method uses more points in highly curved parts of the contour, and fewer points in less curved parts. The proposed algorithm can successfully define the contour of the object, and can track the contour in complex images. Furthermore, we tested our algorithm in the presence of partial object occlusion. Performance of the proposed algorithm has been verified by simulation.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.11
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• pp.541-545
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• 1997

This paper presents a modified geometric active contour model or edge detection and segmentation of computed tomography(CT) scan images. The method is based on the level setup approach developed by Osher and Sethian and the modeling of propagation fronts with curvature dependent speeds by Malladi. Based on above algorithms, the geometric active contour is obtained through a particular level set of hypersurface lowing along its gradient force and curvature force. This technique retains the attractive feature which is topological and geometric flexibility of the contour in recovering objects with complex shapes and unknown topologies. But there are limitations in this algorithm which are being not able to separate the object with weak difference from neighbor object. So we use speed limitation filter to overcome those problems. We apply a 2D model to various synthetic cases and the three cases of real CT scan images in order to segment objects with complicated shapes and topologies. From the results, the presented model confirms that it attracts very naturally and efficiently to the desired feature of CT scan images.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.1
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• pp.59-65
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• 2016

As modern electronic devices get smaller and smaller, high-resolution, large Field-Of-View (FOV), fast, and cost-effective 3-dimensional (3-D) measurement is requested more and more. In particular, defect inspection machines using machine-vision technology nowadays require 3-D inspection as well as the conventional 2-D inspection. Phase Measuring Profilometry (PMP) is one of the fast non-contact 3-D shape measuring methods currently being extensively investigated in the electronic component manufacturing industry. The PMP system is well known and is successfully applied to measuring complex surface profiles with varying reflectance properties. However, for highly reflective surfaces, such as Ball Grid Arrays (BGAs), it has difficulty accurately measuring 3-D shapes. In this paper, we propose a new fast optical system that can eliminate the highly reflective saturated regions in BGA ball images. This is achieved by utilizing four Low Intensity Grating (LIG) images together with the conventional High Intensity Grating (HIG) images. Extensive experiments using BGA samples show a repeatability of under ${\pm}20um$ in standard deviation, which is suitable for most 3-D shape measurements of BGAs.