• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2405-2407
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• 2001

In this paper, we apply and evolutionary computation(EC), probabilistic optimization algorithm, to active contour. A number of problems exist associated with such as algorithm initialization, existence of local minima, non-convex search space, and the selection of model parameters in conventional models. We propose an adequate fitness function for these problems. The determination of fitness function adequate to active contour using EC is important in search capability. As a result of applying the proposed method to non-convex object shape, we improve the unstability and contraction phenomena, in nature, of snake generated in deformable contour optimization.

• Convergence Security Journal
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• v.20 no.4
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• pp.91-101
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• 2020

We analyzed APT attack cases that occurred overseas in the past using a cyber kill chain model and a TTP model. As a result of the analysis, we found that the cyber kill chain model is effective in figuring out the overall outline, but is not suitable for establishing a specific defense strategy, however, TTP model is suitable to have a practical defense system. Based on these analysis results, it is suggested that defense technology development which is based on TTP model to build defense-in-depth system for preparing cyber attacks.

• Journal of Intelligence and Information Systems
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• v.12 no.1
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• pp.17-31
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• 2006

In this paper, passport recognition and face verification methods which can automatically recognize passport codes and discriminate forgery passports to improve efficiency and systematic control of immigration management are proposed. Adjusting the slant is very important for recognition of characters and face verification since slanted passport images can bring various unwanted effects to the recognition of individual codes and faces. Therefore, after smearing the passport image, the longest extracted string of characters is selected. The angle adjustment can be conducted by using the slant of the straight and horizontal line that connects the center of thickness between left and right parts of the string. Extracting passport codes is done by Sobel operator, horizontal smearing, and 8-neighborhood contour tracking algorithm. The string of codes can be transformed into binary format by applying repeating binary method to the area of the extracted passport code strings. The string codes are restored by applying CDM mask to the binary string area and individual codes are extracted by 8-neighborhood contour tracking algerian. The proposed RBF network is applied to the middle layer of RBF network by using the fuzzy logic connection operator and proposing the enhanced fuzzy ART algorithm that dynamically controls the vigilance parameter. The face is authenticated by measuring the similarity between the feature vector of the facial image from the passport and feature vector of the facial image from the database that is constructed with PCA algorithm. After several tests using a forged passport and the passport with slanted images, the proposed method was proven to be effective in recognizing passport codes and verifying facial images.

• Applied Microscopy
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• v.35 no.1
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• pp.31-39
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• 2005

Synapses are contact points where one neuron communicates with another. The morphological change of synapses under various physiological or pathological conditions has long been hypothesized to modify their functional properties. 3-dimensional (3-D) reconstruction of synapses with serial ultrathin sections has contributed to the understanding of ultrastructural dimensions and compositions of synapses. The 3-D reconstruction procedures, however, require a great amount of expertise as well as include prohibitively timeconsuming processes. Here, we introduce efficient 3-D reconstruction technique using high-voltage electron microscopy (HVEM). Primarily, we established an optimal section thickness and staining condition to observe synaptic structures in detail under HVEM. The result showed that synaptic profiles were preserved at the section thickness of 250 nm without the overlapping of synaptic ultrastructures. An increase in the reaction time of en bloc staining was most efficient to enhance contrast than the extension of postembedding staining or the addition of uranyl acetate during dehydration. Then, 3-D reconstruction of parallel fiber-Purkinje cell synapses in the rat cerebellum was carried out with serial HVEM images and reconstruction software. The images were aligned and the contours of synapses were outlined on each section. 3-D synapses were finally extracted from the section files by grouping all the synaptic contours. The reconstructed synapse model clearly demonstrated the configuration of pre and postsynaptic components. These results suggest that 3-D reconstruction of synapses using HVEM is much efficient and suitable for massive quantitative studies on synaptic connectivity than conventional TEM approach using numerous ultrathin sections.

• The Journal of Korean Society for Radiation Therapy
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• v.27 no.2
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• pp.145-156
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• 2015

Purpose : The purpose of this study is to evaluate the efficiency of Split VMAT planning(Contouring rectum divided into an upper and a lower for reduce rectum dose) compare to Conventional VMAT planning(Contouring whole rectum) for prostate cancer radiotherapy involving pelvic lymph nodes. Materials and Methods : A total of 9 cases were enrolled. Each case received radiotherapy with Split VMAT planning to the prostate involving pelvic lymph nodes. Treatment was delivered using TrueBeam STX(Varian Medical Systems, USA) and planned on Eclipse(Ver. 10.0.42, Varian, USA), PRO3(Progressive Resolution Optimizer 10.0.28), AAA(Anisotropic Analytic Algorithm Ver. 10.0.28). Lower rectum contour was defined as starting 1cm superior and ending 1cm inferior to the prostate PTV, upper rectum is a part, except lower rectum from the whole rectum. Split VMAT plan parameters consisted of 10MV coplanar $360^{\circ}$ arcs. Each arc had $30^{\circ}$ and $30^{\circ}$ collimator angle, respectively. An SIB(Simultaneous Integrated Boost) treatment prescription was employed delivering 50.4Gy to pelvic lymph nodes and 63~70Gy to the prostate in 28 fractions. $D_{mean}$ of whole rectum on Split VMAT plan was applied for DVC(Dose Volume Constraint) of the whole rectum for Conventional VMAT plan. In addition, all parameters were set to be the same of existing treatment plans. To minimize the dose difference that shows up randomly on optimizing, all plans were optimized and calculated twice respectively using a 0.2cm grid. All plans were normalized to the prostate $PTV_{100%}$ = 90% or 95%. A comparison of $D_{mean}$ of whole rectum, upperr ectum, lower rectum, and bladder, $V_{50%}$ of upper rectum, total MU and H.I.(Homogeneity Index) and C.I.(Conformity Index) of the PTV was used for technique evaluation. All Split VMAT plans were verified by gamma test with portal dosimetry using EPID. Results : Using DVH analysis, a difference between the Conventional and the Split VMAT plans was demonstrated. The Split VMAT plan demonstrated better in the $D_{mean}$ of whole rectum, Up to 134.4 cGy, at least 43.5 cGy, the average difference was 75.6 cGy and in the $D_{mean}$ of upper rectum, Up to 1113.5 cGy, at least 87.2 cGy, the average difference was 550.5 cGy and in the $D_{mean}$ of lower rectum, Up to 100.5 cGy, at least -34.6 cGy, the average difference was 34.3 cGy and in the $D_{mean}$ of bladder, Up to 271 cGy, at least -55.5 cGy, the average difference was 117.8 cGy and in $V_{50%}$ of upper rectum, Up to 63.4%, at least 3.2%, the average difference was 23.2%. There was no significant difference on H.I., and C.I. of the PTV among two plans. The Split VMAT plan is average 77 MU more than another. All IMRT verification gamma test results for the Split VMAT plan passed over 90.0% at 2 mm / 2%. Conclusion : As a result, the Split VMAT plan appeared to be more favorable in most cases than the Conventional VMAT plan for prostate cancer radiotherapy involving pelvic lymph nodes. By using the split VMAT planning technique it was possible to reduce the upper rectum dose, thus reducing whole rectal dose when compared to conventional VMAT planning. Also using the split VMAT planning technique increase the treatment efficiency.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.8
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• pp.1365-1373
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• 2002

In this paper, we present two types of vision algorithm that mobile robot has CCD camera. for obstacle avoidance. This is simple algorithm that compare with grey level from input images. Also, The mobile robot depend on image processing and move command from PC host. we has been studied self controlled mobile robot system with CCD camera. This system consists of digital signal processor, step motor, RF module and CCD camera. we used wireless RF module for movable command transmitting between robot and host PC. This robot go straight until recognize obstacle from input image that preprocessed by edge detection, converting, thresholding. And it could avoid the obstacle when recognize obstacle by line histogram intensity. Host PC measurement wave from various line histogram each 20 pixel. This histogram is (x, y) value of pixel. For example, first line histogram intensity wave from (0, 0) to (0, 197) and last wave from (280, 0) to (2n, 197. So we find uniform wave region and nonuniform wave region. The period of uniform wave is obstacle region. we guess that algorithm is very useful about moving robot for obstacle avoidance.

• Journal of the Korea Computer Graphics Society
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• v.18 no.4
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• pp.1-8
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• 2012

We present improved deformation energy to enhance the visual quality of a shape deformation technique, where we preserve the local structure of an input planar shape. The deformation energy, in general, consists of several constraints such as Laplacian coordinate constraint to preserve the quality of deformed silhouette edges, mean value coordinates and edge length constraints to preserve the quality of deformed internal shape, and user-specified position constraints to control the shape deformation. When the positions of user-specified vertices change, shape deformation techniques compute the positions of the other vertices by means of nonlinear least squares optimization to minimize the deformation energy. When a user-specified vertex changes its position rapidly, it is frequently observed that the visual quality of the deformed shape decrease rapidly, which is mainly caused by unnecessary enlargement of the Laplacian vectors and unnecessary change of the edge directions along the boundary of the shape. In this paper, we propose improved deformation energy by prohibiting the Laplacian and edge length constraints from changing unnecessarily. The proposed deformation energy incorporated with well-known optimization technique can enhance the visual quality of shape deformation along the silhouette and within the interior of the planar shape while sacrificing only a little execution time.

• Journal of Korea Multimedia Society
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• v.15 no.9
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• pp.1075-1085
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• 2012

In clinical research using medical images, the image segmentation is one of the most important processes. Especially, the hippocampal atrophy is helpful for the clinical Alzheimer diagnosis as a specific marker of the progress of Alzheimer. In order to measure hippocampus volume exactly, segmentation of the hippocampus is essential. However, the hippocampus has some features like relatively low contrast, low signal-to-noise ratio, discreted boundary in MRI images, and these features make it difficult to segment hippocampus. To solve this problem, firstly, We selected region of interest from an experiment image, subtracted a original image from the negative image of the original image, enhanced contrast, and applied anisotropic diffusion filtering and gaussian filtering as preprocessing. Finally, We performed an image segmentation using two level set methods. Through a variety of approaches for the validation of proposed hippocampus segmentation method, We confirmed that our proposed method improved the rate and accuracy of the segmentation. Consequently, the proposed method is suitable for segmentation of the area which has similar features with the hippocampus. We believe that our method has great potential if successfully combined with other research findings.

• Journal of KIISE:Databases
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• v.36 no.2
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• pp.73-83
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• 2009

In this paper we propose an object similarity matching method based on shape characteristics of an object in an image. The proposed method extracts edge points from edges of objects and generates a log polar histogram with respect to each edge point to represent the relative placement of extracted points. It performs the matching in such a way that it compares polar histograms of two edge points sequentially along with edges of objects, and uses a well-known k-NN(nearest neighbor) approach to retrieve similar objects from a database. To verify the proposed method, we've compared it to an existing Shape-Context method. Experimental results reveal that our method is more accurate in object matching than the existing method, showing that when k=5, the precision of our method is 0.75-0.90 while that of the existing one is 0.37, and when k=10, the precision of our method is 0.61-0.80 while that of the existing one is 0.31. In the experiment of rotational transformation, our method is also more robust compared to the existing one, showing that the precision of our method is 0.69 while that of the existing one is 0.30.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.4
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• pp.125-132
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• 2021

As the layers of artificial neural network deepens, and the dimension of data used as an input increases, there is a problem of high arithmetic operation requiring a lot of arithmetic operation at a high speed in the learning and recognition of the neural network (NN). Thus, this study proposes a data dimensionality reduction method to reduce the dimension of the input data in the NN. The proposed Line-segment Feature Analysis (LFA) algorithm applies a gradient-based edge detection algorithm using median filters to analyze the line-segment features of the objects existing in an image. Concerning the extracted edge image, the eigenvalues corresponding to eight kinds of line-segment are calculated, using 3×3 or 5×5-sized detection filters consisting of the coefficient values, including [0, 1, 2, 4, 8, 16, 32, 64, and 128]. Two one-dimensional 256-sized data are produced, accumulating the same response values from the eigenvalue calculated with each detection filter, and the two data elements are added up. Two LFA256 data are merged to produce 512-sized LAF512 data. For the performance evaluation of the proposed LFA algorithm to reduce the data dimension for the recognition of handwritten numbers, as a result of a comparative experiment, using the PCA technique and AlexNet model, LFA256 and LFA512 showed a recognition performance respectively of 98.7% and 99%.