• 한국전산유체공학회:학술대회논문집
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• 2003.08a
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• pp.76-81
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• 2003

전산유체공학에서 그래픽 인터페이스를 이용한 후처리 기법은 수렴된 해의 물리적 구조 및 특성을 이해하는데 있어 매우 중요하다. 따라서 본 연구에서는 그래픽 환경을 이용하여 압축성 유동 해석 코드인 CSCM 수치해석 코드의 후처리 과정을 개발함으로서 코드전체의 완전성을 높이고자 하였다. Visual C++프로그램을 이용하여 Mesh plot, XY plot, 벡터 plot 및 contour plot이 가능한 후처리 프로그램을 개발하였으며 실시간으로 수치해석의 수렴정도를 파악할 수 있는 잔류항에 대한 그래픽 기능을 제공하게 하였다. 개발된 후처리 과정을 2차원 Compression ramp 및 Bump 문제의 해석결과에 대해 본 연구결과와 현재 유체해석의 후처리 프로그램으로 많은 사용자를 확보하고 있는 AMTEC사의 Tecplot 8.0 버전의 결과를 서로 비교해 본 결과 좋은 일치성을 보여주었다.

• Journal of Advanced Marine Engineering and Technology
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• v.31 no.6
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• pp.681-686
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• 2007

The flow analysis was made by applying the turbulent models in the scroll volume of centrifugal compressor. The $k-{\varepsilon}.\;k-{\omega}$, Spalart-Allmaras and reynolds stress models are used in which the hybrid grid is applied for the simulation. The velocity vector the Pressure contour. the change of residual along the iteration number. and the dynamic head are simulated by solving the Navier-Stokes equations for the comparison of four example cases.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.917-922
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• 1990

Pattern classification is an essential step in automatic robotic assembly which joins together finite number of seperated industrial parts. In this paper, a fast and systematic algorithm for classifying occlusion-free objects is proposed, using the notion of incremental circle transform which describes the boundary contour of an object as a parametric vector function of incremental elements. With similarity transform and line integral, normalized determinant curve of an object classifies each object, independent of position, orientation, scaling of an object and cyclic shift of the stating point for the boundary description.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1997.11a
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• pp.123-126
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• 1997

최근에는 영역(혹은 객체)를 이용하여 비디오 시퀀스를 표현하거나 부호화하는 기법들이 많이 연구되고 있다. 이러한 부호화 기법에서는 형태정보를 효율적으로 이용하는 것이 중요함에도 불구하고, 현재 사용되고 있는 대부분의 기법에서는 기존의 블록 기반 부호화 알고리즘에서처럼 오직 PSNR 만을 고려하여 움직임 벡터를 추정하고 있다. 따라서, 형태 정보를 다루는 효율적 움직임 추정 알고리즘이 필요하다. 본 논문에서는 각 영역의 경계(contour)를 잘 피팅(fitting)시키는 움직임 추정 방법을 생각해 본다. 이를 위하여 PSNR과 영역의 모양을 함께 고려하는 비용함수를 제안하고 이를 이용한 움직임 벡터 추정을 고려해 본다.

• Journal of Digital Contents Society
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• v.15 no.6
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• pp.675-682
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• 2014

Prostate is only an organ of men. To diagnose the disease of the prostate, generally transrectal ultrasound(TRUS) images are used. Detecting its boundary is a challenging and difficult task due to weak prostate boundaries, speckle noise and the short range of gray levels. In this paper a method for automatic prostate segmentation in TRUS images using Support Vector Machine(SVM) is presented. This method involves preprocessing, extracting Gabor feature, training, and prostate segmentation. The speckle reduction for preprocessing step has been achieved by using stick filter and top-hat transform has been implemented for smoothing. Gabor filter bank for extraction of rotation-invariant texture features has been implemented. SVM for training step has been used to get each feature of prostate and nonprostate. Finally, the boundary of prostate is extracted. A number of experiments are conducted to validate this method and results shows that the proposed algorithm extracted the prostate boundary with less than 10% relative to boundary provided manually by doctors.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.10
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• pp.3708-3728
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• 2021

Indian herbal plants are used in agriculture and in the food, cosmetics, and pharmaceutical industries. Laboratory-based tests are routinely used to identify and classify similar herb species by analyzing their internal cell structures. In this paper, we have applied computer vision techniques to do the same. The original leaf image was preprocessed using the Chan-Vese active contour segmentation algorithm to efface the background from the image by setting the contraction bias as (v) -1 and smoothing factor (µ) as 0.5, and bringing the initial contour close to the image boundary. Thereafter the segmented grayscale image was fed to a leaky capacitance fired neuron model (LCFN), which differentiates between similar herbs by combining different groups of pixels in the leaf image. The LFCN's decay constant (f), decay constant (g) and threshold (h) parameters were empirically assigned as 0.7, 0.6 and h=18 to generate the 1D feature vector. The LCFN time sequence identified the internal leaf structure at different iterations. Our proposed framework was tested against newly collected herbal species of natural images, geometrically variant images in terms of size, orientation and position. The 1D sequence and shape features of aloe, betel, Indian borage, bittergourd, grape, insulin herb, guava, mango, nilavembu, nithiyakalyani, sweet basil and pomegranate were fed into the 5-fold Bayesian regularization neural network (BRNN), K-nearest neighbors (KNN), support vector machine (SVM), and ensemble classifier to obtain the highest classification accuracy of 91.19%.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.312-316
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• 2004

The paper presents a technique to identify the boundary of the optic disc in infant retinal digital images using an approach based on active contours (snakes). The technique can be used to be develop a automate system in order to help the ophthalmologist's diagnosis the retinopathy of prematurity (ROP) disease which may occurred on preterm infant,. The optic disc detection is one of the fundamental step which could help to create an automate diagnose system for the doctors we use a new kind of active contour (snake) method has been developed by Chenyang et. al. [1], based on a new type of external force field, called gradient vector flow, or GVF. GVF is computed as a diffusion of the gradient vectors of a gray-level or binary edge map derived from the image. The testing results on a set of infant retinal ROP images verify the effectiveness of the proposed methods. We show that GVF has a large capture range and it's able to move snakes into boundary concavities of optic disc and finally the optic disk boundary was determined.

• Journal of Biomedical Engineering Research
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• v.28 no.6
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• pp.713-720
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• 2007

In image-guided surgery, automatic bone segmentation of Computed Tomography (CT) images is an important but challenging step. Previous attempts include intensity-, edge-, region-, and deformable curve-based approaches [1], but none claims fully satisfactory performance. Although active contour (AC) techniques possess many excellent characteristics, their applications in CT image segmentation have not worthily exploited yet. In this study, we have evaluated the automaticity and performance of the model of Chan-Vese Multiphase AC Without Edges towards knee bone segmentation from CT images. This model is suitable because it is initialization-insensitive and topology-adaptive. Its segmentation results have been qualitatively compared with those from four other widely used AC models: namely Gradient Vector Flow (GVF) AC, Geometric AC, Geodesic AC, and GVF Fast Geometric AC. To quantitatively evaluate its performance, the results from a commercial software and a medical expert have been used. The evaluation results show that the Chan-Vese model provides superior performance with least user interaction, proving its suitability for automatic bone segmentation from CT images.

• Journal of Electrical Engineering and Technology
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• v.10 no.2
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• pp.670-675
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• 2015

Medical imaging is one of the most powerful tools for gaining information about internal organs and tissues. It is a challenging task to develop sophisticated image analysis methods in order to improve the accuracy of diagnosis. The objective of this paper is to develop a Computer Aided Diagnostics (CAD) scheme for Brain Tumour detection from Magnetic Resonance Image (MRI) using active contour models and to investigate with several approaches for improving CAD performances. The problem in clinical medicine is the automatic detection of brain Tumours with maximum accuracy and in less time. This work involves the following steps: i) Segmentation performed by Fuzzy Clustering with Level Set Method (FCMLSM) and performance is compared with snake models based on Balloon force and Gradient Vector Force (GVF), Distance Regularized Level Set Method (DRLSE). ii) Feature extraction done by Shape and Texture based features. iii) Brain Tumour detection performed by various tree classifiers. Based on investigation FCMLSM is well suited segmentation method and Random Forest is the most optimum classifier for this problem. This method gives accuracy of 97% and with minimum classification error. The time taken to detect Tumour is approximately 2 mins for an examination (30 slices).

• Proceedings of the KIEE Conference
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• 1993.11a
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• pp.169-171
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• 1993

In order to estimate space charging process in the corona charging apparatus which has been used to make polymer electret cell, the electrical properties of 30[${\mu}m$] thick polypropylene film were obtained from TSC measurement after corona charging between copper knife electrode and aluminum cylinder electrode with the voltage of -8, -7, -6, -5 (kV). And, the electrostatic contour and the electric field vector were calculated using Finite Element Method with the electrical properties obtained from TSC spectra analysis. The edge effect around the edge of knife electrode affects electrostatic contour on the surface of specimen and the electric field concentration inside the polymer. As a result the uneven charging state in the electret cell due to the mistake of design was calculated, and the optimal design of corona charging apparatus opprobriate to various specimen was come to be practicable.