• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 1998년도 가을 학술발표회 논문집
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• pp.69-76
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• 1998

Due to the drastic improvement of computer hardware and operating system, it is easy to break through the main defects of limited computer memory and processing time, etc. To keep up with this situation, this paper is focused on developing the preprocessor program with the input method based on vector graphic editor and the preprocessing technique including automatic node generation algorithm for the $\rho$-version finite element program. To develop this preprocessor program, the special data structure and the OOP(Object Oriented Programming) have been used by the Visual Basic 4.0. The Special data structure is proposed to describe the geometric data of node numberings and coordinates suitable for the $\rho$-version finite element program, which are quite different from the comvential h-version finite element program.

• 대한산업공학회지
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• 제17권2호
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• pp.27-37
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• 1991

A recognition algorithm for Hangul is developed by structural analysis to Hangul in this theses. Four major procedures are proposed : preprocessing, type classification, separation of consonant and vowel, recognition. In the preprocessing procedure, the thinning algorithm proposed by CHEN & HSU is applied. In the type classification procedure, thinned Hangul image is classified into one of six formal types. In the separation of consonant and vowel procedure, starting from branch-points which are existed in a vowel, character elements are separated by means of tracing branch-point pixel by pixel and comparison with proposed templates. In the same time, the vowels are recognized. In the recognition procedure, consonants are extracted from the separated Hangul character and recognized by modified Crossing method. Recognized characters are converted into KS-5601-1989 codes. The experiments show that correct recognition rate is about 80%-90% and recognition speed is about 2-3 character persecond in three types of different input data on computer with 80386 microprocessor.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 하계학술대회 논문집 B
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• pp.959-961
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• 1995

The objective of most image proccessing applications is to extract meaningful information from one or more pictures. It is accomplished efficiently using neural networks, which is used in image classification and image recognition. In neural networks, background and meaningful information are processed with same weight in input layer. In this paper, we propose the image classification method using neural networks, especially EBP(Error Back Propagation). Preprocessing is needed. In preprocessing, background is compressed and meaningful information is emphasized. We use the quadtree approach, which is a hierarchical data structure based on a regular decomposition of space.

• 융합신호처리학회논문지
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• 제7권2호
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• pp.73-80
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• 2006

지문에 기반을 둔 인식시스템은 오래 전부터 사용되었다. 지문은 이미 잘 알려진 바와 마찬가지로 개개인이 서로 다른 특징을 가지고 있기 때문에, 가장 널리 사용되는 생체계측적인 특징의 하나이다. 그러나 지문 인식 시스템은 입력 지문 영상의 상태가 나쁜 경우 인식 성능이 크게 저하되는 치명적인 약점이 있다. 본 논문에서는 이런 문제점을 해결하기 위해 향상된 방향과 향상된 이진화 및 세선화 영상을 이용한 영상 향상 알고리즘을 전처리 과정에서 사용한다. 영상 향상의 목적은 입력 지문 이미지의 품질을 정확히 측정하고, 지문 영상의 융선과 골의 구조를 개선시키는 것이다. 또한 인식 속도를 향상시키기 위하여 색인 테이블을 사용한 융선의 방향 정보 추출 방법을 제안하였다. 제안한 지문 인식 시스템이 특징점 추출과 인식 성능에서 향상되었음을 실험을 통해서 확인할 수 있었다.

• Journal of Electrical Engineering and Technology
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• 제12권6호
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• pp.2388-2398
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• 2017

In this study, we propose a robust face recognition system to pose variations based on automatic pose estimation. Radial basis function neural network is applied as one of the functional components of the overall face recognition system. The proposed system consists of preprocessing and recognition modules to provide a solution to pose variation and high-dimensional pattern recognition problems. In the preprocessing part, principal component analysis (PCA) and 2-dimensional 2-directional PCA ($(2D)^2$ PCA) are applied. These functional modules are useful in reducing dimensionality of the feature space. The proposed RBFNNs architecture consists of three functional modules such as condition, conclusion and inference phase realized in terms of fuzzy "if-then" rules. In the condition phase of fuzzy rules, the input space is partitioned with the use of fuzzy clustering realized by the Fuzzy C-Means (FCM) algorithm. In conclusion phase of rules, the connections (weights) are realized through four types of polynomials such as constant, linear, quadratic and modified quadratic. The coefficients of the RBFNNs model are obtained by fuzzy inference method constituting the inference phase of fuzzy rules. The essential design parameters (such as the number of nodes, and fuzzification coefficient) of the networks are optimized with the aid of Particle Swarm Optimization (PSO). Experimental results completed on standard face database -Honda/UCSD, Cambridge Head pose, and IC&CI databases demonstrate the effectiveness and efficiency of face recognition system compared with other studies.

• 방송공학회논문지
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• 제24권2호
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• pp.315-328
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• 2019

영상 스테그아날리시스는 입력 영상을 스테가노그래피 알고리즘이 적용된 스테고 영상과 스테가노그래피 알고리즘이 적용되지 않은 커버 영상으로 분류하는 알고리즘이다. 기존에는 주로 수제 특징 기반의 스테그아날리시스를 연구하였다. 하지만 CNN 기반의 물체 인식이 큰 성과를 이루면서 최근 CNN 기반의 스테그아날리시스가 활발히 연구되고 있다. CNN 기반의 스테그아날리시스는 물체 인식과는 달리 커버 영상과 스테고 영상의 미세한 차이를 식별하기 위해서 전처리 필터를 필요로 한다. 그러므로, CNN 기반의 스테그아날리시스 연구들은 효과적인 전처리 필터와 네트워크 구조를 개발하는 데 초점을 두고 있다. 본 논문에서는 동일한 실험 조건에서 기존 연구들을 비교하고, 그 결과를 기반으로 전처리 필터와 네트워크 구조적인 차이에 의한 성능 변화를 분석한다.

• 전자공학회논문지B
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• 제33B권6호
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• pp.198-207
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• 1996

A PSRI (position, scale, and rotation invariant ) feature extraction and automatic target recognition system using a cooperative network and an MLP is proposed. We can extract position invarient features by obtaining the target center using the projection and the moment in preprocessing stage. The scale and rotation invariant features are extracted from the contour projection of the number of edge pixels on each of the concentric circles, which is input to the cooperative network. By extracting the representative PSRI features form the features and their differentiations using max-net and min-net, we can rdduce the number of input neurons of the MLP, and make the resulted automatic target recognition system less sensitive to input variances. Experiments are conduted on various complex images which are shifted, rotated, or scaled, and the results show that the proposed system is very efficient for PSRI feature extractions and automatic target recognitions.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 1994년도 봄 학술발표회 논문집
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• pp.25-32
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• 1994

The pre-processor for finite to element structural analysis considering the user-friendly device is developed by using GUI. This can be used on WINDOWS' environment which is realized the multi-tasking and the concurrency by object-oriented paradigm. Data input can be done easily through menu, dialog box, automatic stepwise input and concurrent representation with the structural geometry on multiple windows. It in designed to control integratedly the pre-processing, execution and the post-processing of the finite element structural analysis program on multiple windows, and input data can be seen with result outputs at the same time. In addition, the object-oriented programming environment makes convenient revision and addition of the program components for expanding the scope of analysis and making better user environment.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2002년도 춘계종합학술대회
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• pp.134-138
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• 2002

The human faces do not have distinct features unlike other general objects. In general the features of eyes, nose and mouth which are first recognized when human being see the face are defined. These features have different characteristics depending on different human face. In this paper, We propose a face recognition algorithm using the hidden Markov model(HMM). In the preprocessing stage, we find edges of a face using the locally adaptive threshold scheme and extract features based on generic knowledge of a face, then construct a database with extracted features. In training stage, we generate HMM parameters for each person by using the forward-backward algorithm. In the recognition stage, we apply probability values calculated by the HMM to input data. Then the input face is recognized by the euclidean distance of face feature vector and the cross-correlation between the input image and the database image. Computer simulation shows that the proposed HMM algorithm gives higher recognition rate compared with conventional face recognition algorithms.

• 한국군사과학기술학회지
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• 제10권2호
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• pp.21-30
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• 2007

A practical method to design the input shaping which generates control command is proposed in this paper, We suggest an experimental technique considering human operator's target tracking error to improve aiming accuracy which significantly affects hit probability. It is known that stabilization performance is one of the most important factors for ground combat vehicle system. In particular, stabilization error of the manual target tracking system mounted on moving vehicle directly affects hit probability. To reduce this error, we applied input command shaping method using preprocessing filtering and functional curve fitting. First of all, we construct the human operator model to consider effects of human operator on our system. Input shaping curve is divided into several regions to get rid of the above problems and to improve the system performance. At example design part, we chose three steps of functional command curve and determine the parameters of the function by the proposed design method. In order to verify the proposed design method, we carried out the experiments with real plant of a fighting vehicle.