• 대한산업공학회지
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• 제41권4호
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• pp.338-343
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• 2015

We suggest a method to configure feature functions of continuous conditional random field (C-CRF). Regression tree and similarity analysis are introduced to construct the first and second feature functions of C-CRF, respectively. Rules from the regression tree are transformed to logic functions. If a logic in the set of rules is true for a data then it returns the corresponding value of leaf node and zero, otherwise. We build an Euclidean similarity matrix to define neighborhood, which constitute the second feature function. Using two feature functions, we make a C-CRF model and an illustrate example is provided.

• 대한음성학회지:말소리
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• 제29_30호
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• pp.43-60
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• 1995

Standard generative phonologists assumed that there were no orders or hierarchies among distinctive features. This means that the distinctive features which make up a segment are independent and unordered. The unordered linear matrix cannot explain phonological phenomena such as complex segments as hierarchical representation does neatly. The hierarchical feature representation theory which embodies the concept of multi-tiered phonological representation organizes distinctive features in the appearance of hierarchical dominance. This paper aims to show how we can solve some problems of the linear feature representation. As regard underlying representation the theory of underspecification is discussed. I propose a feature hierarchy similar to that of Sagey(1986) but slightly different. I show English consonantal assimilation in feature hierarchical model compared with that of feature changing theory of linear representation.

• Fisheries and Aquatic Sciences
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• 제4권1호
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• pp.1-9
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• 2001

This paper describes a procedure extracting feature vector of a target cell more precisely in the case of identifying specified cell. The classification of object type is based on feature vector such as area, complexity, centroid, rotation angle, effective diameter, perimeter, width and height of the object So, the feature vector plays very important role in classifying objects. Because the feature vectors is affected by noises and holes, it is necessary to remove noises contaminated in original image to get feature vector extraction exactly. In this paper, we propose the following method to do to get feature vector extraction exactly. First, by Otsu's optimal threshold selection method and morphological filters such as cleaning, filling and opening filters, we separate objects from background an get rid of isolated particles. After the labeling step by 4-adjacent neighborhood, the labeled image is filtered by the area filter. From this area-filtered image, feature vector such as area, complexity, centroid, rotation angle, effective diameter, the perimeter based on chain code and the width and height based on rotation matrix are extracted. To prove the effectiveness, the proposed method is applied for yeast Zygosaccharomyces rouxn. It is also shown that the experimental results from the proposed method is more efficient in measuring feature vectors than from only Otsu's optimal threshold detection method.

• 대한전자공학회논문지SP
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• 제43권4호
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• pp.46-54
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• 2006

최근 각종 온라인 상거래 및 개인 신분카드 이용이 늘어나면서 개인 인증의 중요성이 부각되고 있다. RFID(Radio Frequency Identification) tag가 내장된 개인 신분 카드가 점차 증가하고 있지만, 본인의 인증을 할 수 있는 방법이 미비하기 때문에, 자동화 할 수 있는 대책이 시급하다. RFID tag는 현재 메모리 용량이 매우 작기 때문에, 개인의 생체정보를 저장하기 위해서는 효율적인 특징추출 방법이 필요하며, 저장된 특징들을 비교하기 위해서는 새로운 인식방법이 필요하다. 본 논문에서는 인간의 인지학적인 두뇌 원리인 해마 신경망을 공학적으로 모델링하여 얼굴 영상의 특징 벡터들을 고속 학습하고, 각 영상의 최적의 특정을 구성할 수 있는 해마 신경망 모델링 알고리즘을 이용한 개인생체 인증 시스템에 관한 연구를 수행하였다. 시스템은 크게 NMF(Non-negative Matrix Factorization)와 LDA(Linear Discriminants Analysis) 혼합 알고리즘을 이용한 특징 추출 부분과 해마신경망을 모델링하고 인식 성능을 실험하는 것으로 구성 되어 있다. 제안한 시스템의 성능을 평가하기 위하여 실험은 표정변화와 포즈변화가 포함된 이미지를 각각 구분하여 인식률을 확인하였다. 실험 결과, 본 논문에서 제안하는 특정 추출 방법과 학습 방법을 다른 방법들과 비교하였을 때, 학습시간비용과 인식률에서 우수함을 확인하였다.

• 대한공간정보학회지
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• 제8권1호
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• pp.105-110
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• 2000

고해상도 위성영상의 출현으로 인해 인간이 사용하는 다양한 판독기재를 영상판독에 적용할 가능성이 넓어졌고, 컴퓨터비전, 패턴인식, 인공지능, 원격탐사 등 많은 분야에서 이런 가능성들을 연구해왔다. 이중 질감은 '영상의 밝기와 색조간의 공간적 분포와 관계'된 양으로 영상판독에 중요한 역할을 한다. 특히 통계적 모델을 기초로 질감정보을 얻는 방법이 많이 연구되어 왔고, 이러한 기법 중 공기행렬을 이용하여 질감을 측정한 연구는 다른 기법에 비해 계산이 간편하며 위성영상과 항공사진에 적용되어 높은 분류정확도를 나타내어 좋은 질감측정치로 평가되었다 하지만 기존의 논문들에서 특성치의 선택에 관한 연구 없이 임의적으로 특성치가 선택되었고, 또한 공기행렬이 질감을 잘 표현할 수 있는 적정해상도에 판한 연구가 부족했다. 따라서 본 연구에서는 첫째, 질감측정의 방법으로 공기행렬의 개념을 소개하고, 둘째, 위성영상으로부터 도출된 공기행렬로부터 얻어질 수 있는 여러 가지 특성치들의 유용성을 평가하여 컴퓨터를 이용한 분류 시 적절한 특성치를 선정할 수 있는 근거를 마련하며, 셋째, 여러 특성치들의 공간해상도에 따른 변화추이를 조사하여 공기행렬이 적용될 수 있는 적정해상도를 제시하고자 한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권7호
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• pp.3194-3216
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• 2018

Slow Feature Discriminant Analysis (SFDA) is a supervised feature extraction method inspired by biological mechanism. In this paper, a novel method called Two Dimensional Slow Feature Discriminant Analysis via $L_{2,1}$ norm minimization ($2DSFDA-L_{2,1}$) is proposed. $2DSFDA-L_{2,1}$ integrates $L_{2,1}$ norm regularization and 2D statically uncorrelated constraint to extract discriminant feature. First, $L_{2,1}$ norm regularization can promote the projection matrix row-sparsity, which makes the feature selection and subspace learning simultaneously. Second, uncorrelated features of minimum redundancy are effective for classification. We define 2D statistically uncorrelated model that each row (or column) are independent. Third, we provide a feasible solution by transforming the proposed $L_{2,1}$ nonlinear model into a linear regression type. Additionally, $2DSFDA-L_{2,1}$ is extended to a bilateral projection version called $BSFDA-L_{2,1}$. The advantage of $BSFDA-L_{2,1}$ is that an image can be represented with much less coefficients. Experimental results on three face databases demonstrate that the proposed $2DSFDA-L_{2,1}/BSFDA-L_{2,1}$ can obtain competitive performance.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1994년도 추계학술대회 논문집
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• pp.559-564
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• 1994

Recently, a visual servoing for an eye-in-hand robot has become an interesting problem. A distance between a camera and a task object is very useful information for visual servoing. In the previous works for visual servoing, the distance can be obtained from the difference between a reference and a measured feature value of the object such as area on image plane. However, since this feature depends on the object, the reference feature value must be changed when other task object is taken. To overcome this difficulty, this paper presents a novel method for visual servoing. In the proposed method, a blur is used to obtain the distance. The blur, one of the most important features, depends on the focal length of camera. Since it is not affected by the change of object, the reference feature value is not changed although other task object is taken. In this paper, we show a relationship between the distance and the blur, and define the feature jacobian matrix based on camera defocusing to operate the robot. A series of experiments is performed to verify the proposed method.

• 한국정보통신학회논문지
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• 제18권9호
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• pp.2155-2160
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• 2014

본 논문은 하둡 기반의 분산병렬처리에 의한 문서의 의미특징을 추출하고, 추출된 의미특징을 이용하여 문서를 요약하는 새로운 방법을 제안한다. 제안된 방법은 문서요약에 비음수 분해된 문서의 의미특징을 이용함으로써 문서의 내부 구조를 잘 표현 할 수 있다. 또한 하둡을 이용하여 빅데이터의 문서를 요약할 수 있다. 실험결과 제안방법이 단일 컴퓨터 환경에서 처리할 수 없는 대용량의 문서를 요약할 수 있음을 보인다.

• Journal of Information Processing Systems
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• 제14권4호
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• pp.892-903
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• 2018

The paper proposes a novel gait recognition algorithm based on feature fusion of gait energy image (GEI) dynamic region and Gabor, which consists of four steps. First, the gait contour images are extracted through the object detection, binarization and morphological process. Secondly, features of GEI at different angles and Gabor features with multiple orientations are extracted from the dynamic part of GEI, respectively. Then averaging method is adopted to fuse features of GEI dynamic region with features of Gabor wavelets on feature layer and the feature space dimension is reduced by an improved Kernel Principal Component Analysis (KPCA). Finally, the vectors of feature fusion are input into the support vector machine (SVM) based on multi classification to realize the classification and recognition of gait. The primary contributions of the paper are: a novel gait recognition algorithm based on based on feature fusion of GEI and Gabor is proposed; an improved KPCA method is used to reduce the feature matrix dimension; a SVM is employed to identify the gait sequences. The experimental results suggest that the proposed algorithm yields over 90% of correct classification rate, which testify that the method can identify better different human gait and get better recognized effect than other existing algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권6호
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• pp.2806-2825
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• 2018

Unsupervised learning has shown good performance on image, video and audio classification tasks, and much progress has been made so far. It studies how systems can learn to represent particular input patterns in a way that reflects the statistical structure of the overall collection of input patterns. Many promising deep learning systems are commonly trained by the greedy layerwise unsupervised learning manner. The performance of these deep learning architectures benefits from the unsupervised learning ability to disentangling the abstractions and picking out the useful features. However, the existing unsupervised learning algorithms are often difficult to train partly because of the requirement of extensive hyperparameters. The tuning of these hyperparameters is a laborious task that requires expert knowledge, rules of thumb or extensive search. In this paper, we propose a simple and effective unsupervised feature learning algorithm for image classification, which exploits an explicit optimizing way for population and lifetime sparsity. Firstly, a sparse target matrix is built by the competitive rules. Then, the sparse features are optimized by means of minimizing the Euclidean norm ($L_2$) error between the sparse target and the competitive layer outputs. Finally, a classifier is trained using the obtained sparse features. Experimental results show that the proposed method achieves good performance for image classification, and provides discriminative features that generalize well.