• 말소리와 음성과학
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• 제7권1호
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• pp.3-10
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• 2015

In this paper, different frequency scales in cepstral feature extraction are evaluated for the text-independent speaker recognition. To this end, mel-frequency cepstral coefficients (MFCCs), linear frequency cepstral coefficients (LFCCs), and bilinear warped frequency cepstral coefficients (BWFCCs) are applied to the speaker recognition experiment. In addition, the spectro-temporal features extracted by the cepstral-time matrix (CTM) are examined as an alternative to the delta and delta-delta features. Experiments on the NIST speaker recognition evaluation (SRE) 2004 task are carried out using the Gaussian mixture model-universal background model (GMM-UBM) method and the joint factor analysis (JFA) method, both based on the ALIZE 3.0 toolkit. Experimental results using both the methods show that BWFCC with appropriate warping factor yields better performance than MFCC and LFCC. It is also shown that the feature set including the spectro-temporal information based on the CTM outperforms the conventional feature set including the delta and delta-delta features.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권5호
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• pp.1856-1869
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• 2015

This paper addresses the issues of 3D human activity detection, tracking and recognition from RGB-D video sequences using a feature structured framework. During human tracking and activity recognition, initially, dense depth images are captured using depth camera. In order to track human silhouettes, we considered spatial/temporal continuity, constraints of human motion information and compute centroids of each activity based on chain coding mechanism and centroids point extraction. In body skin joints features, we estimate human body skin color to identify human body parts (i.e., head, hands, and feet) likely to extract joint points information. These joints points are further processed as feature extraction process including distance position features and centroid distance features. Lastly, self-organized maps are used to recognize different activities. Experimental results demonstrate that the proposed method is reliable and efficient in recognizing human poses at different realistic scenes. The proposed system should be applicable to different consumer application systems such as healthcare system, video surveillance system and indoor monitoring systems which track and recognize different activities of multiple users.

• 한국인터넷방송통신학회논문지
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• 제18권6호
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• pp.151-156
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• 2018

필기체 숫자인식은 일반적으로 높은 인식률과 문맥 독립이 요구되고 있고, 쓰는 사람에 따라서 많은 차이점이 있어서 자유 필기체 숫자는 인식이나 알고리즘작성에 아직도 어려운 문제점이 있다. 본 연구에서는, 필기체 숫자의 특성을 분석하고, 구조적 특징기반 자유 필기체 숫자인식 알고리즘을 새롭게 제안한다. 주어진 필기 숫자에 대하여, 끝점과 분기점, 수평선과 함께 숫자의 구조적 특징을 연구한다. 이 방법은 확장된 구조적 특징 알고리즘으로 제안되어 강인하며, 그리고 본 연구에서 제안한 구조적 특징에 기반 한 결정 트리(decision tree)는 필기체 숫자 자동인식방법에 구조적으로 기여한다. 본 알고리즘이 다른 방법과 비교하여 인식률과 강인성이 우수함을 실험결과로 보여주었다.

• Journal of Information Processing Systems
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• 제17권3호
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• pp.556-570
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• 2021

The existing video expression recognition methods mainly focus on the spatial feature extraction of video expression images, but tend to ignore the dynamic features of video sequences. To solve this problem, a multi-mode convolution neural network method is proposed to effectively improve the performance of facial expression recognition in video. Firstly, OpenFace 2.0 is used to detect face images in video, and two deep convolution neural networks are used to extract spatiotemporal expression features. Furthermore, spatial convolution neural network is used to extract the spatial information features of each static expression image, and the dynamic information feature is extracted from the optical flow information of multiple expression images based on temporal convolution neural network. Then, the spatiotemporal features learned by the two deep convolution neural networks are fused by multiplication. Finally, the fused features are input into support vector machine to realize the facial expression classification. Experimental results show that the recognition accuracy of the proposed method can reach 64.57% and 60.89%, respectively on RML and Baum-ls datasets. It is better than that of other contrast methods.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1997년도 추계학술대회 학술발표 논문집
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• pp.311-314
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• 1997

This paper presents recognition of high impedance fault patterns based of chaotic features using the Radial Basis Function Network(RBFN). The chaos attractor is reconstructed from the fault current data for pattern recognition. The RBFN successfully classifies the three kinds of fault pattems and one normal pattem.

• 한국컴퓨터정보학회논문지
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• 제21권9호
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• pp.27-36
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• 2016

Recognition of grapheme is a very important process in the recognition within 'Hangul(Korean written language)' letters using phoneme recognition. It is because the success or failure in the recognition of phoneme greatly affects the recognition of letters. For this reason, it is reported that separation of phonemes is the biggest difficulty in the phoneme recognition study. The current study separates and suggests the new phonemes that used the connective elements that are helpful for dividing phonemes, recommends the features for recognition of such suggested phonemes, databases this, and carried out a set of experiments of recognizing phonemes using the suggested features. The current study used 350 letters in the experiment of phoneme separation and recognition. In this particular kind of letters, there were 1,125 phonemes suggested. In the phoneme separation experiment, the phonemes were divided in the rate of 100%, and the phoneme recognition experiment showed the recognition rate of 98% in recognizing only 14 phonemes into different ones.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1996년도 추계학술대회 학술발표 논문집
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• pp.294-297
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• 1996

This paper proposes the method to recognize of time series data based on the chaotic feature extraction. Features extract from time series data using the chaotic time series data analysis and the pattern recognition process is using a neural network classifier. In experiment, EEG(electroencephalograph) signals are extracted features by correlation dimension and Lyapunov experiments, and these features are classified by multilayer perceptron neural networks. Proposed chaotic feature extraction enhances recognition results from chaotic time series data.

• 대한산업공학회지
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• 제19권4호
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• pp.23-40
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• 1993

In this paper, we developed a prototype part recognition system for FMS (Flexible Manufacturing System) by comparing the features extracted from CAD data and the features obtained from the image through a single camera, Good or distinct features with high recognition efficiency for a specific part are obtained automatically and intelligently according to the rules from CAD data. Since the selection of distinct features are done automatically without any human interference, the developed system seems to be quite robust. Fourther, since it uses only a few distinct features on-line among many features and all the time-consuming calculations are done off-line, it is possible to recognize each part quickly and accurately. In order to evaluate the performance and the effectiveness of the developed system, two example(L and T shaped parts) are tested and the results are reported.

• 대한전자공학회논문지TE
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• 제39권1호
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• pp.37-45
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• 2002

본 논문은 차량계기판에서 벌브를 인식하기 위한 새로운 특징을 제안한다. 대부분의 모델기반 물체 인식에서 사용되는 특징으로는 물체의 다각형 근사점이 있다. 이러한 특징을 이용한 정합방식을 차량계기판의 벌브와 같은 작은 물체에 적용하며, 정합율이 낮다. 이러한 정합율을 높이기 위해서 본 논문에서는 새로운 특징을 제안한다. 제안된 특징은 물체화소의 원분포와 물체의 중심에서 경계선까지의 거리비이다. 본 논문에서는 이러한 세 개의 특징을 모두 같이 이용하기 위해서 새로운 결정함수를 정의한다. 실험 결과는 다각형 근사점을 이용한 정합방식과 3개의 특징을 모두 이용한 정합방식에서의 정합이 되지 않은 물체수로 비교를 한다.

• Journal of information and communication convergence engineering
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• 제6권1호
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• pp.94-99
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• 2008

Segmentation for the region of nucleus in the image of uterine cervical cytodiagnosis is known as the most difficult and important part in the automatic cervical cancer recognition system. In this paper, the region of nucleus is extracted from an image of uterine cervical cytodiagnosis using the HSI model. The characteristics of the nucleus are extracted from the analysis of morphemetric features, densitometric features, colormetric features, and textural features based on the detected region of nucleus area. The classification criterion of a nucleus is defined according to the standard categories of the Bethesda system. The fuzzy C-means clustering algorithm is employed to the extracted nucleus and the results show that the proposed method is efficient in nucleus recognition and uterine cervical Pap-Smears extraction.