• Journal of KIISE:Software and Applications
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• v.35 no.12
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• pp.789-799
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• 2008

For the communication between human and computer in an interactive computing environment, the gesture recognition has been studied vigorously. The algorithms which use the 2D features for the feature extraction and the feature comparison are faster, but there are some environmental limitations for the accurate recognition. The algorithms which use the 2.5D features provide higher accuracy than 2D features, but these are influenced by rotation of objects. And the algorithms which use the 3D features are slow for the recognition, because these algorithms need the 3d object reconstruction as the preprocessing for the feature extraction. In this paper, we propose a method to extract the 3D features combined with the 3D object reconstruction in real-time. This method generates three kinds of 3D projection maps using the modified GPU-based visual hull generation algorithm. This process only executes data generation parts only for the gesture recognition and calculates the Hu-moment which is corresponding to each projection map. In the section of experimental results, we compare the computational time of the proposed method with the previous methods. And the result shows that the proposed method can apply to real time gesture recognition environment.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10b
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• pp.748-750
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• 2004

본 논문은 연속적으로 입력되는 2차원 얼굴 영상에서 얼굴의 특징 영역들을 추출하여 3차원 얼굴 모델의 표정을 실시간으로 제어하는 방법에 관한 연구이다. 2차원 얼굴 영상에서 얼굴을 추출하기 위해 Hue, Saturation 색상 값을 사용하며, 두 가지 색상 값을 이용하여 피부색과 배경색을 분리함으로써 얼굴 영역을 추출 할 수 있다. 추출 된 얼굴에서 특징 영역인 눈 코, 입술 영역 등의 일지를 각각의 영역에 적합한 추출 방법을 이용하여 추출한 뒤, 프레임 별로 영역들의 움직임을 비교함으로써 영역의 움직임 정보를 획득 할 수 있다. 이 정보를 3차원 얼굴 모델에 적용하여 2차원 동영상에서 획득된 대상의 얼굴의 표정을 3차원 얼굴 모델에 실시간으로 표현 할 수 있도록 한다.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06a
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• pp.301-302
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• 2008

• Journal of Korea Multimedia Society
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• v.11 no.11
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• pp.1501-1514
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• 2008

The face shape extracted by the depth values has different appearance as the most important facial information. The face images decomposed into frequency subband are signified personal features in detail. In this paper, we develop a method for recognizing the range face images by multiple frequency domains for each depth image using the modified fuzzy c-mean algorithm. For the proposed approach, the first step tries to find the nose tip that has a protrusion shape on the face from the extracted face area. And the second step takes into consideration of the orientated frontal posture to normalize. Multiple contour line areas which have a different shape for each person are extracted by the depth threshold values from the reference point, nose tip. And then, the frequency component extracted from the wavelet subband can be adopted as feature information for the authentication problems. The third step of approach concerns the application of eigenface to reduce the dimension. And the linear discriminant analysis (LDA) method to improve the classification ability between the similar features is adapted. In the last step, the individual classifiers using the modified fuzzy c-mean method based on the K-NN to initialize the membership degree is explained for extracted coefficient at each resolution level. In the experimental results, using the depth threshold value 60 (DT60) showed the highest recognition rate among the extracted regions, and the proposed classification method achieved 98.3% recognition rate, incase of fuzzy cluster.

• Journal of Korea Multimedia Society
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• v.11 no.4
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• pp.423-433
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• 2008

The surface curvatures extracted from the face contain the most important personal facial information. In particular, the face shape using the depth information represents personal features in detail. In this paper, we develop a method for recognizing the range face images by combining the multiple face regions using fuzzy integral. For the proposed approach, the first step tries to find the nose tip that has a protrusion shape on the face from the extracted face area and has to take into consideration of the orientated frontal posture to normalize. Multiple areas are extracted by the depth threshold values from reference point, nose tip. And then, we calculate the curvature features: principal curvature, gaussian curvature, and mean curvature for each region. The second step of approach concerns the application of eigenface and Linear Discriminant Analysis(LDA) method to reduce the dimension and classify. In the last step, the aggregation of the individual classifiers using the fuzzy integral is explained for each region. In the experimental results, using the depth threshold value 40 (DT40) show the highest recognition rate among the regions, and the maximum curvature achieves 98% recognition rate, incase of fuzzy integral.

• Journal of KIISE:Software and Applications
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• v.35 no.10
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• pp.616-626
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• 2008

The face shape extracted by the depth values has different appearance as the most important facial feature information and the face images decomposed into frequency subband are signified personal features in detail. In this paper, we develop a method for recognizing the range face images by combining the multiple frequency domains for each depth image and depth fusion using fuzzy integral. For the proposed approach, the first step tries to find the nose tip that has a protrusion shape on the face from the extracted face area. It is used as the reference point to normalize for orientated facial pose and extract multiple areas by the depth threshold values. In the second step, we adopt as features for the authentication problem the wavelet coefficient extracted from some wavelet subband to use feature information. The third step of approach concerns the application of eigenface and Linear Discriminant Analysis (LDA) method to reduce the dimension and classify. In the last step, the aggregation of the individual classifiers using the fuzzy integral is explained for extracted coefficient at each resolution level. In the experimental results, using the depth threshold value 60 (DT60) show the highest recognition rate among the regions, and the depth fusion method achieves 98.6% recognition rate, incase of fuzzy integral.

• Proceedings of the Korean Society of Postharvest Science and Technology of Agricultural Products Conference
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• 2003.10a
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• pp.143.1-143
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• 2003

본 논문은 브랜드 쌀 구매에 영향을 미치는 요인(factor)들이 소비자 특성에 따라 차이가 있는지를 분석하기 위한 목적으로 수행되었다. 분석을 위하여 서울시에 1,000명의 주부를 대상으로 설문조사를 실시하였으며, 그 주요 내용은 다음과 같다. 먼저 주부들이 쌀 구매에 영향을 미치는 11개 항목을 5점 리커르트 척도를 이용하여 분석하였다. 그 결과 소비자들이 가장 높은 점수를 부여하는 것은 쌀의 잔류 농약 정도로서 4.12점이 나왔으며, 영양가 높은 쌀(4.01점), 쌀알의 모양(3.96점) 및 쌀의 생산지(3.88점) 등의 순위가 높았다. 요인분석을 위해 고유치(eigen value) 값이 0.8 이상인 경우를 기준으로 요인 수를 분석한 결과 다섯가지의 요인이 추출되었으며, 그 특징은 유형적 차원과 무형적 차원으로 구분되었다. 전자인 유형적차원은 물리적 속성(완전미 여부, 도정일자, 잔류농약), 지역성(생산지), 외관성(크기, 모양) 및 가격성등의 가시적 차원으로 소비자가 쉽게 판단하거나 쌀간의 비교가 용이한 특성이며, 후자는 심리적 속성이나 상징성을 의미하는 것으로서 브랜드 상표와 품질 인증 마크가 가지는 신뢰성 등이 영향을 미치는 것으로 분석되었다. 무형적 차원인 식품의 안전성이 도시주부간 쌀 구매에 미치는 차별성을 알아보기 위하여 정규성 검정 결과에 따라 독립표본 T-검정을 실시하였다. 분석에서 도시주부가 특성별로 두 집단으로 구분되는 기준은 자의적 분류보다는 공통적인 기준을 이용하고자 평균값(나이, 학력, 소득, 동거가족 수)을 기준으로, 그 외의 경우는 더미변수(주부직업 유무, 주거형태 및 거주지역)를 이용하여 구분하였다. 식품의 안전성으로 추출된 요인 값의 평균에 대해 두 집단간 차이를 검정하였으며, 분석 결과 주거형태가 1％ 유의수준에서, 주부취업 여부 및 거주지역이 각각 5%유의수준에서 통계적 유의성이 있는 것으로 분석되었다. 반면 주부간 나이, 학력, 소득수준 및 동거가족 수에서는 유의미한 결과가 추정되지 못하였다. 본 연구결과를 볼 때, 식품의 안전성을 고려한 쌀의 마케팅 전략은 취업주부, 아파트 거주자 및 강남지역 주부들을 대상으로 한 판매활동 강화가 필요하다. 아울러 통계적으로 유의미한 결과를 나타내는 도시 주부의 특성 변수들을 세밀하게 구분해서 연구·분석하는 시장세분화 연구가 필요하다.

• Journal of KIISE:Software and Applications
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• v.35 no.12
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• pp.780-788
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• 2008

Modeling hand poses and tracking its movement are one of the challenging problems in computer vision. There are two typical approaches for the reconstruction of hand poses in 3D, depending on the number of cameras from which images are captured. One is to capture images from multiple cameras or a stereo camera. The other is to capture images from a single camera. The former approach is relatively limited, because of the environmental constraints for setting up multiple cameras. In this paper we propose a method of reconstructing 3D hand poses from a 2D input image sequence captured from a single camera by means of Belief Propagation in a graphical model and recognizing a finger clicking motion using a hidden Markov model. We define a graphical model with hidden nodes representing joints of a hand, and observable nodes with the features extracted from a 2D input image sequence. To track hand poses in 3D, we use a Belief Propagation algorithm, which provides a robust and unified framework for inference in a graphical model. From the estimated 3D hand pose we extract the information for each finger's motion, which is then fed into a hidden Markov model. To recognize natural finger actions, we consider the movements of all the fingers to recognize a single finger's action. We applied the proposed method to a virtual keypad system and the result showed a high recognition rate of 94.66% with 300 test data.

• Food Science and Preservation
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• v.11 no.1
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• pp.122-125
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• 2004

Factor analysis is estimated using housewives's intention data in Seoul province. Rice consumers considered safety, high nutritive value, shape and cultivation region as important factors in buying rice. The factors of food safety are brand and quality certification mark. The differences of two housewife groups on safety factor are caused by residence type, employment existence and residence region. The marketing strategies for rice containing food safety target housewives living in apartment, Gang Nam region. Also selling extension for target consumers housewives should be employed.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.5
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• pp.141-147
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• 2021

In this paper, in order to improve the utilization of hyperspectral large-capacity data feature information by reducing complex computations by dimension reduction of neural network inputs in embedded systems, the band selection algorithm is applied in each subset. Among feature extraction and feature selection techniques, the feature selection aim to improve the optimal number of bands suitable for datasets, regardless of wavelength range, and the time and performance, more than others algorithms. Through this experiment, although the time required was reduced by 1/3 to 1/9 times compared to the others band selection technique, meaningful results were improved by more than 4% in terms of performance through the K-neighbor classifier. Although it is difficult to utilize real-time hyperspectral data analysis now, it has confirmed the possibility of improvement.