• 대한전자공학회:학술대회논문집
• /
• 대한전자공학회 2003년도 하계종합학술대회 논문집 Ⅳ
• /
• pp.2307-2310
• /
• 2003

In this paper, a method for face recognition based on the wavelet packet decomposition is proposed. In the proposed method, the input image is decomposed by the 2-level wavelet packet transformation and then the face areas are defined by the Integral Projection technique applied to each of the 1-level subband images, HL and LH. After the defined face areas are divided into three areas, called top, bottom, and border, the mean and the variance of the three areas of the approximation image are computed, and the variance of the single predetermined face area for the rest of 15 detail images, from which the feature vectors of statistical measure are extracted. In this paper we use the wavelet packet decomposition, a generalization of the classical wavelet decomposition, to obtain its richer signal analysis features such as discontinuity in higher derivatives, self-similarity, etc. And we have shown that even with very simple statistical features such as mean values and variance we can make an excellent basis for face classification, if an appropriate probability distance is used.

• 한국음향학회지
• /
• 제24권5호
• /
• pp.262-270
• /
• 2005

본 논문에서는 음성 신호를 시간축으로 분할하는 새로운 기법으로, 분할 시 왜곡과 엔트로피가 함께 고려된 기법이 제안되었다 시간축 분할에 필요한 보간 함수와 타겟 특징 벡터는 동적 프로그래밍 기법을 이용하여 왜곡과 엔트로피가 동시에 최소화되도록 얻어진다. 보간 함수는 학습 데이터를 이용하여 구성되도록 하였으며, 분할과 추정의 반복적인 수행에 의해 왜곡과 엔트로피가 지역적으로 최소화 되는 지점에서 설계되도록 하였다. 모의 실험에서 제안된 시간축 분할 기법은 현존 음성 부호화 기법에 널리 사용되고 있는 분할 벡터 양자화 기법과 비교하여, 왜곡-비트율 특성 관점에서 보다 우수한 성능을 나타내었으며, 주관적인 청취 테스트 결과, 음질적인 면에서도 기존의 벡터 양자화 기법에 비해 우수한 방법임을 알 수 있었다.

• 인터넷정보학회논문지
• /
• 제25권1호
• /
• pp.49-56
• /
• 2024

There have been emerging many use-cases applying recommendation systems especially in online platform. Although the performance of recommendation systems is affected by a variety of factors, selecting appropriate features is difficult since most of recommendation systems have sparse data. Conventional matrix factorization (MF) method is a basic way to handle with problems in the recommendation systems. However, the MF based scheme cannot reflect non-linearity characteristics well. As deep learning technology has been attracted widely, a deep neural network (DNN) framework based collaborative filtering (CF) was introduced to complement the non-linearity issue. However, there is still a problem related to feature embedding for use as input to the DNN. In this paper, we propose an effective method using singular value decomposition (SVD) based feature embedding for improving the DNN performance of recommendation algorithms. We evaluate the performance of recommendation systems using MovieLens dataset and show the proposed scheme outperforms the existing methods. Moreover, we analyze the performance according to the number of latent features in the proposed algorithm. We expect that the proposed scheme can be applied to the generalized recommendation systems.

• 제어로봇시스템학회논문지
• /
• 제16권6호
• /
• pp.517-523
• /
• 2010

In this paper, we proposed an algorithm to detect aggressive driving status by analysing six kinds of driving patterns, which was achieved by comparing for the feature vectors using mahalanobis distance. The first step is to construct feature matrix of $6{\times}2$ size using frequency response of the time-series accelerometer data. Singular value decomposition makes it possible to find the dominant eigenvalue and its corresponding eigenvector. We use the eigenvector as the feature vector of the driving pattern. We conducted real experiments using three drivers to see the effects of recognition. Although there exists differences from individual drivers, we showed that driving patterns can be recognized with about 80% accuracy. Further research topics will include the development of aggressive driving warning system by improving the proposed technique and combining with post-processing of accelerometer signals.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제6권9호
• /
• pp.2424-2441
• /
• 2012

In this paper, we propose a fake iris detection method that combines the optical and textural features of the human eye. To extract the optical features, we used dual Purkinje images that were generated on the anterior cornea and the posterior lens surfaces based on an analytic model of the human eye's optical structure. To extract the textural features, we measured the amount of change in a given iris pattern (based on wavelet decomposition) with regard to the direction of illumination. This method performs the following two procedures over previous researches. First, in order to obtain the optical and textural features simultaneously, we used five illuminators. Second, in order to improve fake iris detection performance, we used a SVM (Support Vector Machine) to combine the optical and textural features. Through combining the features, problems of single feature based previous works could be solved. Experimental results showed that the EER (Equal Error Rate) was 0.133%.

• Journal of Information Processing Systems
• /
• 제13권6호
• /
• pp.1628-1639
• /
• 2017

The combination texture feature extraction approach for texture image retrieval is proposed in this paper. Two kinds of low level texture features were combined in the approach. One of them was extracted from singular value decomposition (SVD) based dual-tree complex wavelet transform (DTCWT) coefficients, and the other one was extracted from multi-scale local binary patterns (LBPs). The fusion features of SVD based multi-directional wavelet features and multi-scale LBP features have short dimensions of feature vector. The comparing experiments are conducted on Brodatz and Vistex datasets. According to the experimental results, the proposed method has a relatively better performance in aspect of retrieval accuracy and time complexity upon the existing methods.

• 대한기계학회논문집A
• /
• 제24권9호
• /
• pp.2235-2245
• /
• 2000

The necessity of using rapid prototyping(RP) for short-run manufacturing is continuously driving a development of a cost-effective technique that will produce completely-finished quality parts in a very short time. To meet these demands, the improvements in production speed, accuracy, materials, aid cost are crucial. Thus, a new hybrid-RP system performing both deposition and machining in a station is proposed. For the new hybrid RP process to maintain the same degree of process automation as in currently available processes like SLA or FDNI, a sophisticated process planning system is developed. In the process planner, CAD models(STEP AP203) are partitioned into 3D manufacturable volumes called 'Ueposition feature segment"(DFS) after machining features called "machining feature segmenf'(MFS) are extracted from the initial CAD model. Once MFS and DFS are identified, the process planner arranges them into a chain of processes and automatically generates machining information for each DFS and MFS. The goal of this paper is to present a framework for a process planning system for hybrid RP processes and to outline the geometric algorithms involved in developing such an environment.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제15권6호
• /
• pp.2098-2114
• /
• 2021

Light intensity variation is one of the key factors which affect the accuracy of vehicle face re-identification, so in order to improve the robustness of vehicle face features to light intensity variation, a Nonnegative Matrix Factorization model with the constraint of image acquisition time difference is proposed. First, the original features vectors of all pairs of positive samples which are used for training are placed in two original feature matrices respectively, where the same columns of the two matrices represent the same vehicle; Then, the new features obtained after decomposition are divided into stable and variable features proportionally, where the constraints of intra-class similarity and inter-class difference are imposed on the stable feature, and the constraint of image acquisition time difference is imposed on the variable feature; At last, vehicle face matching is achieved through calculating the cosine distance of stable features. Experimental results show that the average False Reject Rate and the average False Accept Rate of the proposed algorithm can be reduced to 0.14 and 0.11 respectively on five different datasets, and even sometimes under the large difference of light intensities, the vehicle face image can be still recognized accurately, which verifies that the extracted features have good robustness to light variation.

• 한국멀티미디어학회논문지
• /
• 제19권2호
• /
• pp.128-137
• /
• 2016

In mobile communication, air conduction(AC) speech signal had been commonly used, but it was easily affected by ambient noise environment such as emergency, military action and rescue. To overcome the weakness of the AC speech signal, bone conduction(BC) speech signal have been used. The BC speech signal is transmitted through bone vibration, so it is affected less by the background noise. In this paper, we proposed noise cancellation algorithm of the BC speech signal using noise feature of decomposed bands. The proposed algorithm consist of three steps. First, the BC speech signal is divided into 17 bands using perceptual wavelet packet decomposition. Second, threshold is calculated by noise feature during short time of separated-band and compared to absolute average of the signal frame. Therefore, the speech and noise parts are detected. Last, the detected noise parts are removed and then, noise eliminated bands are re-synthesised. In order to confirm the efficiency of the proposed algorithm, we compared the proposed algorithm with conventional algorithm. And the proposed algorithm has better performance than the conventional algorithm.

• 정보처리학회논문지D
• /
• 제13D권2호
• /
• pp.279-286
• /
• 2006

인터넷 쇼핑몰에서의 상품 추천을 위해 널리 사용되는 방식 중 한 가지는 상품의 특성과 고객의 특성을 비교하여 고객에 맞는 상품을 추천하는 방식이다. 이 방식은 상품이나 고객의 특성을 표현하는 자질(Feature)의 개수가 많을수록 그 중에 어떤 자질을 선택해야 더 좋은 추천 성과를 가져올 수 있는지 파악해 내는 것이 추천의 효과 및 효율성 측면에서 중요하지만 아직까지 충분히 연구되지 않은 실정이다. 본 연구에서는 인터넷 서점에서의 가상 구매실험을 바탕으로 사용자가 구매한 책 들에서 사용자를 잘 나타낼 수 있는 자질을 선택하는 방식에 대해서 벡터 스페이스 모형, TFIDF(Term Frequency-Inverse Document Frequency), Mutual Information, SVD(Singular Value Decomposition) 방식 등을 활용하여 실험하고 그 결과를 비교해본다. 실험 결과 SVD를 응용한 자질 추출 기법이 가장 좋은 성능을 나타내었다.