• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권3호
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• pp.1464-1480
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• 2019

Robust tracking of infrared (IR) pedestrian targets with various backgrounds, e.g. appearance changes, illumination variations, and background disturbances, is a great challenge in the infrared image processing field. In the paper, we address a new tracking method for IR pedestrian targets via multi-feature local sparse representation (SR), which consists of three important modules. In the first module, a multi-feature local SR model is constructed. Considering the characterization of infrared pedestrian targets, the gray and edge features are first extracted from all target templates, and then fused into the model learning process. In the second module, an effective tracker is proposed via the learned model. To improve the computational efficiency, a sliding window mechanism with multiple scales is first used to scan the current frame to sample the target candidates. Then, the candidates are recognized via sparse reconstruction residual analysis. In the third module, an adaptive dictionary update approach is designed to further improve the tracking performance. The results demonstrate that our method outperforms several classical methods for infrared pedestrian tracking.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2002년도 추계학술대회 논문집
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• pp.404-406
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• 2002

In this paper, the method of image feature extraction is proposed. This method employ the energy field analysis, outlier removal algorithm and ring projection. Using this algorithm, we achieve rotation-translation-scale invariant feature extraction. The force field are exploited to automatically locate the extrema of a small number of potential energy wells and associated potential channels. The image feature is acquired from relationship of local extrema using the ring projection method.

• Journal of Computing Science and Engineering
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• 제11권4호
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• pp.121-129
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• 2017

Feature selection has been widely established as an efficient technique for microarray data analysis. Feature selection aims to search for the most important feature/gene subset of a given dataset according to its relevance to the current target. Unsupervised feature selection is considered to be challenging due to the lack of label information. In this paper, we propose a novel method for unsupervised feature selection, which incorporates embedded learning and $l_{2,1}-norm$ sparse regression into a framework to select genes in microarray data analysis. Local tangent space alignment is applied during embedded learning to preserve the local data structure. The $l_{2,1}-norm$ sparse regression acts as a constraint to aid in learning the gene weights correlatively, by which the proposed method optimizes for selecting the informative genes which better capture the interesting natural classes of samples. We provide an effective algorithm to solve the optimization problem in our method. Finally, to validate the efficacy of the proposed method, we evaluate the proposed method on real microarray gene expression datasets. The experimental results demonstrate that the proposed method obtains quite promising performance.

• 아태비즈니스연구
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• 제13권2호
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• pp.23-37
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• 2022

Purpose - The purpose of this paper is to analyze the dynamic factors of the daily FX implied volatility based on the fractional integration methods focusing on long memory feature and structural changes. Design/methodology/approach - This paper uses the daily FX implied volatility data of the EUR-USD and the JPY-USD exchange rates. For the fractional integration analysis, this paper first applies the basic ARFIMA-FIGARCH model and the Local Whittle method to explore the long memory feature in the implied volatility series. Then, this paper employs the Adaptive-ARFIMA-Adaptive-FIGARCH model with a flexible Fourier form to allow for the structural changes with the long memory feature in the implied volatility series. Findings - This paper finds statistical evidence of the long memory feature in the first two moments of the implied volatility series. And, this paper shows that the structural changes appear to be an important factor and that neglecting the structural changes may lead to an upward bias in the long memory feature of the implied volatility series. Research implications or Originality - The implied volatility has widely been believed to be the market's best forecast regarding the future volatility in FX markets, and modeling the evolution of the implied volatility is quite important as it has clear implications for the behavior of the exchange rates in FX markets. The Adaptive-ARFIMA-Adaptive-FIGARCH model could be an excellent description for the FX implied volatility series

• 한국정보과학회논문지:소프트웨어및응용
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• 제29권11호
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• pp.818-825
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• 2002

얼굴의 깊이 정보는 얼굴 인식에서 가장 중요한 요소이다. 3차원 얼굴 영상은 깊이 정보를 잘 나타내므로 얼굴의 깊이 값을 비교하는데 아주 유용하다. 얼굴 전체에 대한 처리는 많은 계산량과 데이터 량을 포함해야 하는 문제점이 있다. 따라서 본 논문에서는 얼굴의 국부적인 영역들에 대한 3차원 깊이 값을 이용하여 인식하였다. 3D 레이저 스캐너로 입력된 3차원 얼굴 영상으로부터 어떤 깊이에 있는 등고선 영역을 추출한 후, 이를 영역별로 취하면 국부적인 얼굴 깊이에 대한 특징을 잘 반영하게 된다. 얼굴의 가장 중심인 코를 기준점으로 깊이 영역에 대한 등고선 영역을 추출하며, 얼굴의 깊이를 고려한 국부적 깊이 정보를 다중 특징 벡터를 이용하여 얼굴을 인식한다. 다중 특징 벡터는 벡터 수가 적으면서 얼굴의 지역적 깊이 특성을 잘 나타내므로 간단한 방법으로 높은 인식률을 얻을 수 있었다.

• Journal of Electrical Engineering and Technology
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• 제12권1호
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• pp.402-410
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• 2017

For many years, researchers in face description area have been representing and recognizing faces based on different methods that include subspace discriminant analysis, statistical learning and non-statistics based approach etc. But still automatic face recognition remains an interesting but challenging problem. This paper presents a novel and efficient face image representation method based on Optimized Local Ternary Pattern (OLTP) texture features. The face image is divided into several regions from which the OLTP texture feature distributions are extracted and concatenated into a feature vector that can act as face descriptor. The recognition is performed using nearest neighbor classification method with Chi-square distance as a similarity measure. Extensive experimental results on Yale B, ORL and AR face databases show that OLTP consistently performs much better than other well recognized texture models for face recognition.

• 한국정보과학회논문지:소프트웨어및응용
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• 제31권12호
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• pp.1684-1690
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• 2004

본 논문은 시험 표본 주위의 영역에 대한 속성을 이용한 다중 인식기 선택 방법을 제안한다. 기존의 DCS-LA 동적 인식기 선택 방법은 시험 표본 주위의 학습표본들을 사용해서 각 인식기의 국부적 정확성을 계산하여 인식기를 동적으로 선택하기 때문에 인식 시간이 오래 걸린다. 본 논문에서는 특징공간에서 국부적인 속성을 계산해서 그 속성값에 적합한 인식기를 미리 선정해서 저장해 놓은 후 시험 표본이 들어오면 그 주변의 속성값에 따라 저장된 인식기에서 선택을 하기 때문에 인식시간을 줄일 수 있다. 국부적인 속성으로는 표본 주위의 작은 영역에 대한 엔트로피와 밀도를 계산하여 사용하였으며 이들을 특징공간속성(Feature Space Attribute)라고 하였다. 이들 두 속성으로 이루어지는 속성 공간을 규칙적인 사각형 셀로 나누어, 학습과정에서 각각의 학습표본에 대해 계산된 속성값이 어떤 셀에 속하는지를 구한다. 또한 각 셀에 속하는 학습표본들에 대해 각 인식기의 국부적 정확도를 구하여 셀에 저장한다. 시험 과정에서 시험표본에 대해 속성값 계산을 통해 그 표본이 속하는 셀을 구한 후 그 셀에서 국부적 정확도가 가장 높은 인식기로 인식한다. Elena 데이타베이스를 사용해서 기존의 방법과 제안된 방법을 비교하였다. 제안된 방법은 기존의 DCS-LA와 거의 같은 인식률을 나타내지만 인식속도는 약 4배 가까이 빨라짐을 실험을 통해 확인할 수 있었다.

• ETRI Journal
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• 제32권5호
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• pp.784-794
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• 2010

Automatic facial expression recognition has many potential applications in different areas of human computer interaction. However, they are not yet fully realized due to the lack of an effective facial feature descriptor. In this paper, we present a new appearance-based feature descriptor, the local directional pattern (LDP), to represent facial geometry and analyze its performance in expression recognition. An LDP feature is obtained by computing the edge response values in 8 directions at each pixel and encoding them into an 8 bit binary number using the relative strength of these edge responses. The LDP descriptor, a distribution of LDP codes within an image or image patch, is used to describe each expression image. The effectiveness of dimensionality reduction techniques, such as principal component analysis and AdaBoost, is also analyzed in terms of computational cost saving and classification accuracy. Two well-known machine learning methods, template matching and support vector machine, are used for classification using the Cohn-Kanade and Japanese female facial expression databases. Better classification accuracy shows the superiority of LDP descriptor against other appearance-based feature descriptors.

• 한국주거학회:학술대회논문집
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• 한국주거학회 2008년 추계학술발표대회 논문집
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• pp.385-390
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• 2008

The residence plan of Korea has been formed with bias for quantitative growth and uniformity failing at obtaining human value. Also, rapid growth brought about severe problems of deteriorated human life and destruction of environment. The solution for these problems is pursued in many directions, but there is short of active plan yet. The residence shall develop into new direction to satisfy the demand by reflecting society and culture as well as residents. This study examines external design with symbolic representation of regional feature as an alternative for uniform residence environment problem. This study will be a basic data upon suggesting the direction for planning high quality residence environment. This study examined the elements which form external space of residence complex, designed plan for external space of residence complex, and examined how to reflect regional feature which is important element of local community and culture on the space plan for residence complex based on the evaluation by local residents centered on Gimcheon-si, Gyeongbuk which just started local specialty by fostering. 'Special Area for Grape Industry.'

• 한국멀티미디어학회논문지
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• 제16권9호
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• pp.1057-1066
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• 2013

손가락 관절문(FKP, finger-knuckle-print)을 이용한 개인 인증은 손가락 관절부에 나타나는 주름의 특징을 이용하는 것으로, 텍스처의 방향 정보가 중요한 특징이 된다. 본 논문에서는 SIFT 알고리즘을 이용하여 특징점들을 추출하고, 벡터 유사도 정합을 통해 FKP를 효과적으로 인증할 수 있는 방법을 제안하다. 벡터는 질의 영상에서 추출한 특징점과 이에 대응되는 참조 영상의 특징점을 연결하는 방향 벡터로 정의된다. 국소적인 특징점 쌍으로부터 방향 벡터를 생성하기 때문에 방향 벡터 자체는 국소적인 특징만을 나타내지만, 두 영상 간에 존재하는 다른 벡터들 간의 유사도를 비교함으로써 전역적인 특징으로 확장되는 장점이 있다. 실험결과 제안하는 방법은 기존의 방향코드를 이용한 다양한 방식에 비하여 우수한 성능을 나타내었다.