• 한국멀티미디어학회논문지
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• 제6권6호
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• pp.968-976
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• 2003

본 논문은 입술형태를 인식하기 위한 방법을 제안하였다. 입술은 GLDM(Gray Level Distribution Model)을 기반으로 표현하였으며 입술인식은 모델에 대한 입력영상의 정확도에 대한 목적함수를 최적화하는 문제로 단순화하였다. 최적화를 위해 다운힐 심플렉스(Down Hill Simplex) 알고리즘을 이용하였으며 지역 최소점으로 수렴하는 문제를 해결하기 위한 새로운 방법을 제안하였다 제안한 방법으로 기존의 능동적 형태 모델(ASM Active Shape Model)에서 찾지 못하던 입술의 윤곽을 찾아낼 수 있음을 보였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권4호
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• pp.1748-1759
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• 2018

Weber Local Descriptor (WLD) is a stable and effective feature extraction algorithm, which is based on Weber's Law. It calculates the differential excitation information and direction information, and then integrates them to get the feature information of the image. However, WLD only considers the center pixel and its contrast with its surrounding pixels when calculating the differential excitation information. As a result, the illumination variation is relatively sensitive, and the selection of the neighbor area is rather small. This may make the whole information is divided into small pieces, thus, it is difficult to be recognized. In order to overcome this problem, this paper proposes Weber Symmetrical Local Graph Structure (WSLGS), which constructs the graph structure based on the $5{\times}5$ neighborhood. Then the information obtained is regarded as the differential excitation information. Finally, we demonstrate the effectiveness of our proposed method on the database of ORL, JAFFE and our own built database, high-definition infrared faces. The experimental results show that WSLGS provides higher recognition rate and shorter image processing time compared with traditional algorithms.

• 한국지능시스템학회논문지
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• 제18권5호
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• pp.617-623
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• 2008

본 논문에서는 중심이동과 국부기저영상을 이용한 동작인식 기법을 제안하였다. 여기서 중심이동은 1차 모멘트 평형에 기반을 둔 것으로 위치나 크기 변화에 강건한 동작영상을 얻기 위함이고, 국부기저영상의 추출은 독립성분분석 기법에 기반을 둔 것으로 각 동작들마다에 포함된 통계적으로 독립인 동작특징들의 집합을 얻기 위함이다. 특히 국부기저영상을 빠르게 추출하기 위해 뉴우턴(Newton)법의 고정점 알고리즘에 기반을 둔 독립성분분석을 이용하였다. 제안된 기법을 240*215 픽셀의 160(1명*10종류*16동작)개 동물표현의 수화 동작영상을 대상으로 city-block, Euclidean, 그리고 negative angle의 척도들을 분류척도로 이용하여 실험하였다. 실험결과, 제안된 기법은 국부고유영상을 이용한 방법과 중심이동을 거치지 않는 국부기저영상을 이용하는 기법보다 각각 우수한 인식성능이 있음을 확인하였다.

• 한국정보전자통신기술학회논문지
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• 제14권4호
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• pp.314-322
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• 2021

Skeleton-based action recognition has attracted considerable attention in human action recognition. Recent methods for skeleton-based action recognition employ spatiotemporal graph convolutional networks (GCNs) and have remarkable performance. However, most of them have heavy computational complexity for robust action recognition. To solve this problem, we propose a shuffle graph convolutional network (SGCN) which is a lightweight graph convolutional network using pointwise group convolution rather than pointwise convolution to reduce computational cost. Our SGCN is composed of spatial and temporal GCN. The spatial shuffle GCN contains pointwise group convolution and part shuffle module which enhances local and global information between correlated joints. In addition, the temporal shuffle GCN contains depthwise convolution to maintain a large receptive field. Our model achieves comparable performance with lowest computational cost and exceeds the performance of baseline at 0.3% and 1.2% on NTU RGB+D and NTU RGB+D 120 datasets, respectively.

• International Journal of Computer Science & Network Security
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• 제21권8호
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• pp.288-296
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• 2021

Automated face recognition in a runtime environment is gaining more and more important in the fields of surveillance and urban security. This is a difficult task keeping in mind the constantly volatile image landscape with varying features and attributes. For a system to be beneficial in industrial settings, it is pertinent that its efficiency isn't compromised when running on roads, intersections, and busy streets. However, recognition in such uncontrolled circumstances is a major problem in real-life applications. In this paper, the main problem of face recognition in which full face is not visible (Occlusion). This is a common occurrence as any person can change his features by wearing a scarf, sunglass or by merely growing a mustache or beard. Such types of discrepancies in facial appearance are frequently stumbled upon in an uncontrolled circumstance and possibly will be a reason to the security systems which are based upon face recognition. These types of variations are very common in a real-life environment. It has been analyzed that it has been studied less in literature but now researchers have a major focus on this type of variation. Existing state-of-the-art techniques suffer from several limitations. Most significant amongst them are low level of usability and poor response time in case of any calamity. In this paper, an improved face recognition system is developed to solve the problem of occlusion known as FRS-OCC. To build the FRS-OCC system, the color and texture features are used and then an incremental learning algorithm (Learn++) to select more informative features. Afterward, the trained stack-based autoencoder (SAE) deep learning algorithm is used to recognize a human face. Overall, the FRS-OCC system is used to introduce such algorithms which enhance the response time to guarantee a benchmark quality of service in any situation. To test and evaluate the performance of the proposed FRS-OCC system, the AR face dataset is utilized. On average, the FRS-OCC system is outperformed and achieved SE of 98.82%, SP of 98.49%, AC of 98.76% and AUC of 0.9995 compared to other state-of-the-art methods. The obtained results indicate that the FRS-OCC system can be used in any surveillance application.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권11호
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• pp.4534-4555
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• 2015

We propose a robust object cataloging method using multiple locally distinct heterogeneous features for aiding image retrieval. Due to challenges such as variations in object size, orientation, illumination etc. object recognition is extraordinarily challenging problem. In these circumstances, we adapt local interest point detection method which locates prototypical local components in object imageries. In each local component, we exploit heterogeneous features such as gradient-weighted orientation histogram, sum of wavelet responses, histograms using different color spaces etc. and combine these features together to describe each component divergently. A global signature is formed by adapting the concept of bag of feature model which counts frequencies of its local components with respect to words in a dictionary. The proposed method demonstrates its excellence in classifying objects in various complex backgrounds. Our proposed local feature shows classification accuracy of 98% while SURF,SIFT, BRISK and FREAK get 81%, 88%, 84% and 87% respectively.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제16권2호
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• pp.131-139
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• 2016

Recognition of human motions has become a main area of computer vision due to its potential human-computer interface (HCI) and surveillance. Among those existing recognition techniques for human motions, head detection and tracking is basis for all human motion recognitions. Various approaches have been tried to detect and trace the position of human head in two-dimensional (2D) images precisely. However, it is still a challenging problem because the human appearance is too changeable by pose, and images are affected by illumination change. To enhance the performance of head detection and tracking, the real-time three-dimensional (3D) data acquisition sensors such as time-of-flight and Kinect depth sensor are recently used. In this paper, we propose an effective feature extraction method, called adaptive local binary pattern (ALBP), for depth image based applications. Contrasting to well-known conventional local binary pattern (LBP), the proposed ALBP cannot only extract shape information without texture in depth images, but also is invariant distance change in range images. We apply the proposed ALBP for head detection and tracking in depth images to show its effectiveness and its usefulness.

• 제어로봇시스템학회논문지
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• 제6권1호
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• pp.112-119
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• 2000

A die-casting junction method is usually used to manufacture the tub of an IH(induction heating) jar. If there is a very small air bubble in the junction area, the thermal conductivity is deteriorated and local overheat occurs. Such problem brings serious inferiority of the IH jar. In this paper, we propose a new method to detect such defect with simply measured thermal data. Thermal distribution of preheated tubs is obtained by scanning with infrared thermal sensors and analyzed with the statistic pattern recognition method. By defining the characteristic feature as the temperature difference between sensors and using ellipsoid function as decision boundary, a supervised learning method of genetic algorithm is proposed to obtain the required parpameters. After applying the proposed method to experiment, we have proved that the rate of recognition is high even for a small number of data set.

• 정보학연구
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• 제9권1호
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• pp.55-63
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• 2006

In this paper, a Hangul recognition system using of Kohonen Network in the phoneme separation and learning is proposed. A Hangul consists of phoneme that are consists of strokes. The phoneme recognition and separation are very important in the recognition of character. So, the phonemes which mismatching has been happened are correctly separated through the learning of neural networks. also, learning rate($\alpha$) adjusted according to error, in order to solved that its decreased the number of iteration and the problem of local minimum, adaptively.

• 융합보안논문지
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• 제14권7호
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• pp.9-15
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• 2014

본 논문에서는 기존 LDP 코드의 문제점을 보완하고 화소의 색상 정보와 밝기 정보, 에지 방향 정보, 그리고 에지 반응 크기 정보를 포함할 수 있는 새로운 LDP를 제안한다. 제안된 방법은 얼굴색 정보를 포함하기 위해 기존 LDP 코드를 줄이는 방법을 제안하고 그 결과를 분석하였다. 새로운 LDP 코드는 기존 LDP 코드와 달리 6비트로 표현함으로써 나머지 2비트에 필요로 하는 정보를 포함할 수 있도록 하였으며, 기존 LDP 코드에 비해서 잡음과 환경 변화에 효과적으로 적응할 수 있도록 하였다. 실험 결과 제안된 LDP 코드는 기존 방법들에 비해 높은 인식률 향상과 얼굴 표정인식 결과에서도 효과적임을 보여주었다.