• 한국컴퓨터정보학회논문지
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• 제23권1호
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• pp.41-48
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• 2018

Recently, surveillance systems are widely used, and one of the key technologies in this surveillance system is to recognize and track objects. In order to track a moving object robustly and efficiently in a complex environment, it is necessary to extract the feature points in the interesting object and to track the object using the feature points. In this paper, we propose a method to track interesting objects in real time by eliminating unnecessary information from objects, generating feature point descriptors using only key feature points, and reducing computational complexity for object recognition. Experimental results show that the proposed method is faster and more robust than conventional methods, and can accurately track objects in various environments.

• Smart Structures and Systems
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• 제29권1호
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• pp.117-127
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• 2022

Data anomalies seriously threaten the reliability of the bridge structural health monitoring system and may trigger system misjudgment. To overcome the above problem, an efficient and accurate data anomaly detection method is desiderated. Traditional anomaly detection methods extract various abnormal features as the key indicators to identify data anomalies. Then set thresholds artificially for various features to identify specific anomalies, which is the artificial experience method. However, limited by the poor generalization ability among sensors, this method often leads to high labor costs. Another approach to anomaly detection is a data-driven approach based on machine learning methods. Among these, the bidirectional long-short memory neural network (BiLSTM), as an effective classification method, excels at finding complex relationships in multivariate time series data. However, training unprocessed original signals often leads to low computation efficiency and poor convergence, for lacking appropriate feature selection. Therefore, this article combines the advantages of the two methods by proposing a deep learning method with manual experience statistical features fed into it. Experimental comparative studies illustrate that the BiLSTM model with appropriate feature input has an accuracy rate of over 87-94%. Meanwhile, this paper provides basic principles of data cleaning and discusses the typical features of various anomalies. Furthermore, the optimization strategies of the feature space selection based on artificial experience are also highlighted.

• 정보보호학회논문지
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• 제28권6호
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• pp.1427-1437
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• 2018

최근 생체 인증 시스템이 확대됨에 따라, 생체 정보를 이용하여 공개키 기반구조(Bio-PKI)에 적용하는 연구들이 진행 중이다. Bio-PKI 시스템에서는 공개키를 생성하기 위해 생체 정보로부터 암호학적 키를 생성하는 과정이 필요하다. 암호학적 키 생성 방법 중 특성 정보를 숫자로 정량화하는 기법은 데이터 손실을 유발하고 이로 인해 키 추출 성능이 저하된다. 이 논문에서는 다중 분류 모델을 이용하여 생체 정보를 분류한 결과를 이용하여 키를 생성하는 방법을 제안한다. 제안하는 기법은 특성 정보의 손실이 없어 높은 키 추출 성능을 보였고, 여러 개의 분류 모델을 이용하기 때문에 충분한 길이의 키를 생성한다.

• International Journal of CAD/CAM
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• 제9권1호
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• pp.71-77
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• 2010

Various applications, such as mesh composition and model repair, ask for a natural stitching for polygonal surfaces. Unlike the existing algorithms, we make full use of the information from the two feature lines to be stitched up, and present an accurate stitching method for polygonal surfaces, which minimizes the error between the feature lines. Given two directional polylines as the feature lines on polygonal surfaces, we modify the general placement method for points matching and arrive at a closed-form solution for optimal rotation and translation between the polylines. Following calculating out the stitching line, a local surface optimization method is designed and employed for postprocess in order to gain a natural blending of the stitching region.

• 전자공학회논문지CI
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• 제47권6호
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• pp.75-85
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• 2010

본 논문에서는 3D 콘텐츠 인증을 위한 객체별 특징 벡터 기반 강인한 3D 모델 해싱을 제안한다. 제안한 3D 모델 해싱에서는 다양한 객체들로 구성된 3D 모델에서 높은 면적을 가지는 특징 객체내의 꼭지점 거리들을 그룹화한다. 그리고 각 그룹들을 치환한 다음, 그룹 계수, 랜덤 변수 키와 이진화 과정에 의하여 최종 해쉬를 생성한다. 이 때 해쉬의 강인성은 객체 그룹별 꼭지점 거리 분포를 그룹 계수에 의하여 향상되고, 해쉬의 유일성은 그룹 계수를 치환 키 및 랜덤변수 키 기반의 이진화 과정에 의하여 향상된다. 실험 결과로부터 제안한 해싱이 다양한 메쉬 공격 및 기하학 공격에 대한 해쉬의 강인성과 유일성을 확인하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권3호
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• pp.1614-1632
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• 2017

Bag of visual words is a popular model in human action recognition, but usually suffers from loss of spatial and temporal configuration information of local features, and large quantization error in its feature coding procedure. In this paper, to overcome the two deficiencies, we combine sparse coding with spatio-temporal pyramid for human action recognition, and regard this method as the baseline. More importantly, which is also the focus of this paper, we find that there is a hierarchical structure in feature vector constructed by the baseline method. To exploit the hierarchical structure information for better recognition accuracy, we propose a tree regularized classifier to convey the hierarchical structure information. The main contributions of this paper can be summarized as: first, we introduce a tree regularized classifier to encode the hierarchical structure information in feature vector for human action recognition. Second, we present an optimization algorithm to learn the parameters of the proposed classifier. Third, the performance of the proposed classifier is evaluated on YouTube, Hollywood2, and UCF50 datasets, the experimental results show that the proposed tree regularized classifier obtains better performance than SVM and other popular classifiers, and achieves promising results on the three datasets.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권8호
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• pp.4041-4059
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• 2019

Key establishment method based on channel reciprocity for time division duplex (TDD) system has earned a vital consideration in the majority of recent research. While most of the cellular systems rely on frequency division duplex (FDD) systems, especially the 5G network, which is not characterized by the channel reciprocity feature. This paper realizes the generation of a group secret key for multi-terminals communicated through a wireless network in FDD mode, by utilizing the nature of the physical layer for the wireless links between them. I consider a new group key generation approach, which using bitwise XOR with a modified pairwise secret key generation approach not based on the channel reciprocity feature. Precisely, this multi-node secret key agreement technique designed for three wireless network topologies: 1) the triangle topology, 2) the multi-terminal star topology, and 3) the multi-node chain topology. Three multi-node secret key agreement protocols suggest for these wireless communication topologies in FDD mode, respectively. I determine the upper bound for the generation rate of the secret key shared among multi-node, for the three multi-terminals topologies, and give numerical cases to expose the achievement of my offered technique.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권6호
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• pp.3086-3103
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• 2017

Video images captured by vehicle cameras often contain blurry or dithering frames due to inadvertent motion from bumps in the road or by insufficient illumination during the morning or evening, which greatly reduces the perception of objects expression and recognition from the records. Therefore, a real-time electronic stabilization method to correct fuzzy video from driving recorders has been proposed. In the first stage of feature detection, a coarse-to-fine inspection policy and a scale nonlinear diffusion filter are proposed to provide more accurate keypoints. Second, a new antiblurry binary descriptor and a feature point selection strategy for unintentional estimation are proposed, which brought more discriminative power. In addition, a new evaluation criterion for affine region detectors is presented based on the percentage interval of repeatability. The experiments show that the proposed method exhibits improvement in detecting blurry corner points. Moreover, it improves the performance of the algorithm and guarantees high processing speed at the same time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권11호
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• pp.5592-5609
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• 2017

JPEG steganography detection is an active research topic in the field of information hiding due to the wide use of JPEG image in social network, image-sharing websites, and Internet communication, etc. In this paper, a new steganalysis method for content-adaptive JPEG steganography is proposed by integrating the evolutionary feature selection and classifier ensemble selection. First, the whole framework of the proposed steganalysis method is presented and then the characteristic of the proposed method is analyzed. Second, the feature selection method based on genetic algorithm is given and the implement process is described in detail. Third, the method of classifier ensemble selection is proposed based on Pareto evolutionary optimization. The experimental results indicate the proposed steganalysis method can achieve a competitive detection performance by compared with the state-of-the-art steganalysis methods when used for the detection of the latest content-adaptive JPEG steganography algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권2호
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• pp.832-854
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• 2019

Action recognition has been studied in computer vision field for years. We present an effective approach to recognize actions using a dimension reduction method, which is applied as a crucial step to reduce the dimensionality of feature descriptors after extracting features. We propose to use sparse matrix and randomized kd-tree to modify it and then propose modified Local Fisher Discriminant Analysis (mLFDA) method which greatly reduces the required memory and accelerate the standard Local Fisher Discriminant Analysis. For feature encoding, we propose a useful encoding method called mix encoding which combines Fisher vector encoding and locality-constrained linear coding to get the final video representations. In order to add more meaningful features to the process of action recognition, the convolutional neural network is utilized and combined with mix encoding to produce the deep network feature. Experimental results show that our algorithm is a competitive method on KTH dataset, HMDB51 dataset and UCF101 dataset when combining all these methods.