• The korean journal of orthodontics
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• v.32 no.2 s.91
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• pp.91-105
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• 2002

In general, orthodontists make problem lists and treatment plans based on norms of several cephalometric standards. But consideration of dentoalveolar compensation, which tends to maintain normal dental arch relationship in various skeletal jaw relationships, helps orthodontists make more individualized treatment objectives and plans. The purpose of this study was to classify skeletal patterns of normal occlusion samples by cluster analysis and to investigate the dentoalveolar compensation according to skeletal patterns. The subjects were consisted of 125 subjects who were normal occlusion samples at Seoul National University Dental Hospital, Department of Orthodontics. Lateral cephalograms in centric occlusion were traced and digitized. The skeletal patterns of normal occlusion samples were classified into three horizontal groups and three vertical groups by cluster analysis and ANOVA on the skeletal and dentoalveolar measurements among the groups were carried out. The results were as follows ; 1. Anteroposterior and vertical skeletal relationships of normal occlusion samples were very variable. 2. As the mandibular position was anterior to the maxilla, the maxillary incisors inclined more labially, the mandibular incisors more lingually, and the occlusal plane was flattened due to the anteroposterior dentoalveolar compensation. dentoalveolar height was decreased and upper posterior teeth was uprighted to the palatal plane and lower incisors and lower posterior teeth to the mandibular plane. 4. Lower incisors were more strongly associated with the dentoalveolar compensation than upper incisors according to the anteroposterior and vertical skeletal relationship.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.6
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• pp.194-203
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• 2008

Though conventional video surveillance systems such as CCTV depended on operators, recently developed intelligent surveillance systems no longer needed operators. However, these new intelligent surveillance systems have their own problems such as Occlusion, changing scale of target object, and affine. This paper handled information damage caused by changing the scale of the target object among other objects. Due to the change of the scale, the inaccurate information of target can be obtained when we update the background information. To handle this problem, we introduce a solution for information damage caused by problem of changing scale of target object located among other objects. Specifically, we suggest multi-stage sampling particle filter based advanced MSER for object tracking system. Through this method, the problem caused by changing scale of target can be avoided.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.11
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• pp.4502-4518
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• 2015

Fisher linear discriminant analysis (LDA) is one of the most popular projection techniques for feature extraction and has been widely applied in face recognition. However, it cannot be used when encountering the single sample per person problem (SSPP) because the intra-class variations cannot be evaluated. In this paper, we propose a novel method called local similarity based linear discriminant analysis (LS_LDA) to solve this problem. Motivated by the "divide-conquer" strategy, we first divide the face into local blocks, and classify each local block, and then integrate all the classification results to make final decision. To make LDA feasible for SSPP problem, we further divide each block into overlapped patches and assume that these patches are from the same class. To improve the robustness of LS_LDA to outliers, we further propose local similarity based median discriminant analysis (LS_MDA), which uses class median vector to estimate the class population mean in LDA modeling. Experimental results on three popular databases show that our methods not only generalize well SSPP problem but also have strong robustness to expression, illumination, occlusion and time variation.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.12
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• pp.1817-1824
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• 1990

This paper describes a shape matching algorithm for occluded or distorted two-dimensional objects. In our approach, the shape matchin is viewed as a segment matching problem. A shape matching algorithm, based on both the stochastic labeling technique and the hypothesis generate-test paradigm, is proposed, and a simple technique which performs the stochastic labeling process in accordance with the definition of consisten labeling assignment without requiring an iterative updating process of probability valiues is also proposed. Several simulation results show that the proposed algorithm is very effective when occlusion, scaling or change of orientation has occurred in the object.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.915-916
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• 2008

We propose a clustering based object feature matching for identification of same object in multi-camera system. The method is focused on ease to system initialization and extension. Clustering is used to estimate parameters of Gaussian mixture models of objects. A similarity measure between models are determined by Kullback-Leibler divergence. This method can be applied to occlusion problem in tracking.

• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.671-674
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• 1988

This paper describes a matching method to solve the problem of occlusion in a two dimensional scene. The technique consist of three steps: generation of hypotheses, clustering of hypotheses by matching probability, updating of hypotheses. Using this algorithm, simulation results have been tested for 20 scenes contained the 80 models, and have obtained 95% of properly correct recognition rate in average.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.11
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• pp.305-313
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• 2019

Recently, spatial information is being constructed actively based on the images obtained by drones. Because occlusion areas occur due to buildings as well as many obstacles, such as trees, pedestrians, and banners in the urban areas, an efficient way to resolve the problem is necessary. Instead of the traditional way, which replaces the occlusion area with other images obtained at different positions, various models based on deep learning were examined and compared. A comparison of a type of feature descriptor, HOG, to the machine learning-based SVM, deep learning-based DNN, CNN, and RNN showed that the CNN is used broadly to detect and classify objects. Until now, many studies have focused on the development and application of models so that it is impossible to select an optimal model. On the other hand, the upgrade of a deep learning-based detection and classification technique is expected because many researchers have attempted to upgrade the accuracy of the model as well as reduce the computation time. In that case, the procedures for generating spatial information will be changed to detect the occlusion area and replace it with simulated images automatically, and the efficiency of time, cost, and workforce will also be improved.

• The KIPS Transactions:PartA
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• v.17A no.4
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• pp.189-196
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• 2010

This paper presents an improved marker detection algorithm using hybrid features such as corner, line segment, region, and adaptive threshold values, etc. In usual augmented reality environments, there are often marker occlusion and poor illumination. However, existing ARToolkit fails to recognize the marker in these situations, especially, partial concealment of marker by user, large change of illumination and dim circumstances. In order to solve these problems, the adaptive threshold technique is adopted to extract a marker region and a corner extraction method based on line segments is presented against marker occlusions. In addition, a compensating method, corresponding the marker size and center between registered and extracted one, is proposed to increase the template matching efficiency, because the inside marker size of warped images is slightly distorted due to the movement of corner and warping. Therefore, experimental results showed that the proposed algorithm can robustly detect the marker in severe illumination change and occlusion environment and use similar markers because the matching efficiency was increased almost 30%.

• Journal of Broadcast Engineering
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• v.4 no.1
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• pp.59-67
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• 1999

In this paper, we study two important topics in stereoscopic image communication system. One is a disparity estimation (DE) method to obtain the depth information of a scene at the transmitter and the other is an intermediate view reconstruction(IVR) method at the receiver. We propose a new DE method using an edge-preserving directional regularization technique. The proposed DE method smooths disparity vectors in smooth regions and preserves edges without over-smoothing problem. It provides better reconstructed stereoscopic images and improved coding efficiency than the existing regularization techniques. In addition. we propose a new IVR method using interpolation and extrapolation techniques. The proposed IVR method preserves edge regions as well as occlusion regions well. Thus. it gives better intermediate views than the existing IVR methods.

• Journal of the Korea Society of Computer and Information
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• v.21 no.10
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• pp.11-19
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• 2016

The size of a display is large, The form becoming various of that do not apply to previous methods of gaze tracking and if setup gaze-track-camera above display, can solve the problem of size or height of display. However, This method can not use of infrared illumination information of reflected cornea using previous methods. In this paper, Robust pupil detecting method for eye's occlusion, corner point of inner eye and center of pupil, and using the face pose information proposes a method for calculating the simply position of the gaze. In the proposed method, capture the frame for gaze tracking that according to position of person transform camera mode of wide or narrow angle. If detect the face exist in field of view(FOV) in wide mode of camera, transform narrow mode of camera calculating position of face. The frame captured in narrow mode of camera include gaze direction information of person in long distance. The method for calculating the gaze direction consist of face pose estimation and gaze direction calculating step. Face pose estimation is estimated by mapping between feature point of detected face and 3D model. To calculate gaze direction the first, perform ellipse detect using splitting from iris edge information of pupil and if occlusion of pupil, estimate position of pupil with deformable template. Then using center of pupil and corner point of inner eye, face pose information calculate gaze position at display. In the experiment, proposed gaze tracking algorithm in this paper solve the constraints that form of a display, to calculate effectively gaze direction of person in the long distance using single camera, demonstrate in experiments by distance.