• Plant Journal
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• v.10 no.4
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• pp.29-34
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• 2014

In this consideration, this research investigated the piping design items and drew out key design items through interview with experts and surveys to apply them to actual project examples and look into piping design item-specific significance and cost variation ranges. Based on this investigation, the Monte-Carlo simulation was employed herein to analyze the cost variation range for the entire piping design costs with a view to presenting a way to calculate a reasonable bidding price for any similar project and verify the appropriateness of joining a bid.

• Journal of Information Processing Systems
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• v.9 no.1
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• pp.141-156
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• 2013

In this paper we propose a method to detect human faces in color images. Many existing systems use a window-based classifier that scans the entire image for the presence of the human face and such systems suffers from scale variation, pose variation, illumination changes, etc. Here, we propose a lighting insensitive face detection method based upon the edge and skin tone information of the input color image. First, image enhancement is performed, especially if the image is acquired from an unconstrained illumination condition. Next, skin segmentation in YCbCr and RGB space is conducted. The result of skin segmentation is refined using the skin tone percentage index method. The edges of the input image are combined with the skin tone image to separate all non-face regions from candidate faces. Candidate verification using primitive shape features of the face is applied to decide which of the candidate regions corresponds to a face. The advantage of the proposed method is that it can detect faces that are of different sizes, in different poses, and that are making different expressions under unconstrained illumination conditions.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1771-1776
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• 2003

In mobile robot navigation, one of the key problems is the pose estimation of the mobile robot. Although the odometry can be used to describe the motions of the mobile robots quite simple and accurately, the validities of the models are limited by a number of error sources contaminating the encoder outputs so that applying the conventional extended Kalman filter to these nominal model does not yield the satisfactory performance. As a remedy for this problem, we consider the uncertain nonlinear kinematic model of the mobile robot that contains the norm bounded uncertainties and also propose a new robust extended Kalman filter based on the Krein space approach. The proposed robust filter has the same recursive structure as the conventional extended Kalman filter and can hence be readily designed to effectively account for the uncertainties. The computer simulations will be given to verify the robustness against the parameter variation as well as the reliable performance of the proposed robust filter.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.403-406
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• 2001

We propose an algorithm for extracting the boundary of an object. In order to take full advantage of global shape, our approach uses global shape parameters derived from Point Distribution Model (PDM). Unlike PDM, the proposed method models global shape using curvature as well as edge. The objective function of applying the shape model is formulated using Bayesian rule. We can extract the boundaries of an object by evaluating iteratively the solution maximizing the objective function. Experimental results show that the proposed method can reduce computation cost than the PDM and it is robust to noise, pose variation, and some occlusion.

• Journal of information and communication convergence engineering
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• v.12 no.3
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• pp.193-197
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• 2014

Edges are a robust feature for object detection. In this paper, we present an edge-based background modeling method for the detection of moving objects. The edges in the image frames were mapped using robust Canny edge detector. Two edge maps were created and combined to calculate the ultimate moving-edge map. By selecting all the edge pixels of the current frame above the defined threshold of the ultimate moving edges, a temporary background-edge map was created. If the frequencies of the temporary background edge pixels for several frames were above the threshold, then those edge pixels were treated as background edge pixels. We conducted a performance comparison with previous works. The existing edge-based moving-object detection algorithms pose some difficulty due to the changes in background motion, object shape, illumination variation, and noises. The result of the performance evaluation shows that the proposed algorithm can detect moving objects efficiently in real-world scenarios.

• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.2
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• pp.173-180
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• 2011

SRMD system which protects MBT from the threats in a short range is composed of 2 radars, 2 IRTs 1 CCU and 2 countermeasures. To analyze the performance of this system, there is a need to make the model of MBT on which this system is loaded. In this paper, a method of MBT movement modeling is proposed to fulfill the above need. The methods of the coordinate system transformation among the components and the modeling of a hull's traveling and a turret's turning are proposed. The method of the modeling of a pose variation caused by the vibrations is proposed and the results of the modeling are presented.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.1
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• pp.228-246
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• 2011

A 3D model based approach for a face representation and recognition algorithm has been investigated as a robust solution for pose and illumination variation. Since a generative 3D face model consists of a large number of vertices, a 3D model based face recognition system is generally inefficient in computation time and complexity. In this paper, we propose a novel 3D face representation algorithm based on a pixel to vertex map (PVM) to optimize the number of vertices. We explore shape and texture coefficient vectors of the 3D model by fitting it to an input face using inverse compositional image alignment (ICIA) to evaluate face recognition performance. Experimental results show that the proposed face representation and recognition algorithm is efficient in computation time while maintaining reasonable accuracy.

• Anatomy and Cell Biology
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• v.51 no.4
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• pp.305-308
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• 2018

Anatomical variations of the sternocleidomastoid muscle (SCM) have been observed to occupy multiple origins and insertion points and have supernumerary heads, sometimes varying in thickness. During routine dissection, a SCM was observed to have six distinct insertions that interface with the course of the superior nuchal line, ending at the midline, bilaterally. This variation was also seen to receive innervation from the accessory nerve as well as the great auricular nerve. To our knowledge, this variant of supernumerary insertions and nerve innervations has not yet been reported. These variants may pose as problematic during surgical approaches to the upper neck and occiput, and should thus be appreciated by the clinician. Herein we discuss the case report, possible embryological origins, and the clinical significance of the observed variant SCM.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.871-891
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• 2019

Visual tracking is a challenging task that needs learning an effective model to handle the changes of target appearance caused by factors such as pose variation, illumination change, occlusion and motion blur. In this paper, a novel tracking algorithm based on weighted local sub-space reconstruction error is presented. First, accounting for the appearance changes in the tracking process, a generative weight calculation method based on structural reconstruction error is proposed. Furthermore, a template update scheme of occlusion-aware is introduced, in which we reconstruct a new template instead of simply exploiting the best observation for template update. The effectiveness and feasibility of the proposed algorithm are verified by comparing it with some state-of-the-art algorithms quantitatively and qualitatively.

• Journal of Information Processing Systems
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• v.15 no.1
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• pp.67-90
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• 2019

Accurate detection, tracking and analysis of human movement using robots and other visual surveillance systems is still a challenge. Efforts are on to make the system robust against constraints such as variation in shape, size, pose and occlusion. Traditional methods of detection used the sliding window approach which involved scanning of various sizes of windows across an image. This paper concentrates on employing a state-of-the-art, hierarchical graph based method for segmentation. It has two stages: part level segmentation for color-consistent segments and object level segmentation for category-consistent regions. The tracking phase is achieved by employing SIFT keypoint descriptor based technique in a combined matching and tracking scheme with validation phase. Localization of human region in each frame is performed by keypoints by casting votes for the center of the human detected region. As it is difficult to avoid incorrect keypoints, a consensus-based framework is used to detect voting behavior. The designed methodology is tested on the video sequences having 3 to 4 persons.