• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.4
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• pp.173-180
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• 2006

A key component of autonomous navigation of intelligent home robot is localization and map building with recognized features from the environment. To validate this, accurate measurement of relative location between robot and features is essential. In this paper, we proposed relative localization algorithm based on 3D reconstruction of scale invariant features of two images which are captured from two parallel cameras. We captured two images from parallel cameras which are attached in front of robot and detect scale invariant features in each image using SIFT(scale invariant feature transform). Then, we performed matching for the two image's feature points and got the relative location using 3D reconstruction for the matched points. Stereo camera needs high precision of two camera's extrinsic and matching pixels in two camera image. Because we used two cameras which are different from stereo camera and scale invariant feature point and it's easy to setup the extrinsic parameter. Furthermore, 3D reconstruction does not need any other sensor. And the results can be simultaneously used by obstacle avoidance, map building and localization. We set 20cm the distance between two camera and capture the 3frames per second. The experimental results show :t6cm maximum error in the range of less than 2m and ${\pm}15cm$ maximum error in the range of between 2m and 4m.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.517-521
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• 2008

Hand motion classification problem is considered as basis for sign or gesture recognition. We promote optical flow as main feature extracted from images sequences to simultaneously segment the motion's area by its magnitude and characterize the motion' s directions by its orientation. We manage the flow orientation histogram as motion descriptor. A motion is encoded by concatenating the flow orientation histogram from several frames. We utilize simple histogram matching to classify the motion sequences. Attempted experiments show the feasibility of our method for hand motion localization and classification.

• The KIPS Transactions:PartB
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• v.14B no.3 s.113
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• pp.163-170
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• 2007

In autonomous navigation of a mobile vehicle or a mobile robot, localization calculated from recognizing its environment is most important factor. Generally, we can determine position and pose of a camera equipped mobile vehicle or mobile robot using INS and GPS but, in this case, we must use enough known ground landmark for accurate localization. hi contrast with homography method to calculate position and pose of a camera by only using the relation of two dimensional feature point between two frames, in this paper, we propose a method to calculate the position and the pose of a camera using relation between the location to predict through perspective transform of 3D feature points obtained by overlaying 3D model with previous frame using GPS and INS input and the location of corresponding feature point calculated using KLT tracking method in current frame. For the purpose of the performance evaluation, we use wireless-controlled vehicle mounted CCD camera, GPS and INS, and performed the test to calculate the location and the rotation angle of the camera with the video sequence stream obtained at 15Hz frame rate.

• Molecules and Cells
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• v.21 no.2
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• pp.186-191
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• 2006

Severe acute respiratory syndrome-associated coronavirus (SARS-CoV), a distant member of the Group 2 coronaviruses, has recently been identified as the etiological agent of severe acute respiratory syndrome (SARS). The genome of SARS-CoV contains four structural genes that are homologous to genes found in other coronaviruses, as well as six subgroup-specific open reading frames (ORFs). ORF3 encodes a predicted 154-amino-acid protein that lacks similarity to any known protein, and is designated 3b in this article. We reported previously that SARS-CoV 3b is predominantly localized in the nucleolus, and induces G0/G1 arrest and apoptosis in transfected cells. In this study, we show that SARS-CoV 3b fused with EGFP at its N- or C- terminus co-localized with a mitochondriaspecific marker in some transfected cells. Mutation analysis of SARS-CoV 3b revealed that the domain spanning amino acids 80 to 138 was essential for its mitochondria localization. These results provide new directions for studies of the role of SARS-CoV 3b protein in SARS pathogenesis.

• ETRI Journal
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• v.43 no.4
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• pp.630-639
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• 2021

Autonomous driving requires a computerized perception of the environment for safety and machine-learning evaluation. Recognizing semantic information is difficult, as the objective is to instantly recognize and distinguish items in the environment. Training a model with real-time semantic capability and high reliability requires extensive and specialized datasets. However, generalized datasets are unavailable and are typically difficult to construct for specific tasks. Hence, a light detection and ranging semantic dataset suitable for semantic simultaneous localization and mapping and specialized for autonomous driving is proposed. This dataset is provided in a form that can be easily used by users familiar with existing two-dimensional image datasets, and it contains various weather and light conditions collected from a complex and diverse practical setting. An incremental and suggestive annotation routine is proposed to improve annotation efficiency. A model is trained to simultaneously predict segmentation labels and suggest class-representative frames. Experimental results demonstrate that the proposed algorithm yields a more efficient dataset than uniformly sampled datasets.

• Journal of Advanced Navigation Technology
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• v.19 no.2
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• pp.133-139
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• 2015

This paper surveys vision based autonomous navigation technologies for unmanned systems. Main branches of visual navigation technologies are visual servoing, visual odometry, and visual simultaneous localization and mapping (SLAM). Visual servoing provides velocity input which guides mobile system to desired pose. This input velocity is calculated from feature difference between desired image and acquired image. Visual odometry is the technology that estimates the relative pose between frames of consecutive image. This can improve the accuracy when compared with the exisiting dead-reckoning methods. Visual SLAM aims for constructing map of unknown environment and determining mobile system's location simultaneously, which is essential for operation of unmanned systems in unknown environments. The trend of visual navigation is grasped by examining foreign research cases related to visual navigation technology.

• Journal of the Korea institute for structural maintenance and inspection
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• v.25 no.2
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• pp.58-65
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• 2021

As increasing social needs of pavement maintenance, pattern frame method has been constructed in Korea. The pattern frame not only increases the skid resistance of pavements but also improve the scenery. However, construction of the pattern frame currently relies on imported materials. In this paper, we localize the materials used in pattern frame and conduct performance verification on them. The important performance indicators are the adhesion strength of undercoating materials and the skid resistance of finished pattern frames. The adhesion strength was targeted at 1.4MPa, and the localization alternative material met the target performance with 2.35MPa, the skid resistance performance was targeted at 40BPN, and the localization alternative material met the target performance with 75BPN. In the case of localized materials, approximately 40% cost reduction (per 1m²)compared to imported materials was confirmed.

• Honam Mathematical Journal
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• v.27 no.2
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• pp.233-241
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• 2005

A sequence $\{f_i\}^{\infty}_{i=1}$ in a Hilbert space H is said to be exponentially localized with respect to a Riesz basis $\{g_i\}^{\infty}_{i=1}$ for H if there exist positive constants r < 1 and C such that for all i, $j{\in}N$, ${\mid}{\mid}{\leq}Cr^{{\mid}i-j{\mid}}$ and ${\mid}{\mid}{\leq}Cr^{{\mid}i-j{\mid}}$ where $\{{\tilde{g}}_i\}^{\infty}_{i=1}$ is the dual basis of $\{g_i\}^{\infty}_{i=1}$. It can be shown that such sequence is always a Bessel sequence. We present an additional condition which guarantees that $\{f_i\}^{\infty}_{i=1}$ is a frame for H.

• The Journal of the Acoustical Society of Korea
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• v.36 no.5
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• pp.345-352
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• 2017

We propose a methods which is reducing direction estimation error of sound source in the reverberant and noisy environments. The proposed algorithm divides speech signal into voice and unvoice using VAD. We estimate the direction of source when current frame is voiced. TDOA (Time-Difference of Arrival) between microphone array using the GCC-PHAT (Generalized Cross Correlation with Phase Transform) method will be estimated in that frame. Then, we compare the peak value of cross-correlation of two signals applied to estimated time-delay with other time-delay in time-table in order to improve the accuracy of source location. If the angle of current frame is far different from before and after frame in successive voiced frame, the angle of current frame is replaced with mean value of the estimated angle in before and after frames.

• Journal of Broadcast Engineering
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• v.26 no.6
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• pp.748-757
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• 2021

In this paper, we propose a gaze estimation network in which eye landmark position detection and gaze direction vector estimation are integrated into one deep learning network. The proposed network uses the Stacked Hourglass Network as a backbone structure and is largely composed of three parts: a landmark detector, a feature map extractor, and a gaze direction estimator. The landmark detector estimates the coordinates of 50 eye landmarks, and the feature map extractor generates a feature map of the eye image for estimating the gaze direction. And the gaze direction estimator estimates the final gaze direction vector by combining each output result. The proposed network was trained using virtual synthetic eye images and landmark coordinate data generated through the UnityEyes dataset, and the MPIIGaze dataset consisting of real human eye images was used for performance evaluation. Through the experiment, the gaze estimation error showed a performance of 3.9, and the estimation speed of the network was 42 FPS (Frames per second).