• Journal of Internet Computing and Services
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• v.10 no.5
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• pp.183-194
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• 2009

In this paper, we propose a new method for detecting objectionable images with an active shape model. Our method first learns the shape of breast lines through principle component analysis and alignment as well as the distribution of intensity values of corresponding landmarks, and then extracts breast lines with the learned shape and intensity distribution. To accurately select the initial position of active shape model, we obtain parameters on scale, rotation, and translation. After positioning the initial location of active shape model using scale and rotation information, iterative searches are performed. We can identify adult images by calculating the average of the distance between each landmark and a candidate breast line. The experiment results show that the proposed method can detect adult images effectively by comparing various results.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.12
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• pp.1492-1499
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• 2019

This paper propose how to track the position of the observer to control the viewing zone using an adaptive parallax barrier. The pose is estimated using a Constrained Local Model based on the shape model and Landmark for robust eye-distance measurement in the face pose. Camera's correlation converts distance and horizontal location to centimeter. The pixel pitch of the adaptive parallax barrier is adjusted according to the position of the observer's eyes, and the barrier is moved to adjust the viewing area. This paper propose a method for tracking the observer in the range of 60cm to 490cm, and measure the error, measurable range, and fps according to the resolution of the camera image. As a result, the observer can be measured within the absolute error range of 3.1642cm on average, and it was able to measure about 278cm at 320×240, about 488cm at 640×480, and about 493cm at 1280×960 depending on the resolution of the image.

• Journal of Multimedia Information System
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• v.5 no.1
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• pp.15-20
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• 2018

There are a lot of problems in the face detection area. One of them is detecting faces by facial features and reducing number of the false negatives and positions. This paper is directed to solve this problem by the proposed triangle method. Also, this paper explans cascades, Haar-like features, AdaBoost, HOG. We propose a scheme using 12-net, 24-net, 48-net to scan images and improve efficiency. Using triangle method for frontal pose, B and B1 methods for other poses in neural networks are proposed.

• Journal of Sensor Science and Technology
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• v.13 no.2
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• pp.144-151
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• 2004

In this paper, we propose an active stereo surveillance system with human-like convergence function. The proposed system uses a bottom-up saliency map model with the human-like selective attention visual function to select an interesting region in each camera. and this system compares the landmarks whether the selective region in each camera finds a same region. If the left and right cameras successfully find a same landmarks, the implemented vision system focuses on the landmark. Using the motor encoder information, we can automatically obtain the depth information and resultantly construct a depth map using the depth information. Computer simulation and experimental results show that the proposed convergence method is very effective to implement the active stereo surveillance system.

• Korean Journal of Head & Neck Oncology
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• v.22 no.1
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• pp.29-32
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• 2006

Background: The location of parotid gland tumors can influence the duration and the difficulty of the operation. If the information about tumor location was available preoperatively, it would allow accurate operative planning and counseling of patients in terms of the length of the operation and the potential morbidity. Methods: This study was based on a retrospective review of 100 patients with parotid gland tumors underwent parotidectomy from January 2000 to October 2005 at Yong-Dong Severance Hospital. Based on computed tomographic(CT) scan findings, 4 landmarks such as facial nerve(FN) line, Utrecht(U) line, Conn's are(CA), and retromandibular vein (RV) were drawn on the scans in same plane. The location of tumors were determined by the landmarks and confirmed by the operative findings. The accuracy of each landmarks was evaluated. To find out the accuracies according to tumor size, the tumors were divided into 2 groups; less than 2 cm and larger than 2 cm in diameter. Results: U line was the most accurate(94%), sensitive(89.3%) and specific(97.7%) in predicting tumor location of the parotid gland. However, in small tumors less than 2cm, FN line (p=0.022) and RV criteria (p=0.028) were more reliable in accuracy. Conclusion: CA, FN line, U line, and RV are all useful landmarks in preoperative prediction for the location of parotid gland tumors. However, U line was the most accurate, but we must consider that proper landmark should be used in prediction according to the size of tumor because the accuracy of landmark may change.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37C no.9
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• pp.763-773
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• 2012

본 논문에서는 Wi-Fi를 이용한 RSSI (Received Signal Strength Indication)기반의 측위 시스템에서 전파환경변화에 민감한 시스템의 문제를 보완하기 위하여 랜드마크 발신기 기반의 측위 시스템을 제안한다. 이를 위하여 핑거프린팅 알고리즘 및 랜드마크 발신기를 구현 하였으며 간단한 측위 정확도 예측모델을 만들어 수신된 전파세기를 기반으로 측위 정확도를 예측하였다. 이를 바탕으로 실내 TEST BED를 구축하였고 주요 지점에 랜드마크 발신기를 부착하여 시스템의 효용성을 검증하였다.

• Journal of Advanced Marine Engineering and Technology
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• v.36 no.5
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• pp.674-682
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• 2012

A new intelligent stereo vision sensor system was studied for the motion and depth control of unmanned vehicles. A new bottom-up saliency map model for the human-like active stereo vision system based on biological visual process was developed to select a target object. If the left and right cameras successfully find the same target object, the implemented active vision system with two cameras focuses on a landmark and can detect the depth and the direction information. By using this information, the unmanned vehicle can approach to the target autonomously. A number of tests for the proposed bottom-up saliency map were performed, and their results were presented.

• The Journal of Korea Robotics Society
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• v.4 no.4
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• pp.312-319
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• 2009

This paper proposes a low-complexity indoor localization method of mobile robot under the dynamic environment by fusing the landmark image information from an ordinary camera and the distance information from sensor nodes in an indoor environment, which is based on sensor network. Basically, the sensor network provides an effective method for the mobile robot to adapt to environmental changes and guides it across a geographical network area. To enhance the performance of localization, we used an ordinary CCD camera and the artificial landmarks, which are devised for self-localization. Experimental results show that the real-time localization of mobile robot can be achieved with robustness and accurateness using the proposed localization method.

• Proceedings of the KSRS Conference
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• v.1
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• pp.47-50
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• 2006

Tsunami disaster caused great damages and very large victims especially when occurs in urban area along coastal region. Therefore information of evacuation in a map is very important for disaster preparedness in order to minimize the number of victims in affected area. Here, information generated from remote sensing satellite data (SPOT 5 and DEM) and secondary data (administration boundary and field survey data) are used to simulate evacuation route and to produce a map for Padang City. Vulnerability and evacuation areas are determined based on DEM. Landuse/landcover, accessibility areas, infrastructure and landmark are extracted from SPOT 5 data. All the data obtained from remote sensing and secondary data are integrated using geospatial modelling to determine evacuation routes. Finally the simulation of evacuation route in Padang City for tsunami preparedness is provided based on the parameters derived from remote sensing data such as distances from shelters, save zones, city's landmarks and the local community experiences how they can survive with the disaster.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.2
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• pp.133-139
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• 2013

This paper proposes a vision-based indoor localization method for autonomous vehicles. A single upward-facing digital camera was mounted on an autonomous vehicle and used as a vision sensor to identify artificial landmarks and any natural corner features. An interest point detector was used to find the natural features. Using an optical flow detection algorithm, information related to the direction and vehicle translation was defined. This information was used to track the vehicle movements. Random noise related to uneven light disrupted the calculation of the vehicle translation. Thus, to estimate the vehicle translation, a Kalman filter was used to calculate the vehicle position. These algorithms were tested on a vehicle in a real environment. The image processing method could recognize the landmarks precisely, while the Kalman filter algorithm could estimate the vehicle's position accurately. The experimental results confirmed that the proposed approaches can be implemented in practical situations.