• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.2 s.308
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• pp.30-39
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• 2006

Iris recognition is that identifies a user based on the unique iris texture patterns which has the functionalities of dilating or contracting pupil region. Iris recognition systems extract the iris pattern in iris image captured by iris recognition camera. Therefore performance of iris recognition is affected by the quality of iris image which includes iris pattern. If iris image is blurred, iris pattern is transformed. It causes FRR(False Rejection Error) to be increased. Optical defocusing is the main factor to make blurred iris images. In conventional iris recognition camera, they use two kinds of focusing methods such as lilted and auto-focusing method. In case of fixed focusing method, the users should repeatedly align their eyes in DOF(Depth of Field), while the iris recognition system acquires good focused is image. Therefore it can give much inconvenience to the users. In case of auto-focusing method, the iris recognition camera moves focus lens with auto-focusing algorithm for capturing the best focused image. However, that needs additional H/W equipment such as distance measuring sensor between users and camera lens, and motor to move focus lens. Therefore the size and cost of iris recognition camera are increased and this kind of camera cannot be used for small sized mobile device. To overcome those problems, we propose method to increase DOF by iris image restoration algorithm based on focus value of iris image. When we tested our proposed algorithm with BM-ET100 made by Panasonic, we could increase operation range from 48-53cm to 46-56cm.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.2072-2072
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• 2009

최근 연구들에서, 증강 현실(Augmented Reality; AR) 환경에서의 촉각 상호작용에 대한 가능성이 논의되었다. 비젼 기반의 트래킹을 기초로 한 증강 현실 기술은 미리 정의된 2차원 마커(marker)를 이용하여, 카메라로부터 획득된 실시간 영상 위에 가상 물체를 증강한다. 그러나, 카메라로부터 획득된 데이터는 몇몇 오차 요인들, 예를 들어 마커의 위치를 인식하는데 나타나는 오차, 카메라 안에 존재하는 센서 잡음 등으로 인해서 마커 잡음(마커를 인식하면서 나타나는 잡음)이 불가결하게 발생하게 된다. 이러한 이유로 인해서, 사용자가 한 손에는 마커를, 다른 한 손으로는 촉감 장치를 이용하여, 마커에 증강된 물체를 만질 때, 마커 잡음은 힘의 떨림(force trembling)을 발생시킨다. 심지어, 이러한 현상은 정지된 마커에 증강된, 마커가 움직이지 않는 상황에서도 발생한다. 게다가, 마커 위에 증강된 물체가 약간 빠른 속도로 이동하게 될 경우, 측정된 이동 거리는 연속적인 프레임(frame)들 간의 불연속적일 수 있다. 만약 사용자가, 대략 30Hz로 위치와 방향이 갱신되는 가상물체를 촉각적으로 상호작용하려 한다면, 계산되는 반력은 급작스런 힘의 변화를 생성하게 될 수도 있다. 이러한 현상을 극복하기 위해서, 마커 잡음을 최소화하기 위해서 정적 임계값(constant threshold)을 이용할 뿐만 아니라, 보간법을 같이 사용한 방법이 있었다. 하지만, 이러한 방법은 정적 임계값을 이용하고, 영상 프레임 갱신 속도와(video frame rate)와 촉각 프레임 갱신 속도가 일정하다는 가정을 사용하였기 때문에, 여전히 힘의 불연속적인 발생이 나타난다. 따라서, 이 논문에서는 두 가지 방법을 이용하여 증강 현실 내에서, 발생할 수 있는 힘의 불연속적인 변화를 보정하는 두 가지 방법, 잡음 제거를 위한 확장된 칼만 필터(Extend Kalman Filter)와 영상과 촉각 갱신 속도 차이에 따른 갑작스런 힘의 변화를 제거하기 위한 적응적 외삽법(Adaptive Extrapolation method)을 제안한다.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.2
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• pp.155-160
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• 2014

In this paper, an algorithm using RGB-depth camera is proposed to detect smoke in interrior. RGB-depth camera, the Kinect provides RGB color image and depth information. The Kinect sensor consists of an infra-red laser emitter, infra-red camera and an RGB camera. A specific pattern of speckles radiated from the laser source is projected onto the scene. This pattern is captured by the infra-red camera and is analyzed to get depth information. The distance of each speckle of the specific pattern is measured and the depth of object is estimated. As the depth of object is highly changed, the depth of object plain can not be determined by the Kinect. The depth of smoke can not be determined too because the density of smoke is changed with constant frequency and intensity of infra-red image is varied between each pixels. In this paper, a smoke detection algorithm using characteristics of the Kinect is proposed. The region that the depth information is not determined sets the candidate region of smoke. If the intensity of the candidate region of color image is larger than a threshold, the region is confirmed as smoke region. As results of simulations, it is shown that the proposed method is effective to detect smoke in interior.

• Journal of the Korean Geotechnical Society
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• v.28 no.12
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• pp.111-122
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• 2012

Resistivity seismic dilatometer (RSDMT) system is introduced. The resistivity module for obtaining resistivity-depth plot and seismic module for obtaining wave velocity-depth plot are attached to the conventional flat dilatometer testing equipment. To enhance the reliability and repeatability of seismic part in RSDMT, automatic testing system including automatic surface source, PC based data acquisition system and operating program was constructed. To obtain real resistivity value of soil, geometric factor for the array of electrodes in RSDMT was derived empirically. The verification studies for the developed RSDMT system were performed with SPT, CPTu, bender element test and DC resistivity survey. Through one penetration of RSDMT, various soil parameters were obtained and the reliability and repeatability of developed RSDMT system could be checked.

• The KIPS Transactions:PartB
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• v.9B no.6
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• pp.853-862
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• 2002

Face Detection can be defined as follows : Given a digitalized arbitrary or image sequence, the goal of face detection is to determine whether or not there is any human face in the image, and if present, return its location, direction, size, and so on. This technique is based on many applications such face recognition facial expression, head gesture and so on, and is one of important qualify factors. But face in an given image is considerably difficult because facial expression, pose, facial size, light conditions and so on change the overall appearance of faces, thereby making it difficult to detect them rapidly and exactly. Therefore, this paper proposes fast and exact face detection which overcomes some restrictions by using neural network. The proposed system can be face detection irrelevant to facial expression, background and pose rapidily. For this. face detection is performed by neural network and detection response time is shortened by reducing search region and decreasing calculation time of neural network. Reduced search region is accomplished by using skin color segment and frame difference. And neural network calculation time is decreased by reducing input vector sire of neural network. Principle Component Analysis (PCA) can reduce the dimension of data. Also, pose estimates in extracted facial image and eye region is located. This result enables to us more informations about face. The experiment measured success rate and process time using the Squared Mahalanobis distance. Both of still images and sequence images was experimented and in case of skin color segment, the result shows different success rate whether or not camera setting. Pose estimation experiments was carried out under same conditions and existence or nonexistence glasses shows different result in eye region detection. The experiment results show satisfactory detection rate and process time for real time system.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.25 no.3
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• pp.296-303
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• 2014

In this paper, we propose an algorithm to visualize radio wave environment based on the measured Received Signal Strength Indication( RSSI) and 3D geographic information. We estimate the source position using the circumcenter of the triangle and visualize the radio wave environment using the empirical propagation models. A mode seeking algorithm(mean-shift clustering) is used to seek the peak points and the center of gravity is utilized to reduce the estimation errors. Our approach finds its applications in the radio wave monitoring systems for the efficient utilization of radio resources.

• Journal of the Korea Computer Graphics Society
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• v.27 no.5
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• pp.63-71
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• 2021

Among drone autonomous flight technologies, obstacle avoidance is a very important technology that can prevent damage to drones or surrounding environments and prevent danger. Although the LiDAR sensor-based obstacle avoidance method shows relatively high accuracy and is widely used in recent studies, it has disadvantages of high unit price and limited processing capacity for visual information. Therefore, this paper proposes an obstacle avoidance algorithm for drones using camera-based PPO(Proximal Policy Optimization) reinforcement learning, which is relatively inexpensive and highly scalable using visual information. Drone, obstacles, target points, etc. are randomly located in a learning environment in the three-dimensional space, stereo images are obtained using a Unity camera, and then YOLov4Tiny object detection is performed. Next, the distance between the drone and the detected object is measured through triangulation of the stereo camera. Based on this distance, the presence or absence of obstacles is determined. Penalties are set if they are obstacles and rewards are given if they are target points. The experimennt of this method shows that a camera-based obstacle avoidance algorithm can be a sufficiently similar level of accuracy and average target point arrival time compared to a LiDAR-based obstacle avoidance algorithm, so it is highly likely to be used.

• Korean Journal of Optics and Photonics
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• v.29 no.5
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• pp.204-214
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• 2018

Since LIDAR is advantageous for accurate information acquisition and realization of a high-resolution 3D image based on characteristics that can be precisely measured, it is essential to autonomous navigation systems that require acquisition and judgment of accurate peripheral information without user intervention. Recently, as an autonomous navigation system applying LIDAR has been utilized in human living space, it is necessary to solve the eye-safety problem, and to make reliable judgment through accurate obstacle recognition in various environments. In this paper, we construct a single-shot LIDAR system (SSLs) using a 1550-nm eye-safe light source, and report the analysis method and results of LIDAR signals for various measurement environments, reflective materials, and material angles. We analyze the signals of materials with different reflectance in each measurement environment by using a 5% Al reflector and a building wall located at a distance of 25 m, under indoor, daytime, and nighttime conditions. In addition, signal analysis of the angle change of the material is carried out, considering actual obstacles at various angles. This signal analysis has the merit of possibly confirming the correlation between measurement environment, reflection conditions, and LIDAR signal, by using the SNR to determine the reliability of the received information, and the timing jitter, which is an index of the accuracy of the distance information.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.01a
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• pp.187-188
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• 2021

최근 COVID19 상황에서 생활 속 거리두기가 강조되면서 관광지나 다중이용시설 등의 이용객 수와 밀집도를 파악하는 것이 중요해지고 있다. 따라서, CCTV 영상을 활용하여 저렴한 비용으로 다중이용시설의 출입자수에 대한 정보를 실시간으로 모니터링할 수 있는 시스템이 필요하다. 이를 위해 본 논문에서는 딥러닝 실시간 객체인식기술을 활용한 출입자의 수와 동선을 측정하여 출입자에 대한 통계정보를 웹브라우저를 통해 제공하는 시스템을 개발하였다. 실시간 객체인식기술인 YOLOv4와 YOLOv4-tiny 알고리즘을 Nvidia사의 Jetson AGX Xavier 와 데스크톱PC에 적용하여 각 알고리즘의 FPS와 객체 인식률을 비교 분석 하여 알고리즘을 적용하였다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.11
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• pp.2538-2543
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• 2012

The Percutaneous Coronary intervention is a typical way of testing which could be performance to treat a stenosed region by inserting a stent using catheter. In this case, choosing the best stent amongst various kinds of stent for performing an intervention is the most difficult process. For the reason, a width of the blood vessel which is stenosed must be correctly measured to help an operator choose right size of stent. So based on pixel, a width of the blood vessel measured by using the way of Euclidean distance after designing a center-line of vessel from a certain point assigned by operator is shown as a profile in this study. This study would be used as a goof reference for operators when choosing right size of stent.