• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1999년도 제14차 학술회의논문집
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• pp.270-273
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• 1999

Circular dynamic stereo has special advantages as it enables a 3-D measurement using a single TV camera and also enables a high accurate measurement without a cumbersome calibration. Annular particle streaks are recorded using this system and the size of annular streaks directly concerns to the depth from TV camera. That is, the size of annular streaks is inversely proportional to the depth from the TV camera and the depth can be measured automatically by image processing technique. Overlapped streaks can be processed also by our method. The flow measurement in a water tank is one of the applications of our system. Tracer particles are introduced into the water in a flow measurement. Since the tracer particles flow with water, three-dimensional velocity distributions in the water tank can be obtained by measuring the all movement of tracer particles. Experimental results demonstrate the feasibility of our method.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2006년도 하계종합학술대회
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• pp.941-942
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• 2006

A depth measurement system to recognize 3D shape of objects using single camera, line laser and a rotating mirror has been investigated. The camera and the light source are fixed, facing the rotating mirror. The laser light is reflected by the mirror and projected to the scene objects whose locations are to be determined. The camera detects the laser light location on object surfaces through the same mirror. The scan over the area to be measured is done by mirror rotation. The Segmentation process of object recognition is performed using the depth data of restored 3D data. The Object recognition domain can be reduced by separating area of interest objects from complex background.

• 한국군사과학기술학회지
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• 제19권1호
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• pp.66-75
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• 2016

Stereoscopic image generated by depth image-based rendering(DIBR) for surveillance robot and camera is appropriate in a low bandwidth network. The image is very important data for the decision-making of a commander and thus its integrity has to be guaranteed. One of the methods used to detect manipulation is to check if the stereoscopic image is taken from the original camera. Sensor pattern noise(SPN) used widely for camera identification cannot be directly applied to a stereoscopic image due to the stereo warping in DIBR. To solve this problem, we find out a shifted object in the stereoscopic image and relocate the object to its orignal location in the center image. Then the similarity between SPNs extracted from the stereoscopic image and the original camera is measured only for the object area. Thus we can determine the source of the camera that was used.

• 한국통신학회논문지
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• 제37권4C호
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• pp.277-282
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• 2012

본 논문에서는 실감 원격 영상회의를 위한 시선 맞춤 시스템을 제안한다. 제안하는 방법은 적외선 구조광을 사용하는 Kinect 깊이 카메라를 이용해서 색상 영상과 깊이 영상을 획득하고, 깊이 영상을 이용해서 사용자를 배경으로부터 분리한다. 깊이 카메라로부터 획득한 가공되지 않은 깊이 영상은 다양한 형태의 잡음을 가지고 있기 때문에, 첫번째 전처리 과정으로 결합형 양방향 필터를 사용해서 잡음을 제거한다. 그 다음, 깊이값의 불연속성에 적응적인 저역 필터를 적용한다. 색상 영상과 전처리 과정을 거친 깊이 영상을 이용해서 우리는 가상시점에서의 화자를 3차원 모델로 복원한다. 전체 시스템은 GPU 기반의 병렬 프로그래밍을 통해 실시간 처리가 가능하도록 했다. 최종적으로, 우리는 시선이 조정된 원격의 화자 영상을 얻을 수 있게 된다. 실험 결과를 통해 제안하는 시스템이 자연스러운 화자간 시선 맞춤을 실시간으로 가능하게 하는 것을 확인했다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 추계학술대회 논문집 학회본부 B
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• pp.839-841
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• 1999

This paper presents the method of 3D reconstruction of the depth information from the endoscopic stereo scopic images. After camera modeling to find camera parameters, we performed feature-point based stereo matching to find depth information. Acquired some depth information is finally 3D reconstructed using the NURBS(Non Uniform Rational B-Spline) algorithm. The final result image is helpful for the understanding of depth information visually.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1997년도 추계학술대회
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• pp.142-145
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• 1997

This paper presents the development of depth extraction algorithm or the 3D Endoscopic Data using a stereo matching method and depth calculation. The purpose of other's algorithms is to reconstruct 3D object surface and make depth map, but a one of this paper is to measure exact depth information on the base of [cm] from camera to object. For this, we carried out camera calibration.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 G
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• pp.2950-2952
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• 1999

This paper presents the method of 3D reconstruction of the depth information from the endoscopic stereo scopic images. After camera modeling to find camera parameters, we performed feature-point based stereo matching to find depth information. Acquired some depth information is finally 3D reconstructed using the NURBS(Non Uniform Rational B-Spline) method and OpenGL. The final result image is helpful for the understanding of depth information visually.

• Journal of information and communication convergence engineering
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• 제11권2호
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• pp.132-138
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• 2013

In this paper, an accelerated generation algorithm to effectively generate an elemental image array in computational integral imaging system is proposed. In the proposed method, the depth information of 3D object is extracted from the images picked up by a stereo camera or depth camera. Then, the elemental image array can be generated by using the proposed accelerated generation algorithm with the depth information of 3D object. The resultant 3D image generated by the proposed accelerated generation algorithm was compared with that the conventional direct algorithm for verifying the efficiency of the proposed method. From the experimental results, the accuracy of elemental image generated by the proposed method could be confirmed.

• 한국산학기술학회논문지
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• 제21권6호
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• pp.28-34
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• 2020

본 논문에서는 라이다(LIDAR) 센서와 일반 카메라 (RGB 센서)가 획득한 영상들을 정합하고, 일반 카메라가 획득한 컬러 영상에 해당하는 깊이맵을 생성하는 방법을 제시한다. 본 연구에서는 Slamtec사의 RPLIDAR A3 와 일반 디지털 카메라를 활용하고, 두 종류의 센서가 획득 및 제공하는 정보의 특징 및 형태는 서로 다르다. 라이다 센서가 제공하는 정보는 라이다부터 객체 또는 주변 물체들까지의 거리이고, 디지털 카메라가 제공하는 정보는 2차원 영상의 Red, Green, Blue 값이다. 두 개의 서로 다른 종류의 센서를 활용하여 정보를 정합할 경우 객체 검출 및 추적에서 더 좋은 성능을 보일 수 있는 가능성이 있고, 자율주행 자동차, 로봇 등 시각정보처리 기술이 필요한 영역에서 활용도가 높은 것으로 기대한다. 두 종류의 센서가 제공하는 정보들을 정합하기 위해서는 각 센서가 획득한 정보를 가공하고, 정합에 적합하도록 처리하는 과정이 필요하다. 본 논문에서는 두 센서가 획득하는 정보들을 정합한 결과를 제공할 수 있는 전처리 방법을 실험 결과와 함께 제시한다.

• 전기학회논문지
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• 제67권8호
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• pp.1080-1088
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• 2018

General methods of controlling a drone are divided into manual control and automatic control, which means a drone moves along the route. In case of manual control, a man should be able to figure out the location and status of a drone and have a controller to control it remotely. When people control a drone, they collect information about the location and position of a drone with the eyes and have its internal information such as the battery voltage and atmospheric pressure delivered through telemetry. They make a decision about the movement of a drone based on the gathered information and control it with a radio device. The automatic control method of a drone finding its route itself is not much different from manual control by man. The information about the position of a drone is collected with the gyro and accelerator sensor, and the internal information is delivered to the CPU digitally. The location information of a drone is collected with GPS, atmospheric pressure sensors, camera sensors, and ultrasound sensors. This paper presents an investigation into drone control by a remote computer. Instead of using the automatic control function of a drone, this approach involves a computer observing a drone, determining its movement based on the observation results, and controlling it with a radio device. The computer with a Depth camera collects information, makes a decision, and controls a drone in a similar way to human beings, which makes it applicable to various fields. Its usability is enhanced further since it can control common commercial drones instead of specially manufactured drones for swarm flight. It can also be used to prevent drones clashing each other, control access to a drone, and control drones with no permit.