• IEMEK Journal of Embedded Systems and Applications
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• v.12 no.2
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• pp.97-103
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• 2017

Thermal imaging is mainly used in military equipment required for night observation. In particular, technologies of uncooled thermal imaging detectors are being developed as applied to low-cost night observation system. Many system integrators require different specifications of the uncooled thermal imaging camera but their development time is short. In this approach, EOSYSTEM has developed a small size, TEC-less uncooled thermal imaging camera module with $32{\times}32mm$ size and low power consumption. Both domestic detector and import detector are applied to the EOSYSTEM's thermal imaging camera module. The camera module contains efficient infrared image processing algorithms including : Temperature compensation non-uniformity correction, Bad/Dead pixel replacement, Column noise removal, Contrast/Edge enhancement algorithms providing stable and low residual non-uniformity infrared image.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.463-466
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• 2004

This paper describes an image processing algorithm for recognition of small-obstacles using a camera and program for a mobile robot in indoor environment. Mobile robot could meet small-obstacles such as a small plastic bottle of about 1l in quantity, a small box of 7$\times$7$\times$7 cm$^3$ in volume, and so on in its designated path, and could be disturbed by them in the locomotion of a mobile robot. So, it is necessary to research on the image processing algorithm for recognition of small-obstacles using a camera and program. In this paper, 2-D the image processing algorithm for recognition of small-obstacles using a camera and program for a mobile robot in indoor environment was developed. The characteristic test of the developed program to confirm the recognition of small-obstacles was performed. It is shown that the developed program could recognize small-obstacles accurately.

• Journal of the Korean Institute of Navigation
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• v.16 no.3
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• pp.77-85
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• 1992

Camera arrangement for depth and image correspondence is very important to the computer vision. Two conventional comera arrangements for stereo computer vision are lateral model and axial motion model. In this paper, using the axial motion stereo camera model, the algorithm for camera focal length measurement and the surface smoothness with the radiance-irradiance is proposed fro 3-dimensional image correspondence on stereo computer vision. By adapting the above algorithm, camera focal length can be measured precisely and the resolution of 3-dimensional image correspondence has been improved comparing to that of the axial motion model without the radiance-irradiance relation.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2821-2823
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• 2005

When we deal with the image data through camera lens, much works are necessary for removing image distortions and obtaining accurate informations from the raw data. However, the calibration process is very complicated and requires many trials and errors. In this paper, 3 new approach to image processing is presented by developing a H/W vision system with a tracking camera. Using motor control with encoders the proposed tracking method tells us exact displacements of a moving object. Therefore this method does not require any calibration process for pin cusion. Owing to the mobility one camera covers wide ranges and, by lowering its height, the camera also obtains high resolution of the image. We first introduce the structure of the motion analysis system. Then the construced vision system is investigated by some experiments.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.255-257
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• 2005

This paper describes a method for obstacle avoidance and map building for mobile robots using one CCD camera. The captured image from one camera has the feature that some parts where focused look fine but the other parts look blear (this is the out-focusing effect). Using this feature a mobile robot can find obstacles in his way from the captured image. After Processing the image, a robot can not only determine whether an obstacle is in front of him or not, but also calculate the distance from obstacles based on image data and the focal distance of its camera lens. Finally, robots can avoid the obstacle and build the map using this calculated data.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1995.06a
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• pp.77-81
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• 1995

Video composition is to integrate multiple image materials into one scene. It considerably enhances the degree of freedom in producing various scenes. However, we need to adjust the viewing point sand the image planes of image planes of image materials for high quality video composition. In this paper, were propose an intelligent video composition technique concentrating on the composition of CG and real scene. We first model the camera system. The projection is assumed to be perspective and the camera motion is assumed to be 3D rotational and 3D translational. Then, we automatically extract camera parameters comprising the camera model from real scene by a dedicated algorithm. After that, CG scene is generated according to the camera parameters of the real scene. Finally the two are composed into one scene. Experimental results justify the validity of the proposed method.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.339-339
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• 2000

In this parer, a method of measuring the size of defects on the wall and restructing the defect image of 3-dimension is developed based on the tracking algorithm of a camera position which uses the inclination angle of line slit beam for overcoming the difficulty of the corresponding problem identifying the image point in the both image. In the experiments, an algorithm for estimating the horizontal angle of CCD camera is presented and validated by applying it to the measurement of area and length under the variations of both the distance and the angle of CCD camera. And its performance is compared to that of the rotating and mapping method of image which has the Euclidian distance.

• Journal of Sensor Science and Technology
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• v.22 no.6
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• pp.393-399
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• 2013

We propose a method of sensing the rotation and distance of a camera by using a fisheye lens system as a vision sensor. We estimate the rotation angle of a camera with a modified correlation method by clipping similar regions to avoid symmetry problems and suppressing highlight areas. In order to eliminate the rectification process of the distorted points of a fisheye lens image, we introduce an offline process using the normalized focal length, which does not require the image sensor size. We also formulate an equation for calculating the distance of a camera movement by matching the feature points of the test image with those of the reference image.

• Journal of information and communication convergence engineering
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• v.2 no.1
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• pp.52-57
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• 2004

In this paper, we propose a panorama-based object tracking scheme for wide-view surveillance systems that can detect and track moving objects with a pan-tilt camera. A dynamic mosaic of the background is progressively integrated in a single image using the camera motion information. For the camera motion estimation, we calculate affine motion parameters for each frame sequentially with respect to its previous frame. The camera motion is robustly estimated on the background by discriminating between background and foreground regions. The modified block-based motion estimation is used to separate the background region. Each moving object is segmented by image subtraction from the mosaic background. The proposed tracking system has demonstrated good performance for several test video sequences.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06c
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• pp.477-481
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• 2007

A background is a part where do not vary too much or frequently change in an image sequence. Using this assumption, it is presented a background acquisition algorithm for not only static but also dynamic view in this paper. For generating background, we detect a region, where has high correlation rate compared within selected region in the prior pyramid image, from the searching region in the current image. Between a detected region in the current image and a selected region in the prior image, we calculate movement vector for each regions in time sequence. After we calculate whole movement vectors for two successive images, vector histogram is used to determine the camera movement. The vector which has the highest density in the histogram is determined a camera movement. Using determined camera movement, we classify clusters based on pixel intensities which pixels are matched with prior pixels following camera movement. Finally we eliminate clusters which have lower weight than threshold, and combine remained clusters for each pixel to generate multiple background clusters. Experimental results show that we can automatically detect background whether camera move or not.