• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.538-539
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• 2018

This paper summarizes the implementation of lane recognition using OpenCV, one of the open source computer vision libraries. The Linux operating system Rasbian(r18.03.13) was installed on the ARM processor-based Raspberry Pi 3 board, and Raspberry Pi Camera was used for image processing. In order to realize the lane recognition, Canny Edge Detection and Hough Transform algorithm implemented in OpenCV library was used and RANSAC algorithm was used to prevent shaking of vanishing point and to detect only the desired straight line. In addtion, the DC motor and the Servo motor were controlled so that the vehicle would run according to the detected lane.

• Journal of the Semiconductor & Display Technology
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• v.13 no.1
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• pp.27-33
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• 2014

Video from unmanned aerial vehicle (UAV) is influenced by natural environments due to the light-weight UAV, specifically by winds. Thus UAV's shaking movements make the video shaking. Objective of this paper is making a stabilized video by removing shakiness of video acquired by UAV. Stabilizer estimates camera's motion from calculation of optical flow between two successive frames. Estimated camera's movements have intended movements as well as unintended movements of shaking. Unintended movements are eliminated by smoothing process. Experimental results showed that our proposed method performs almost as good as the other off-line based stabilizer. However estimation of camera's movements, i.e., calculation of optical flow, becomes a bottleneck to the real-time stabilization. To solve this problem, we make parallel stabilizer making average 30 frames per second of stabilized video. Our proposed method can be used for the video acquired by UAV and also for the shaking video from non-professional users. The proposed method can also be used in any other fields which require object tracking, or accurate image analysis/representation.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.12
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• pp.1002-1010
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• 2016

This paper describes the design of a cellphone curved glass measuring device using by camera. The measuring device was composed of two camera, two backlight system, a body and so on, and the program was made for a camera calibration and noise removal, and also the program was made for height measurement of a cellphone curved glass using by subpixel algorism. And then a new technique for measuring the height of the cell phone curved glass was proposed. The characteristics test of height measurement of gage blocks and cell phone curved glasses was carried out, the error of the height measurement of gage block is less than ${\pm}0.005$ and the error of the height measurement of the cell phone curved glasses is less than ${\pm}0.005$. Thus it thought that the designed cellphone curved glass measuring device and the new technique for measuring the height was used to measure the height of the cellphone curved glass.

• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.7
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• pp.23-30
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• 2014

"Cut off line" in automotive passing beam has very important safety function because it serves for headlamp aiming. Headlights that are aimed incorrectly will not only perform poorly but also offend oncoming traffic. In addition, an objective definition of cut off line in low beam is necessary, since a requirement for correct aiming of the beams is specified within all the existing regulations. Accordingly, headlight regulations are requirements that automobiles must satisfy in order to be sold in a particular country. In this study, a more advanced recognition method for the cut off lines of the various headlamps commonly used in Europe, North America, and domestic is suggested and a headlamp testing system is developed to adjust the beam to the country-specific regulation. This system uses image processing technology to detect the cut off lines in the beam patterns of halogen headlamps, high-intensity discharge headlamps, and light-emitting diode headlamps as well.

• Journal of the Korea Institute of Military Science and Technology
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• v.17 no.2
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• pp.175-181
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• 2014

In this paper, we propose a robust clutter pre-thresholding method using background adaptive Threshold Map for the clutter rejection in the complex coastal environment. The proposed algorithm is composed of the use of Threshold Map's and method of its calculation. Additionally we also suggest an automatic decision method of Thresold Map's update. Experimental results on some sets of real infrared image sequence show that the proposed method could remove clutters effectively without any loss of detection rate for the aim target and reduce processing time dramatically.

• The Journal of Korea Robotics Society
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• v.14 no.4
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• pp.357-362
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• 2019

This paper proposes a model and train method that can real-time detect objects and distances estimation based on a monocular camera by applying deep learning. It used YOLOv2 model which is applied to autonomous or robot due to the fast image processing speed. We have changed and learned the loss function so that the YOLOv2 model can detect objects and distances at the same time. The YOLOv2 loss function added a term for learning bounding box values x, y, w, h, and distance values z as 클래스ification losses. In addition, the learning was carried out by multiplying the distance term with parameters for the balance of learning. we trained the model location, recognition by camera and distance data measured by lidar so that we enable the model to estimate distance and objects from a monocular camera, even when the vehicle is going up or down hill. To evaluate the performance of object detection and distance estimation, MAP (Mean Average Precision) and Adjust R square were used and performance was compared with previous research papers. In addition, we compared the original YOLOv2 model FPS (Frame Per Second) for speed measurement with FPS of our model.

• Smart Structures and Systems
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• v.18 no.3
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• pp.585-599
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• 2016

Steel cables serve as the key structural components in long-span bridges, and the force state of the steel cable is deemed to be one of the most important determinant factors representing the safety condition of bridge structures. The disadvantages of traditional cable force measurement methods have been envisaged and development of an effective alternative is still desired. In the last decade, the vision-based sensing technology has been rapidly developed and broadly applied in the field of structural health monitoring (SHM). With the aid of vision-based multi-point structural displacement measurement method, monitoring of the tensile force of the steel cable can be realized. In this paper, a novel cable force monitoring system integrated with a multi-point pattern matching algorithm is developed. The feasibility and accuracy of the developed vision-based force monitoring system has been validated by conducting the uniaxial tensile tests of steel bars, steel wire ropes, and parallel strand cables on a universal testing machine (UTM) as well as a series of moving loading experiments on a scale arch bridge model. The comparative study of the experimental outcomes indicates that the results obtained by the vision-based system are consistent with those measured by the traditional method for cable force measurement.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.4160-4173
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• 2019

Borescopes facilitate the inspection of areas inside machines and systems that are not directly accessible for visual inspection. They offer real-time, up-close access to confined and hard-to-access spaces without having to dismantle or destructure the object under inspection. Borescopes are ideal instruments for routine maintenance, quality inspection and monitoring of systems and structures. The main application being fault or defect detection, it is useful to have measuring capability to quantify object dimensions in a target area. High-end borescopes use multi-optic solutions to provide measurement information of viewed objects. Multi-optic solutions can provide accurate measurements at the expense of structural complexity and cost increase. Measuring functionality is often unavailable in low-end, single camera borescopes. In this paper, a single camera measurement solution that enables the size estimation of viewed objects is proposed. The proposed solution computes and overlays a scaled grid of known spacing value over the screen view, enabling the human inspector to estimate the size of the objects in view. The proposed method provides a simple means of measurement that is applicable to low-end borescopes with no built-in measurement capability.

• ETRI Journal
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• v.41 no.1
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• pp.93-108
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• 2019

Guaranteeing interoperability between devices and applications is the core role of standards organizations. Since its first JPEG standard in 1992, the Joint Photographic Experts Group (JPEG) has published several image coding standards that have been successful in a plethora of imaging markets. Recently, these markets have become subject to potentially disruptive innovations owing to the rise of new imaging modalities such as light fields, point clouds, and holography. These so-called plenoptic modalities hold the promise of facilitating a more efficient and complete representation of 3D scenes when compared to classic 2D modalities. However, due to the heterogeneity of plenoptic products that will hit the market, serious interoperability concerns have arisen. In this paper, we particularly focus on the holographic modality and outline how the JPEG committee has addressed these tremendous challenges. We discuss the main use cases and provide a preliminary list of requirements. In addition, based on the discussion of real-valued and complex data representations, we elaborate on potential coding technologies that range from approaches utilizing classical 2D coding technologies to holographic content-aware coding solutions. Finally, we address the problem of visual quality assessment of holographic data covering both visual quality metrics and subjective assessment methodologies.

• The Journal of Korean Institute of Information Technology
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• v.17 no.5
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• pp.77-82
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• 2019

Diabetic retinopathy is a complicated form of diabetes due to circulatory disorder in the peripheral blood vessels of the retina. We segment the microvessel for diagnosing diabetic retinophathy. The conventional methods using filter and features can segment the thick blood vessels, but it has relatively weak for segmenting fine blood vessels. In pre-processing step, noise reduction filter and histogram equalization are applied to suppress the noise and enhance the image contrast. Then, deep learning technique is used for pixel-by-pixel segmentation. The accuracy of conventional methods is between 90% to 94%, while the proposed method has improved as 95% accuracy. There is a problem of segmentation error around the optic disc and exudate due to the network depth. However the accuracy can be improved by modifying the network architecture in the future.