• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2014.10a
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• pp.245-246
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• 2014

This paper suggests the navigation information display system which is based on augmented reality technology and especially focuses on image analysis technology. Navigator has to always confirm the information from marine electronic navigation devices and then they compare with the view of outside targets of the windows. During this 'head down' posture, they feel uncomfortable and sometimes it cause near-accidents such as collision or missing objects, because he or she cannot keep an eye on the front view of windows. Augmented reality can display both of information of virtual and real in a single display. Therefore we tried to adapt the AR technology to help navigators and have been studied and developed image pre-processing module as a previous research already. To analysis the outside view of the bridge window, we have extracted navigational information from the camera image by using image processing. This paper mainly describes about recognizing ship feature by haar-like feature and filtering region of interest area by AIS data, which are to improve accuracy of the image analysis.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.6B
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• pp.663-673
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• 2011

Applications of two dimensional image codes like QR and color code on mobile phone are spreading widely and in lieu of that, better decoding technology and platform designs are required. Futhermore "Bokode" technology which recognizes the defocus-blur image information from imperceptible visual tiny code on ordinary digital camera has been announced newly. There is an urgent need for competitive strategies to activate 2D image code industry and R&D under smart environment. This paper focused on suggesting valuable idea and practical task through investigation and analysis required for supporting of thus strategy construction. Description consist of introduction, technological tendency for R&D, patent and market, implementation task, conclusion and suggestion.

• Journal of Satellite, Information and Communications
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• v.12 no.2
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• pp.56-60
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• 2017

This paper describes the design and implementation of fall-down detection algorithm based on image processing. The fall-down detection algorithm separates objects by using background subtraction and binarization after grayscale conversion of the input image acquired by the camera, and recognizes the human body by using labeling operation. The recognized human body can be monitored on the display image, and an alarm is generated when fall-down is detected. By using computer simulation, the proposed algorithm has shown a detection rate of 90%. We verify the feasibility of the proposed system by verifying the function by using the prototype test implemented on the DSP image processing board.

• Journal of Korea Multimedia Society
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• v.11 no.6
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• pp.746-754
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• 2008

A 3D stereoscopic image is generated by interdigitating every scene with video editing tools that are rendered by two cameras' views in 3D modeling tools, like Autodesk MAX(R) and Autodesk MAYA(R). However, the depth of object from a static scene and the continuous stereo effect in the view of transformation, are not represented in a natural method. This is because after choosing the settings of arbitrary angle of convergence and the distance between the modeling and those two cameras, the user needs to render the view from both cameras. So, the user needs a process of controlling the camera's interval and rendering repetitively, which takes too much time. Therefore, in this paper, we will propose the 3D stereoscopic image editing system for solving such problems as well as exposing the system's inherent limitations. We can generate the view of two cameras and can confirm the stereo effect in real-time on 3D modeling tools. Then, we can intuitively determine immersion of 3D stereoscopic image in real-time, by using the 3D stereoscopic image preview function.

• Journal of the Korea Society of Computer and Information
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• v.23 no.1
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• pp.33-39
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• 2018

In this paper, We studied the possibility of SoC(System On Chip) design using infrared image processing IP(Intellectual Property). And, we studied NUC(Non Uniformity Correction), BPR(Bad Pixel Recovery), and CEM(Contrast Enhancement) processing, the infrared image processing algorithm implemented by IP. We showed the logic and timing diagram implemented through the hardware block designed based on each algorithm. Each algorithm was coded as RTL(Register Transfer Level) using Verilog HDL(Hardware Description Language), ALTERA QUARTUS synthesis, and programed in FPGA(Field Programmable Gated Array). In addition, we have verified that the image data is processed at each algorithm without any problems by integrating the infrared image processing algorithm. Particularly, using the directly manufactured electronic board, Processor, SRAM, and FLASH are interconnected and tested and the verification result is presented so that the SoC type can be realized later. The infrared image processing IP proposed and verified in this study is expected to be of high value in the future SoC semiconductor fabrication. In addition, we have laid the basis for future application in the camera SoC industry.

• Journal of radiological science and technology
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• v.42 no.5
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• pp.365-371
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• 2019

We have developed and applied a diagnostic Multi-Leaf Collimator (MLC) to optimized the X-ray field in medical imaging and the usefulness evaluated through the fusion of infrared image and X-ray image acquired by infrared camera. The hand and skull radiography with multi-leaf collimator(MLC) showed significant area dose reductions of 22.9% and 31.3% compared to ARC and leakage dose was compliant with KS A 4732. Also scattering doses of 50 cm and 100 cm showed a significant decrease to confirm the usefulness of MLC. It was confirmed that the fusion of infrared images with an adjustable degree of transparency was possible in the X-ray images. Therefore, fusion of anatomical information with physiological convergence is expected to contribute and improvement of diagnostic ability. In addition, the feasibility of convergence X-ray imaging and DITI devices and the possibility of driving MLC with infrared images were confirmed.

• Journal of Advanced Information Technology and Convergence
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• v.10 no.2
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• pp.153-166
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• 2020

DDI(Display Driver IC) are used to drive numerous pixels that make up display. For stable driving of DDI, it is necessary to attach a protective film to shield electromagnetic waves. When the protective film is attached, defects often occur if the film is inclined or the center point is not aligned. In order to minimize such defects, an algorithm for correcting the center point and the inclined angle using camera image information is required. This technology detects the corner coordinates of the protective film by image processing in order to correct the positional defects where the protective film is attached. Corner point coordinates are detected using an algorithm, and center point position finds and correction values are calculated using the detected coordinates. LUT (Lookup Table) is used to quickly find out whether the angle is inclined or not. These algorithms were described by Verilog HDL. The method using the existing software requires a memory to store the entire image after processing one image. Since the method proposed in this paper is a method of scanning by adding a line buffer in one scan, it is possible to scan even if only a part of the image is saved after processing one image. Compared to those written in software language, the execution time is shortened, the speed is very fast, and the error is relatively small.

• Annual Conference of KIPS
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• 2024.10a
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• pp.626-629
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• 2024

AI-based image detection technology offers a promising solution for identifying defects in car doors, significantly improving efficiency compared to traditional human inspections. This paper introduces an advanced automatic defect detection system utilizing camera-recorded datasets and trained models to identify defects in aluminum car doors. Unlike previous models focused on aluminum castings, this is the first application specifically targeting car doors. Despite progress in defect detection research, challenges such as data imbalance, complex defect characteristics, and limited research on aluminum car doors persist. To address these issues, we propose the LKADenseNet201 model, enhancing the DenseNet201 architecture with a large kernel attention mechanism.While doing this, we focus on 3 important issues: image augmentation, channel attention and model evaluation.Our image processing process mainly include image augmentation. With image augmentation, we aimed to make data diversity suitable for the real world by obtaining data from different angles and to eliminate the imbalance between defect and normal images. This improvement boosts the model's ability to perceive contextual features and increases computational efficiency, essential for detailed spatial understanding and time-critical tasks. Our approach not only enhances operator efficiency but also moves towards automating the inspection process.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.3
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• pp.2192-2200
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• 2015

In this paper, we propose an efficient vehicle image compensation algorithm based on Histogram Equalization. The proposed a vehicle image compensation algorithm was elimination to the vehicle image shake using motion compensation and motion estimation. And, algorithm was calculated the histogram of pixel values from each sub-image by dividing the image as the constant size areas in order to image enhancement. Also, it had enhancement to the image by adjusting the gradient. The proposed algorithm was evaluate the difference between of performance and time, image by applied to the IP, and were confirmed the image enhancement with removing of vehicle camera image shake. In this paper, the proposed vehicle image enhancement algorithm was demonstrated effectiveness when compared to existing vehicle image stabilization, because the elimination of shake for the vehicle images used real-time processing without using a memory. And it was obtained the reduction effect of the computation time by the calculated through block matching, and obtained the better restoration result for naturalness of the image with the lowest noise.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.835-838
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• 2003

Computer vision and natural-language dialogue play an important role in friendly human-machine interfaces for service robots. In this paper we describe an integrated face detection and face recognition system for a welfare robot, which has also been combined with the robot's speech interface. Our approach to face detection is to combine neural network (NN) and genetic algorithm (GA): ANN serves as a face filter while GA is used to search the image efficiently. When the face is detected, embedded Hidden Markov Model (EMM) is used to determine its identity. A real-time system has been created by combining the face detection and recognition techniques. When motivated by the speaker's voice commands, it takes an image from the camera, finds the face inside the image and recognizes it. Experiments on an indoor environment with complex backgrounds showed that a recognition rate of more than 88% can be achieved.