• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.6
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• pp.107-116
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• 2015

In compliance with digital device growth, the proliferation of high-tech computers, the availability of high quality and inexpensive video cameras, the demands for automated video analysis is increasing, especially in field of intelligent monitor system, video compression and robot vision. That is why object tracking of computer vision comes into the spotlight. Tracking is the process of locating a moving object over time using a camera. The consideration of object's scale, rotation and shape deformation is the most important thing in robust object tracking. In this paper, we propose a robust object tracking scheme using Random Forest. Specifically, an object detection scheme based on region covariance and ZNCC(zeros mean normalized cross correlation) is adopted for estimating accurate object location. Next, the detected region will be divided into five regions for random forest-based learning. The five regions are verified by random forest. The verified regions are put into the model pool. Finally, the input model is updated for the object location correction when the region does not contain the object. The experiments shows that the proposed method produces better accurate performance with respect to object location than the existing methods.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1013-1027
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• 2021

Images taken with KOMPSAT-3 have additional NIR and PAN bands, as well as RGB regions of the visible ray band, compared to imagestaken with a standard camera. Furthermore, electrical and optical properties must be considered because a wide radius area of approximately 17 km or more is photographed at an altitude of 685 km above the ground. In other words, the camera sensor of KOMPSAT-3 is distorted by each CCD pixel, characteristics of each band,sensitivity and time-dependent change, CCD geometry. In order to solve the distortion, correction of the sensors is essential. In this paper, we propose a method for detecting uniform regions in side-slider-based KOMPSAT-3 images using segment-based noise analysis. After detecting a uniform area with the corresponding algorithm, a correction table was created for each sensor to apply the non-uniformity correction algorithm, and satellite image correction was performed using the created correction table. As a result, the proposed method reduced the distortion of the satellite image,such as vertical noise, compared to the conventional method. The relative radiation accuracy index, which is an index based on mean square error (RA) and an index based on absolute error (RE), wasfound to have a comparative advantage of 0.3 percent and 0.15 percent, respectively, over the conventional method.

• Korean Journal of Optics and Photonics
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• v.4 no.1
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• pp.15-21
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• 1993

This paper describes the design and development of a KrF excimer laser stepper and discusses the detailed system parameters and characterization data obtained from the performance test. We have developed a deep UV step-and-repeat system, operating at 248 nm, by retrofitting a commercial modules such as KrF excimer laser, precision wafer stage and fused silica illumination and 5X projection optics of numerical aperture 0.42. What we have developed, to the basic structure, are wafer alignment optics, reticle alignment system, autofocusing/leveling mechanisms and environment chamber. Finally, all these subsystem were integrated under the control of microprocessor-based controllers and computer. The wafer alignment system comprises the OFF-AXIS and the TTL alignment. The OFF-AXIS alignment system was realized with two kinds of optics. One is the magnification system with the image processing technique and the other is He-Ne laser diffraction type system using the alignment grating on the wafer. 'The TTL alignment system employs a dual beam inteferometric method, which takes advantages of higher diffraction efficiency compared with other TTL type alignment systems. As the results, alignment accuracy for OFF-AXIS and TTL alignment system were obtained within 0.1 $\mu\textrm{m}$/ 3 $\sigma$ for the various substrate on the wafers. The wafer focusing and leveling system is modified version of the conventional systems using position sensitive detectors (PSD). This type of detection method showed focusing and leveling accuracies of about $\pm$ 0.1 $\mu\textrm{m}$ and $\pm$ 0.5 arcsec, respectively. From the CD measurement, we obtained 0.4 $\mu\textrm{m}$ resolution features over the full field with routine use, and 0.3 $\mu\textrm{m}$ resolution was attainable under more strict conditions.

• Journal of Biomedical Engineering Research
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• v.24 no.4
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• pp.241-246
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• 2003

To evaluate the usefulness if functional MR imaging(MRI) for the determination of language dominance system and to assess differences in the visual and auditory instrument language generation task according to activation task or activated area. Functional maps of the language area were obtained during visual and auditory instructions in word generation tasks in 6 healthy volunteer with right-handness were examined on a 1.5T scanner and the EPI BOLD technique, and three pulse sequence technique get of the true axial planes. Both task consisted of 96 phases including 6 activations and rests contents. Postprocessing were done on MRDx program by using cross correlation method. Two task compare the blain activation area surveyed of 1anguage lateralization index. To evaluated of the detection rates of Broca. Wernicke, pre-frontal lobe, Supplementary Motor Area (SMA) and pre-motor cortex areas and the differences of language lateraliaztion among two word generation task To lateralization index survey in 1anguage area on right and left in brain get to activation area pixel in brain. Compared to visual and auditory instrument task in the language areas get to the lateralization index. Two language generation task high detection rates of Broca and Wernicke areas. The visual instruction no detected in the auditory area, and auditory instruction no detected in the visual area. There was statistics significant different of them among language generation task. 1'his indicated that language area obtained image of the brain functional MR imaging usefulness in the visual and auditory task instrument.

• KIPS Transactions on Software and Data Engineering
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• v.1 no.1
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• pp.43-54
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• 2012

This paper presents a new localization technique of an UGV(Unmanned Ground Vehicle) by matching ortho-edge images generated from a DSM (Digital Surface Map) which represents the 3D geometric information of an outdoor navigation environment and 3D range data which is obtained from a LIDAR (Light Detection and Ranging) sensor mounted at the UGV. Recent UGV localization techniques mostly try to combine positioning sensors such as GPS (Global Positioning System), IMU (Inertial Measurement Unit), and LIDAR. Especially, ICP (Iterative Closest Point)-based geometric registration techniques have been developed for UGV localization. However, the ICP-based geometric registration techniques are subject to fail to register 3D range data between LIDAR and DSM because the sensing directions of the two data are too different. In this paper, we introduce and match ortho-edge images between two different sensor data, 3D LIDAR and DSM, for the localization of the UGV. Details of new techniques to generating and matching ortho-edge images between LIDAR and DSM are presented which are followed by experimental results from four different navigation paths. The performance of the proposed technique is compared to a conventional ICP-based technique.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.27-35
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• 2023

In this paper, we propose a process of increasing productivity by applying a deep learning-based defect detection and classification system to the prepreg fiber manufacturing process, which is in high demand in the field of producing composite materials. In order to apply it to toe prepreg manufacturing equipment that requires a solution due to the occurrence of a large amount of defects in various conditions, the optimal environment was first established by selecting cameras and lights necessary for defect detection and classification model production. In addition, data necessary for the production of multiple classification models were collected and labeled according to normal and defective conditions. The multi-classification model is made based on CNN and applies pre-learning models such as VGGNet, MobileNet, ResNet, etc. to compare performance and identify improvement directions with accuracy and loss graphs. Data augmentation and dropout techniques were applied to identify and improve overfitting problems as major problems. In order to evaluate the performance of the model, a performance evaluation was conducted using the confusion matrix as a performance indicator, and the performance of more than 99% was confirmed. In addition, it checks the classification results for images acquired in real time by applying them to the actual process to check whether the discrimination values are accurately derived.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.1099-1110
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• 2023

The recognition system of autonomous driving and robot navigation performs vision work such as object recognition, tracking, and lane detection after multi-sensor fusion to improve performance. Currently, research on a deep learning model based on the fusion of a camera and a lidar sensor is being actively conducted. However, deep learning models are vulnerable to adversarial attacks through modulation of input data. Attacks on the existing multi-sensor-based autonomous driving recognition system are focused on inducing obstacle detection by lowering the confidence score of the object recognition model.However, there is a limitation that an attack is possible only in the target model. In the case of attacks on the sensor fusion stage, errors in vision work after fusion can be cascaded, and this risk needs to be considered. In addition, an attack on LIDAR's point cloud data, which is difficult to judge visually, makes it difficult to determine whether it is an attack. In this study, image scaling-based camera-lidar We propose an attack method that reduces the accuracy of LCCNet, a fusion model (camera-LiDAR calibration model). The proposed method is to perform a scaling attack on the point of the input lidar. As a result of conducting an attack performance experiment by size with a scaling algorithm, an average of more than 77% of fusion errors were caused.

• Journal of the Korean Society of Radiology
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• v.10 no.2
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• pp.81-87
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• 2016

In this study we proposed a texture feature analysis algorithm that distinguishes between a normal image and a diseased image using CT images of some fatty liver patients, and generates both Eigen images and test images which can be applied to the proposed computer aided diagnosis system in order to perform a quantitative analysis for 6 parameters. And through the analysis, we derived and evaluated the recognition rate of CT images of fatty liver. As the results of examining over 30 example CT images of fatty liver, the recognition rates representing a specific texture feature-value are as follows: some appeared to be as high as 100% including Average Gray Level, Entropy 96.67%, Skewness 93.33%, and Smoothness while others showed a little low disease recognition rate: 83.33% for Uniformity 86.67% and for Average Contrast 80%. Consequently, based on this research result, if a software that enables a computer aided diagnosis system for medical images is developed, it will lead to the availability for the automatic detection of a diseased spot in CT images of fatty liver and quantitative analysis. And they can be used as computer aided diagnosis data, resulting in the increased accuracy and the shortened time in the stage of final reading.

• The KIPS Transactions:PartB
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• v.17B no.1
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• pp.47-54
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• 2010

Old archived film shows two major defects: line scratch and blobs. In this paper, we present a design and implementation of an automatic video restoration system for line scratches observed in archived film. We use autoregressive (AR) image model because we can make stochastic and specifically autoregressive image generation process with our PAST-PRESENT model and Sampling Pattern. We designed locality maximizing scanning pattern, which can generate nearly stationary time-like series of pixels, which is a strong requirement for a stochastic series to be autoregressive. The sampled pixel series undergoes filtering and model fitting using Durbin-Levinson algorithm before interpolation process. We designed three-stage film restoration system, which includes (1) film acquisition from VHS tapes, (2) simple line scratch detection and restoration, and (3) manual blob identification and sophisticated inpainting scheme. We implemented film acquisition and simple inpainting scheme on Texas Instruments DSP board TMS320DM642 EVM, and implemented our AR inpainting scheme on PC for sophisticated restoration. We experimented our scheme with two old Korean films: "Viva Freedom" and "Robot Tae-Kwon-V", and the experimental results show that our scheme improves Bertalmio's scheme for subjective quality (MOS), objective quality (PSNR), and especially restoration ratio (RR), which reflects how much similar to the manual inpainting results.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.18 no.4
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• pp.34-40
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• 2004

In this paper, we have studied the OCT(Optical Coherence Tomography) system which has been advantages of high resolution, 2-D cross-sectional images, low cost and small size configuration. The characteristics of light source determine the resolution and coherence length. The light source has a commercial SLD with a central wavelength of 1,285 ill11, 35.3 nm(FWHM). The optical delay line is necessary to make equal with the optical path length to scattered light or reflected light from a sample. In order to make equal the optical path length, the stage that is attached to a reference mirror is controled by a step motor. And the interferometer is configured with the Michelson interferometer by using a single mode fiber, and the scanner can be focused on the sample by using a reference ann Also, the 2-dimension cross-sectional images were measured with scanning the transverse direction of the sample by using a step motor. After detecting the internal signal of lateral direction, a scanner is moved to obtain the cross-sectional image of 2-dimension by using step motor. A photodiode, which has high detection sensitivity and excellent noise characteristics has been used. The detected small signal has a noise and interference. After filtering and amplifying the signal, the output signal is demodulated the waveform And then, a cross-sectional image is seen through converting this signal into a digitalized signal by using an AID converter. The resolution of the sample is about 30${\mu}{\textrm}{m}$, which corresponds to the theoretical resolution. Also, the cross-sectional images of onion cells were measured in real time scheme.