• Journal of Institute of Control, Robotics and Systems
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• v.22 no.12
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• pp.1046-1052
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• 2016

For the safe driving of autonomous vehicles, accurate position estimation is required. Generally, position error must be less than 1m because of lane keeping. However, GPS positioning error is more than 1m. Therefore, we must correct this error and a map matching algorithm is generally used. Especially, road marking intensity map have been used in many studies. In previous work, 3D LIDAR with many vertical layers was used to generate a local intensity map. Because it can be obtained sufficient longitudinal information for map matching. However, it is expensive and sufficient road marking information cannot be obtained in rush hour situations. In this paper, we propose a localization algorithm using an accumulated intensity local map. An accumulated intensity local map can be generated with sufficient longitudinal information using 3D LIDAR with a few vertical layers. Using this algorithm, we can also obtain sufficient intensity information in rush hour situations. Thus, it is possible to increase the reliability of the map matching and get accurate position estimation result. In the experimental result, the lateral RMS position error is about 0.12m and the longitudinal RMS error is about 0.19m.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2003.03a
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• pp.23-29
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• 2003

We have developed a Web-based intensity map generation system. We describe here briefly our system that is automatically generating seismic intensity map using the informations afforded by Internet users who actually experience recent earthquake. Whenever internet survey questionnaires, reported from the citizens using the form available through our Web site, are received, the associated ZIP code region is color-coded according to the Modified Mercalli Intensity Scale. As the number of questionnaires is increased, the intensity value of each region divided by ZIP code is averaged and updated automatically. The coverage of acceleration stations is insufficient to map the accurate extent of ground shaking in Korea. So our system will serve as an alternative for much more rapid generation of intensity map than conventional method, such as mailing intensity survey or elaborate manual processing.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 1997.04a
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• pp.21-26
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• 1997

For 262 locations throughout the southern part of Korean peninsula, intensities of 13 December 1996 Yeongweol earthquake are estimated to make an isoseismal map and investigate attenuation properties in the southern part of Korea. Due to the inherent uncertainties in the estimation of intensities, obtained intensity map show quite scattered pattern of intensity distribution. Estimated intensities range from III to possibly Ⅷ. In case of intensity larger than Ⅵ, considerable damages such as fracturing of walls are reported one of the most significant feature of the intensity map is, considering its magnitude 4.7 reported by KMA, the felt area is appeared to be unusually large covering most of the Korean peninsula except Cheju island. This result indicates ether the magnitude is underestimated or the focus of this earthquake is much deeper. Assuming shallow(less than 10km) intraplate earthquake, we obtained average magnitude 5.6 by using the area encircled by isoseismal contour lines from intensity IV to intensity Ⅶ. This ambiguity can be clarified if more reliable focal depth is estimated by using teleseismic earthquake records in the future.

• Korean Journal of Remote Sensing
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• v.23 no.5
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• pp.455-464
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• 2007

Lee(2007) suggested the Point-Jacobian iteration MAP estimation(PJIMAP) for noise removal of the images that are corrupted by multiplicative speckle noise. It is to find a MAP estimation of noisy-free imagery based on a Bayesian model using the lognormal distribution for image intensity and an MRF for image texture. When the image intensity is logarithmically transformed, the speckle noise is approximately Gaussian additive noise, and it tends to a normal probability much faster than the intensity distribution. The MRF is incorporated into digital image analysis by viewing pixel types as states of molecules in a lattice-like physical system. In this study, the MAP estimation is computed by the Point-Jacobian iteration using adaptive parameters. At each iteration, the parameters related to the Bayesian model are adaptively estimated using the updated information. The results of the proposed scheme were compared to them of PJIMAP with SAR simulation data generated by the Monte Carlo method. The experiments demonstrated an improvement in relaxing speckle noise and estimating noise-free intensity by using the adaptive parameters for the Ponit-Jacobian iteration.

• Progress in Medical Physics
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• v.13 no.1
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• pp.32-36
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• 2002

For Intensity Modulated Radiation Therapy(IMRT), the spatial resolution of intensity map(IM) is limited by the width of multi-leaf collimator, which would make an effect on the conformity of the target, as well as organs at risk. Several Methods are suggested to increase the spatial resolution, which can be categorized by the hardware-dependent technique and the software-based technique. However the best solution might be to make the width of MLC finer. it has several obstacles in the respects of technical difficulty and cost. This preliminary study is designed to investigate the clinical effectiveness of the virtual-micro IMRT(VMIMRT) technique, one of the software-based technique. A particular intensity map was created, which has 42$\times$54 pixel dimension ,0.5cm pixel size and 15 intensity levels. Using this intensity map, segment fields of IMRT were generated with 1$\times$lcm, 0.5$\times$1cm, 0.5$\times$0.5cm(VMIM) beamlet size, respectively As results, we found that there was no evidence of improvement for VMIMRT, compared with the 0.5$\times$lcm beamlet size which can be delivered by 1cm width MLC. The reason seems to be due to the constraint of VMIMRT. Further study is required to prove the benefit of the VIMRT in clinical case like head and neck cancer, where is expected that higher resolution than 1cm is necessary.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.9
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• pp.95-103
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• 1998

This paper proposes an image segmentation algorithm based on fusion of range and intensity images. Based on Bayesian theory, a priori knowledge is encoded by the Markov random field (MRF). A maximum a posteriori (MAP) estimator is constructed using the features extracted from range and intensity images. Objects are approximated by local planar surfaces in range images, and the parametric space is constructed with the surface parameters estimated pixelwise. In intensity images the ${\alpha}$-trimmed variance constructs the intensity feature. An image is segmented by optimizing the MAP estimator that is constructed using a likelihood function based on edge information. Computer simulation results shw that the proposed fusion algorithm effectively segments the images independentl of shadow, noise, and light-blurring.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.93-96
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• 2015

The aim of the estimation of disparity map is to find the corresponding pixels from similar two or more images. However, it is a difficult problem to get precise and consistent disparity under a variety of real world situations. In other words, the color values of stereo images are easily influenced by radiometric properties such as illumination direction, illumination color, and camera exposure. Therefore, conventional stereo matching methods can have low performances under radiometric conditions. In this paper, we propose an approaching of disparity map estimation that is reliable in controlling various radiometric variations close to the real environment. This method is motivated by following constancy. Even though each other has different radiometric property in stereo images, intensity of pixels of object have general constancy in specific block. Experimental results show that the proposed method has better performances compared to the comparison group under different radiometric conditions between stereo images. Consequentially, the proposed method is able to estimate the disparity map in stable under various radiometric variations.

• Journal of Korea Multimedia Society
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• v.19 no.3
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• pp.626-632
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• 2016

In this paper, we propose the defect emphasis in TFT-LCD panel image. The defect emphasis image consist of S(Shape) map and B(Brightness) map. S map based on DoG(difference of gaussian) is made with the mura defect shape characteristic. And B map use defect intensity property that defect intensity is higher than background. The experiments were conducted to evaluate the performance of the proposed defect emphasis method. The results of experiments show the validity of the defect emphasis using the proposed method.

• The Journal of Korea Robotics Society
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• v.16 no.3
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• pp.189-198
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• 2021

In this paper, we introduce the software/hardware system that can reliably calculate the distance from sensor to the model regardless of point cloud density. As the 3d point cloud map is widely adopted for SLAM and computer vision, the accuracy of point cloud map is of great importance. However, the 3D point cloud map obtained from Lidar may reveal different point cloud density depending on the choice of sensor, measurement distance and the object shape. Currently, when measuring map accuracy, high reflective bands are used to generate specific points in point cloud map where distances are measured manually. This manual process is time and labor consuming being highly affected by Lidar sparsity level. To overcome these problems, this paper presents a hardware design that leverage high intensity point from three planar surface. Furthermore, by calculating distance from sensor to the device, we verified that the automated method is much faster than the manual procedure and robust to sparsity by testing with RGB-D camera and Lidar. As will be shown, the system performance is not limited to indoor environment by progressing the experiment using Lidar sensor at outdoor environment.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.2
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• pp.195-206
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• 2016

The remote sensing technique using SAR data have many advantages when applied to the disaster site due to its wide coverage and all-weather acquisition availability. Although a single-pol (polarimetric) SAR image cannot represent the land surface better than a quad-pol SAR image can, single-pol SAR data are worth using for disaster-induced change detection. In this paper, an automatic change detection method based on a mixture of GGDs (generalized Gaussian distribution) is proposed, and usability of the textural features and intensity is evaluated by using the proposed method. Three ALOS/PALSAR images were used in the experiments, and the study site was Norita City, which was affected by the 2011 Tohoku earthquake. The experiment results showed that the proposed automatic change detection method is practical for disaster sites where the large areas change. The intensity information is useful for detecting disaster-induced changes with a 68.3% g-mean, but the texture information is not. The autocorrelation and correlation show the interesting implication that they tend not to extract agricultural areas in the change detection map. Therefore, the final tsunami-induced change map is produced by the combination of three maps: one is derived from the intensity information and used as an initial map, and the others are derived from the textural information and used as auxiliary data.