• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2004.05b
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• pp.587-590
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• 2004

In this paper proposed a HMM(Hidden Martov Model)-based segmentation method which is able to model shadows as well as foreground and background regions. Shadow of moving objects often obstruct visual tracking. We propose an HMM-based segmentation method which classifies in real time oath objects. In the case of traffic monitoring movies, the effectiveness of the proposed method has been proven through experimental results

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.274-274
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• 2002

Damage to forest trees, caused by wildfire, changes their spectral reflectance signature. This factor led to the initiation of a research project at the Remote Sensing & GIS Laboratory, Kookmin University, to determine if multispectral data acquired by IKONOS could provide fire scar and bum severity mapping. This paper will present detail mapping of burned areas in the eastern coast of Korea with IKONOS imagery. In addition, a single post-burn Landsat-7 ETM＋ data was used to compare with IKONOS, the study area. Burn severity map based on IKONOS image was found to be affected by strong topographic illumination effects in the mountain forest. But it has better the delineation of the bum-scarred area. In this study the NDVI was analyzed for geometric illumination conditions influenced by topography(slop, aspect and elevation) and shadow(solar elevation and azimuth angle).

• Journal of Welding and Joining
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• v.22 no.3
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• pp.45-49
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• 2004

Generally, it is used the compensation spring to compensate the inaccuracy of screen image induced by thermal deformation in CRT monitor. Its mechanism is hi-metallic system made of heterogeneous metals and these springs are bonded by laser welding. But laser welding induces a non-uniform temperature distribution, and residual stress is yielded locally by these temperature deviation. Therefore, this study measures the curvature constant to assess functions of the compensation spring of shadow mask with respect to increment temperature and estimates the effect of residual stress on the performance of tri-metal used to compensation spring.

• International Journal of Contents
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• v.12 no.2
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• pp.1-5
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• 2016

Extensible 3D (X3D) is the ISO standard for defining 3D interactive web- and broadcast-based 3D content integrated with multimedia. With the advent of this integration of interactive 3D graphics into the web, users can easily produce 3D scenes within web contents. Even though there are diverse texture nodes in X3D, projective textures are not provided. We enable X3D to provide SingularProjectiveTexture and MultiProjectiveTexture nodes by materializing independent nodes of projector nodes for a singular projector and multi-projector. Our approach takes the creation of an independent projective texture node instead of Kamburelis's method, which requires inconvenient and duplicated specifications of two nodes, ImageTexture and Texture Coordinate.

• Journal of Korean Society for Geospatial Information Science
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• v.17 no.3
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• pp.71-79
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• 2009

We expanded the 3D building information extraction method using shadow and vertical line from single high resolution image with meta information into the method for single high resolution image without meta information. Our method guesses an azimuth angle and an elevation angle of the sensor and the sun using reference building, selected by user, on an image. For test, we used an IKONOS image and an image extracted from the Google Earth. We calculated the Root Mean Square (RMS) error of heights extracted by our method using the building height extracted from stereo IKONOS image as reference, and the RMS error from the IKONOS image and the Google Earth image was under than 3 m. We also calculated the RMS error of horizontality position by comparison between building position extracted from only the IKONOS image and it from 1:1,000 digital map, and the result was under than 3 m. This test results showed that the height pattern of building models by our method was similar with it by the method using meta information.

• Journal of the Korean earth science society
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• v.39 no.3
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• pp.241-249
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• 2018

As the international trade increases, vessel traffics around the Korean Peninsula are also increasing. Maritime accidents hence take place more frequently in the southern coast of Korea where many big and small ports are located. Accidents involving ship collision and sinking result in a substantial human and material damage as well as the marine environmental pollution. Therefore, it is necessary to locate the ships quickly when such accidents occur. In this study, we suggest a new ship detection index by comparing and analyzing the reflectivity of each channel of the Korea MultiPurpose SATellite-2 (KOMPSAT-2) images of the area around the Gwangyang Bay. A threshold value of 0.1 is set based on a histogram analysis, and all vessels are detected when compared with RGB composite images. After selecting a relatively large ship as a representative sample, the distribution of spatial reflectivity around the ship is studied. Uniform shadows are detected on the northwest side of the vessel. This indicates that the sun is in the southeast, the azimuth of the actual satellite image is $144.80^{\circ}$, and the azimuth angle of the sun can be estimated using the shadow position. The reflectivity of the shadows is 0.005 lower than the surrounding sea and ship. The shadow height varies with the position of the bow and the stern, perhaps due to the relative heights of the ship deck and the structure. The results of this study can help search technology for missing vessels using optical satellite images in the event of a marine accident around the Korean Peninsula.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.2
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• pp.133-140
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• 2017

In this paper, we has proposed an automatic vehicle monitoring system based on computer vision technology. The real-time display system has displayed a system that can be performed in automatic monitoring and control while meeting the essential requirements of ITS. Another advantage has that for a powerful vehicle tracking, the main obstacle handing system, which has the shadow tracking of moving objects. In order to obtain all kinds of information from the tracked vehicle image, the vehicle must be clearly displayed on the surveillance screen. Over time, it's necessary to precisely control the vehicle, and a three-dimensional model-based approach has been also necessary. In general, each type of vehicle has represented by the skeleton of the object or wire frame model, and the trajectory of the vehicle can be measured with high precision in a 3D-based manner even if the system has not running in real time. In this paper, we has applied on segmentation method to vehicle, background, and shadow. The validity of the low level vehicle control tracker was also detected through speed tracking of the speeding car. In conclusion, we intended to improve the improved tracking method in the tracking control system and to develop the highway monitoring and control system.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1149-1161
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• 2021

With a trend of the utilization of computer vision for satellite images, cloud detection using deep learning also attracts attention recently. In this study, we conducted a U-Net cloud detection modeling using SPARCS (Spatial Procedures for Automated Removal of Cloud and Shadow) Cloud Dataset with the image data augmentation and carried out 10-fold cross-validation for an objective assessment of the model. Asthe result of the blind test for 1800 datasets with 512 by 512 pixels, relatively high performance with the accuracy of 0.821, the precision of 0.847, the recall of 0.821, the F1-score of 0.831, and the IoU (Intersection over Union) of 0.723. Although 14.5% of actual cloud shadows were misclassified as land, and 19.7% of actual clouds were misidentified as land, this can be overcome by increasing the quality and quantity of label datasets. Moreover, a state-of-the-art DeepLab V3+ model and the NAS (Neural Architecture Search) optimization technique can help the cloud detection for CAS500 (Compact Advanced Satellite 500) in South Korea.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1343-1356
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• 2022

Clouds or shadows are the most problematic when monitoring crops using optical satellite images. To reduce this effect, a composite algorithm was used to select the maximum Normalized Difference Vegetation Index (NDVI) for a certain period. This Maximum NDVI Composite (MNC) method reduces the influence of clouds, but since only the maximum NDVI value is used for a certain period, it is difficult to show the phenomenon immediately when the NDVI decreases. As a way to maintain the spectral information of crop as much as possible while minimizing the influence of clouds, a Score-Based Composite (SBC) algorithm was proposed, which is a method of selecting the most suitable pixels by defining various environmental factors and assigning scores to them when compositing. In this study, the Sentinel-2A/B Level 2A reflectance image and cloud, shadow, Aerosol Optical Thickness(AOT), obtainging date, sensor zenith angle provided as additional information were used for the SBC algorithm. As a result of applying the SBC algorithm with a 15-day and a monthly period for Dangjin rice fields and Taebaek highland cabbage fields in 2021, the 15-day period composited data showed faster detailed changes in NDVI than the monthly composited results, except for the rainy season affected by clouds. In certain images, a spatially heterogeneous part is seen due to partial date-by-date differences in the composited NDVI image, which is considered to be due to the inaccuracy of the cloud and shadow information used. In the future, we plan to improve the accuracy of input information and perform quantitative comparison with MNC-based composite algorithm.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.179-184
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• 2009

Robust face recognition under various illumination environments is difficult to achieve. For face recognition robust to illumination changes, usually face images are normalized with respect to illumination as a preprocessing step before face recognition. The anisotropic smoothing-based illumination normalization method, known to be one of the best illumination normalization methods, cannot handle casting shadows. In this paper, we present an efficient illumination normalization method for face recognition. The proposed illumination normalization method separates the effect of illumination from eigenfaces and constructs an illumination-separated eigenface subspace. Then, an incoming face image is projected into the subspace and the obtained projected face image is rendered so that illumination effects including casting shadows are reduced as much as possible. Application to real face images shows the proposed illumination normalization method.