• Korean Journal of Remote Sensing
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• v.34 no.5
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• pp.739-747
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• 2018

Integration analysis of multi-sensor satellite images is becoming increasingly important. The first step in integration analysis is image registration between multi-sensor. SIFT (Scale Invariant Feature Transform) is a representative image registration method. However, optical image and SAR (Synthetic Aperture Radar) images are different from sensor attitude and radiation characteristics during acquisition, making it difficult to apply the conventional method, such as SIFT, because the radiometric characteristics between images are nonlinear. To overcome this limitation, we proposed a modified method that combines the SAR-SIFT method and shape descriptor vector DLSS(Dense Local Self-Similarity). We conducted an experiment using two pairs of Cosmo-SkyMed and KOMPSAT-2 images collected over Daejeon, Korea, an area with a high density of buildings. The proposed method extracted the correct matching points when compared to conventional methods, such as SIFT and SAR-SIFT. The method also gave quantitatively reasonable results for RMSE of 1.66m and 2.45m over the two pairs of images.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.5
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• pp.934-940
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• 2003

In this paper, hair beauty fashion design feature points detection system is proposed. A hair models and hair face is represented as a graph where the nodes are placed at facial feature points labeled by their Gabor features and the edges are describes their spatial relations. An innovative flexible feature matching is proposed to perform features correspondence between hair models and the input image. This matching hair model works like random diffusion process in the image space by employing the locally competitive and globally corporative mechanism. The system works nicely on the face images under complicated background. pose variations and distorted by accessories. We demonstrate the benefits of our approach by its implementation on the face identification system.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.27 no.5
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• pp.585-597
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• 2009

This research aims to develop a registration method to remove the geometric inconsistency between aerial images and LIDAR data acquired from an airborne multi-sensor system. The proposed method mainly includes registration primitives extraction, correspondence establishment, and EOP(Exterior Orientation Parameters) adjustment. As the registration primitives, we extracts planar patches and intersection edges from the LIDAR data and object points and linking edges from the aerial images. The extracted primitives are then categorized into horizontal and vertical ones; and their correspondences are established. These correspondent pairs are incorporated as stochastic constraints into the bundle block adjustment, which finally precisely adjusts the exterior orientation parameters of the images. According to the experimental results from the application of the proposed method to real data, we found that the attitude parameters of EOPs were meaningfully adjusted and the geometric inconsistency of the primitives used for the adjustment is reduced from 2 m to 2 cm before and after the registration. Hence, the results of this research can contribute to data fusion for the high quality 3D spatial information.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.6
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• pp.24-34
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• 2007

Application systems that easy to access a information have been developed by IT growth and a human life variation. In this paper, we propose a application system to register a 3D footwear model using a monocular camera. In General, a human motion analysis research to body movement. However, this system research a new method to use a foot movement. This paper present a system process and show experiment results. For projection to 2D foot plane from 3D shoe model data, we construct processes that a foot tracking, a projection expression and pose estimation process. This system divide from a 2D image analysis and a 3D pose estimation. First, for a foot tracking, we propose a method that find fixing point by a foot characteristic, and propose a geometric expression to relate 2D coordinate and 3D coordinate to use a monocular camera without a camera calibration. We make a application system, and measure distance error. Then, we confirmed a registration very well.

• Journal of Advanced Navigation Technology
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• v.19 no.5
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• pp.389-394
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• 2015

This paper considers the transfer alignment in the inertial navigation system which has lever-arm and the time delay in the velocity measurement. We suggest a method to improve the performance of the velocity matching. First, we analyze the estimation performance of the velocity matching through the tuning of the two covariance matrices of process noise and measurement noise. Next we provide some maneuvering conditions of the vehicles to improve the estimation performance using the observability analysis. The analysis results are verified using the computer simulations, which show that cruise movements do not provide the azimuth estimation of the vehicles, while east or north accelerating movement can provide.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.3 no.2
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• pp.12-17
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• 2010

An automatic facial feature points(FFPs) detection system is proposed. A face is represented as a graph where the nodes are placed at facial feature points(FFPs) labeled by their Gabor features and the edges are describes their spatial relations. An innovative flexible feature matching is proposed to perform features correspondence between models and the input image. This matching model works likes random diffusion process in the image space by employing the locally competitive and globally corporative mechanism. The system works nicely on the face images under complicated background, pose variations and distorted by facial accessories. We demonstrate the benefits of our approach by its implementation on the system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.4
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• pp.705-711
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• 2003

An automatic face and facial feature points(FFPs) detection system is proposed. A face is represented as a graph where the nodes are placed at facial feature points(FFPs) labeled by their Gabor features and the edges are describes their spatial relations. An innovative flexible feature matching is proposed to perform features correspondence between models and the input image. This matching model works likes random diffusion process in !be image space by employing the locally competitive and globally corporative mechanism. The system works nicely on the face images under complicated background, pose variations and distorted by facial accessories. We demonstrate the benefits of our approach by its implementation on the face identification system.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.05a
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• pp.608-612
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• 2002

An automatic face and facial feature points(FFPs) detection system is proposed. A face is represented as a graph where the nodes are placed at facial feature points(FFPs) labeled by their Gabor features md the edges are describes their spatial relations. An innovative flexible feature matching is proposed to perform features correspondence between models and the input image. This matching model works likes random diffusion process in the image spare by employing the locally competitive and globally corporative mechanism. The system works nicely on the face images under complicated background, pose variations and distorted by facial accessories. We demonstrate the benefits of our approach by its implementation on the fare identification system.

• Journal of Korean Society for Geospatial Information Science
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• v.21 no.4
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• pp.101-107
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• 2013

Generally the commercial high-resolution images have their coordinates, but the locations are locally different according to the pose of sensors at the acquisition time and relief displacement of terrain. Therefore, a process of image co-registration has to be applied to use the multi-temporal images together. However, co-registration is interrupted especially when images include the cloud-covered regions because of the difficulties of extracting matching points and lots of false-matched points. This paper proposes an automatic co-registration method for the cloud-covered high-resolution images. A scale-invariant feature transform (SIFT), which is one of the representative feature-based matching method, is used, and only features of the target (cloud-covered) images within a circular buffer from each feature of reference image are used for the candidate of the matching process. Study sites composed of multi-temporal KOMPSAT-2 images including cloud-covered regions were employed to apply the proposed algorithm. The result showed that the proposed method presented a higher correct-match rate than original SIFT method and acceptable registration accuracies in all sites.

• The Journal of Korean Society for Radiation Therapy
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• v.21 no.1
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• pp.33-39
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• 2009

Purpose: The aim of this study is to compare patient's body posture and its position at the time of simulation with one at the treatment room using On-board Imaging (OBI) and CT (CBCT). The detected offsets are compared with position errors of Rando Phantom that are practically applied. After that, Rando Phantom's position is selected by moving couch based on detected deviations. In addition, the errors between real measured values of Rando Phantom position and theoretical ones is compared. And we will evaluate target position's accuracy of KV X-ray imaging's 2D and CBCT's 3D one. Materials and Methods: Using the Rando Phantom (Alderson Research Laboratories Inc. Stanford. CT, USA) which simulated human body's internal structure, we will set up Rando Phantom on the treatment couch after implementing simulation and RTP according to the same ways as the real radioactive treatment. We tested Rando Phantom that are assumed to have accurate position with different 3 methods. We measured setup errors on the axis of X, Y and Z, and got mean standard deviation errors by repeating tests 10 times on each tests. Results: The difference between mean detection error and standard deviation are as follows; lateral 0.4+/-0.3 mm, longitudinal 0.6+/-0.5 mm, vertical 0.4+/-0.2 mm which all within 0~10 mm. The couch shift variable after positioning that are comparable to residual errors are 0.3+/-0.1, 0.5+/-0.1, and 0.3+/-0.1 mm. The mean detection errors by longitudinal shift between 20~40 mm are 0.4+/-0.3 in lateral, 0.6+/-0.5 in longitudinal, 0.5+/-0.3 in vertical direction. The detection errors are all within range of 0.3~0.5 mm. Residual errors are within 0.2~0.5 mm. Each values are mean values based on 3 tests. Conclusion: Phantom is based on treatment couch shift and error within the average 5mm can be gained by the diminution detected by image registration based on OBI and CBCT. Therefore, the selection of target position which depends on OBI and CBCT could be considered as useful.