• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
• /
• 2006.04a
• /
• pp.181-185
• /
• 2006

Image co-registration is the process of overlaying two images of the same scene, one of which represents a reference image, while the other is geometrically transformed to the one. In order to improve efficiency and effectiveness of the co-registration approach, the author proposed a pre-qualified area matching algorithm which is composed of feature extraction with canny operator and area matching algorithm with cross correlation coefficient. For refining matching points, outlier detection using studentized residual was used and iteratively removes outliers at the level of three standard deviation. Throughout the pre-qualification and the refining processes, the computation time was significantly improved and the registration accuracy is enhanced. A prototype of the proposed algorithm was implemented and the performance test of 3 Landsat images of Korea showed: (1) average RMSE error of the approach was 0.436 Pixel (2) the average number of matching points was over 38,475 (3) the average processing time was 489 seconds per image with a regular workstation equipped with a 3 GHz Intel Pentium 4 CPU and 1 Gbytes Ram. The proposed approach achieved robustness, full automation, and time efficiency.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.35 no.11C
• /
• pp.896-900
• /
• 2010

We investigate the bit error rate against signal-to-noise ratio performance corresponding to track mis-registration for patterned media storage. The patterned media channels with and without soft underlayer are implemented, and we simulate using one-dimensional Viterbi detector and two-dimensional soft output Viterbi detector (SOVA) when the track mis-registration is 0% (on-track), 10%, 20%, 30%, and 40%. While the BER performance degrades approximate 0.3 ~ 0.5 dB at 10% track mis-registration, it degrades severe over 10% track mis-registration.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.18 no.6
• /
• pp.303-310
• /
• 2023

A single vision system limits the ability to accurately understand the spatial constraints and interactions between robots and dynamic workers caused by gantry robots and collaborative robots during production manufacturing. In this paper, we propose a 3D pose registration method for dynamic workers based on a multi-domain vision system for safety monitoring in manufacturing environments. This method uses OpenPose, a deep learning-based posture estimation model, to estimate the worker's dynamic two-dimensional posture in real-time and reconstruct it into three-dimensional coordinates. The 3D coordinates of the reconstructed multi-domain vision system were aligned using the ICP algorithm and then registered to a single 3D coordinate system. The proposed method showed effective performance in a manufacturing process environment with an average registration error of 0.0664 m and an average frame rate of 14.597 per second.

• Journal of the Institute of Convergence Signal Processing
• /
• v.9 no.1
• /
• pp.18-23
• /
• 2008

In this paper, we propose the input image selection-method to improve the reconstructed high-resolution (HR) image quality. To obtain ideal super-resolution (SR) reconstruction image, all input images are well-registered. However, the registration is not ideal in practice. Due to this reason, the selection of input images with low registration error (RE) is more important than the number of input images in order to obtain good quality of a HR image. The suitability of a candidate input image can be determined by using statistical and restricted registration properties. Therefore, we propose the proper candidate input Low Resolution(LR) image selection-method as a pre-processing for the SR reconstruction in automatic manner. In video sequences, all input images in specified region are allowed to use SR reconstruction as low-resolution input image and/or the reference image. The candidacy of an input LR image is decided by the threshold value and this threshold is calculated by using the maximum motion compensation error (MMCE) of the reference image. If the motion compensation error (MCE) of LR input image is in the range of 0 < MCE < MMCE then this LR input image is selected for SR reconstruction, else then LR input image are neglected. The optimal reference LR (ORLR) image is decided by comparing the number of the selected LR input (SLRI) images with each reference LR input (RLRI) image. Finally, we generate a HR image by using optimal reference LR image and selected LR images and by using the Hardie's interpolation method. This proposed algorithm is expected to improve the quality of SR without any user intervention.

• Korean Journal of Remote Sensing
• /
• v.34 no.6_3
• /
• pp.1351-1367
• /
• 2018

Image registration is an essential process for image fusion, change detection and time series analysis using multi-sensor images. For this purpose, we need to detect accurately the difference of scale and rotation between the multi-sensor images with difference spatial resolution. In this paper, we propose a new feature matching method using variable circular template for image registration between multi-resolution images. The proposed method creates a circular template at the center of a feature point in a coarse scale image and also a variable circular template in a fine scale image, respectively. After changing the scale of the variable circular template, we rotate the variable circular template by each predefined angle and compute the mutual information between the two circular templates and then find the scale, the angle of rotation and the center location of the variable circular template, respectively, in fine scale image when the mutual information between the two circular templates is maximum. The proposed method was tested using Kompsat-2, Kompsat-3 and Kompsat-3A images with different spatial resolution. The experimental results showed that the error of scale factor, the error of rotation angle and the localization error of the control point were less than 0.004, $0.3^{\circ}$ and one pixel, respectively.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.23 no.6
• /
• pp.652-658
• /
• 2019

When IoT is applied to various research experiment fields such as physics, pharmacy, biology and medicine, it can increase the safety and convenience of researchers by intelligently monitoring and controlling research equipment and environment with various sensors and devices. For accurate and convenient record management and the research history and the basis but also for the reverse tracking, real-time reagent measurement and registration should be provided as a research support automation services. Currently, existing methods of reagent management are operated by computerized method, but reagent registration and management are not automated. And also record is managed manually, there are many hassles and problems such as a record error and too much time required for quantification and registration for many reagents. In this paper, we study a real time reagent measuring and registration method based on IoT to resolve the problems aforementioned, by the information of the reagent acquired by image recognition and NFC method.

• Journal of Korea Multimedia Society
• /
• v.14 no.3
• /
• pp.348-355
• /
• 2011

In this paper, we propose an automatic prostate segmentation method from dynamic magnetic resonance (MR) images. Our method detects contrast-enhanced images among the dynamic MR images using an average intensity analysis. Then, the candidate regions of prostate are detected by the B-spline non-rigid registration and subtraction between the pre-contrast and contrast-enhanced MR images. Finally, the prostate is segmented by performing a dilation operation outward, and sequential shape propagation inward. Our method was validated by ten data sets and the results were compared with the manually segmented results. The average volumetric overlap error was 6.8%, and average absolute volumetric measurement error was 2.5%. Our method could be used for the computer-aided prostate diagnosis, which requires an accurate prostate segmentation.

• The korean journal of orthodontics
• /
• v.27 no.4 s.63
• /
• pp.633-641
• /
• 1997

Spatial change of craniofacial structures from growth or orthopedic force is usually evaluated by cephalometric superimpositions at different points in time.As the evaluation cu be changed according to cephalometric superimposition method,selettion for correct superimposition method is very important.Double registration of nineteen pairs of female lateral head films,their growth period is average 3.7 years and age is overage 10.7 years,were performed by two observers.Comparisions was made between two methods and the results revealed the following, 1. NS line and ACB superimpositions ate not markedly different in reliability due to almost same registration error of them. 2. The investigation was undertaken to examine interobserver difference.In NS line method, there is no significance in all measurements. In ACB method,significant difference was revealed in 4 measurements of 7 measurements was 3. In the investigation of intermethod difference, there is no significance between NS line and ACB superimpositions

• Journal of Broadcast Engineering
• /
• v.24 no.5
• /
• pp.765-774
• /
• 2019

In this paper, we propose a point cloud matching algorithm for multiple RGB-D cameras. In general, computer vision is concerned with the problem of precisely estimating camera position. Existing 3D model generation methods require a large number of cameras or expensive 3D cameras. In addition, the conventional method of obtaining the camera external parameters through the two-dimensional image has a large estimation error. In this paper, we propose a method to obtain coordinate transformation parameters with an error within a valid range by using depth image and function optimization method to generate omni-directional three-dimensional model using 8 low-cost RGB-D cameras.

• The Journal of Korea Robotics Society
• /
• v.17 no.1
• /
• pp.8-15
• /
• 2022

In this paper, we proposed the method of virtual reality-based surgical navigation to reproduce the pre-planned position and angle of the pedicle screw in spinal fusion surgery. The goal of the proposed method is to quantitatively save the surgical plan by applying a virtual guide coordinate system and reproduce it in the surgical process through virtual reality. In the surgical planning step, the insertion position and angle of the pedicle screw are planned and stored based on the virtual guide coordinate system. To implement the virtual reality-based surgical navigation, a vision tracking system is applied to set the patient coordinate system and paired point-based patient-to-image registration is performed. In the surgical navigation step, the surgical plan is reproduced by quantitatively visualizing the pre-planned insertion position and angle of the pedicle screw using a virtual guide coordinate system. We conducted phantom experiment to verify the error between the surgical plan and the surgical navigation, the experimental result showed that target registration error was average 1.47 ± 0.64 mm when using the proposed method. We believe that our method can be used to accurately reproduce a pre-established surgical plan in spinal fusion surgery.