• Proceedings of the KSME Conference
• /
• 2005.11a
• /
• pp.203.1-203.1
• /
• 2005

• Proceedings of the KSRS Conference
• /
• 2005.10a
• /
• pp.709-712
• /
• 2005

Ground processing for geostationary weather satellite such as GOES, MTSAT includes the process called image navigation. Image navigation means the retrieval of satellite navigational parameters from images and requires landmark detection by matching satellite images against landmark chips. For an automated preprocessing, a matching must be performed automatically. However, if match results contain errors, the accuracy of image navigation deteriorates. To overcome this problem, we propose the use of a robust estimation technique, called Random Sample Consensus (RANSAC), to automatically detect mismatches. We tested GOES-9 satellite images with 30 landmark chips. Landmark chips were extracted from the world shoreline database. To them, matching was applied and mismatch results were detected automatically by RANSAC. Results showed that all mismatches were detected correctly by RANSAC with a threshold value of 2.5 pixels.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.63 no.8
• /
• pp.1104-1110
• /
• 2014

This paper investigates geometric effect of landmarks on three-dimensional navigation error in landmark-based vision navigation systems. Dilution of precision is derived for landmark measurement error on the focal plane of the camera and separately expressed in position DOP and attitude DOP. Values of DOP are observed for various configurations of landmarks.

• Proceedings of the KIEE Conference
• /
• 2001.11c
• /
• pp.91-94
• /
• 2001

In this paper, we proposed localization method of mobile robot using color landmark mounted on ceiling. This work is composed 2 parts : landmark recognition part which finds the position of multiple landmarks in image and identifies them and absolute position estimation part which estimates the location and orientation of mobile robot in indoor environment. In landmark recognition part, mobile robot detects artificial color landmarks using simple histogram intersection method in rg color space which is insensitive to the change of illumination. Then absolute position estimation part calculates relative position of the mobile robot to the detected landmarks. For the verification of proposed algorithm, ceiling-orientated camera was installed on a mobile robot and performance of localization was examined by designed artificial color landmarks. As the result of test, mobile robot could achieve the reliable landmark detection and accurately estimate the position of mobile robot in indoor environment.

• Journal of Institute of Control, Robotics and Systems
• /
• v.16 no.4
• /
• pp.368-373
• /
• 2010

A new indoor mobile robot localization method is presented. Robot recognizes well designed single color landmarks on the ceiling by vision system, as reference to compute its precise position. The proposed likelihood prediction based method enables the robot to estimate its position based only on the orientation of landmark.The use of single color landmarks helps to reduce the complexity of the landmark structure and makes it easily detectable. Edge based optical flow is further used to compensate for some landmark recognition error. This technique is applicable for navigation in an unlimited sized indoor space. Prediction scheme and localization algorithm are proposed, and edge based optical flow and data fusing are presented. Experimental results show that the proposed method provides accurate estimation of the robot position with a localization error within a range of 5 cm and directional error less than 4 degrees.

• Journal of International Society for Simulation Surgery
• /
• v.3 no.1
• /
• pp.9-11
• /
• 2016

Purpose In this paper we proposed bi-directional hierarchical regression for accurate human finger landmark detection with only using depth information.Materials and Methods Our algorithm consisted of two different step, initialization and landmark estimation. To detect initial landmark, we used difference of random pixel pair as the feature descriptor. After initialization, 16 landmarks were estimated using cascaded regression methods. To improve accuracy and stability, we proposed bi-directional hierarchical structure.Results In our experiments, the ICVL database were used for evaluation. According to our experimental results, accuracy and stability increased when applying bi-directional hierarchical regression more than typical method on the test set. Especially, errors of each finger tips of hierarchical case significantly decreased more than other methods.Conclusion Our results proved that our proposed method improved accuracy and stability and also could be applied to a large range of applications such as augmented reality and simulation surgery.

• Journal of Korea Multimedia Society
• /
• v.24 no.8
• /
• pp.1020-1025
• /
• 2021

Facial alignment is very important task for human life. And facial landmark detection is one of the instrumental methods in face alignment. We introduce the stacked hourglass networks with transposed convolutional layers for facial landmark detection. our method substitutes nearest neighbor upsampling for transposed convolutional layer. Our method returns better accuracy in facial landmark detection compared to stacked hourglass networks with nearest neighbor upsampling.

• International conference on construction engineering and project management
• /
• 2009.05a
• /
• pp.573-578
• /
• 2009

Previous research on super high rise buildings focused mostly on the use of public space from building plan perspective, survey of residents' satisfaction evaluation, construction technology and structural technology. But little research is done on the economic analysis of landmark factors. The purpose of this study is to find landmark factors that can be quantitatively measured, collect data on super high rise residential buildings in Seoul. Find the intrinsic values of the landmarks, and analyze how these values differ in areas with different densities, i.e. in 3 Gangnam-gus & Yeongdeungpo-gu and in other areas. It is expected that the results of this study can be used to set an appropriate price of super high rise building in consideration of its landmark value in different area

• Journal of Positioning, Navigation, and Timing
• /
• v.9 no.3
• /
• pp.285-292
• /
• 2020

This paper introduces a new computational efficient Dilution of Precision (DOP)-based landmark exclusion method while ensuring the safety of the LiDAR-based navigation system that uses an innovation-based Nearest-Neighbor (NN) Data Association (DA) process. The NN DA process finds a correct landmark association hypothesis among all potential landmark permutations using Kalman filter innovation vectors. This makes the computational load increases exponentially as the number of landmarks increases. In this paper, we thus exclude landmarks by introducing DOP that quantifies the geometric distribution of landmarks as a way to minimize the loss of integrity performance that can occur by reducing landmarks. The number of landmarks to be excluded is set as the maximum number that can satisfy the integrity risk requirement. For the verification of the method, we developed a simulator that can analyze integrity risk according to the landmark number and its geometric distribution. Based on the simulation, we analyzed the relationship between DOP and integrity risk of the DA process by excluding each landmark. The results showed a tendency to minimize the loss of integrity performance when excluding landmarks with poor DOP. The developed method opens the possibility of assuring the safety risk of the Lidar-based navigation system in real-time applications by reducing a substantial amount of computational load.

• Journal of Institute of Control, Robotics and Systems
• /
• v.10 no.1
• /
• pp.81-86
• /
• 2004

This paper describes self-localization of a mobile robot from the multiple candidates of landmarks in outdoor environment. Our robot uses omnidirectional vision system for efficient self-localization. This vision system acquires the visible information of all direction views. The robot uses feature of landmarks whose size is bigger than that of others in image such as building, sculptures, placard etc. Robot uses vertical edges and those merged regions as the feature. In our previous work, we found the problem that landmark matching is difficult when selected candidates of landmarks belonging to region of repeating the vertical edges in image. To overcome these problems, robot uses the merged region of vertical edges. If interval of vertical edges is short then robot bundles them regarding as the same region. Thus, these features are selected as candidates of landmarks. Therefore, the extracted merged region of vertical edge reduces the ambiguity of landmark matching. Robot compares with the candidates of landmark between previous and current image. Then, robot is able to find the same landmark between image sequences using the proposed feature and method. We achieved the efficient self-localization result using robust landmark matching method through the experiments implemented in our campus.