• Journal of Institute of Control, Robotics and Systems
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• v.18 no.12
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• pp.1115-1121
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• 2012

This paper proposes a method to extract accurate plane of an object in unstructured environment for a humanoid robot by using a laser scanner. By panning and tilting 2D laser scanner installed on the head of a humanoid robot, 3D depth map of unstructured environment is generated. After generating the 3D depth map around a robot, the proposed plane extraction method is applied to the 3D depth map. By using the hierarchical clustering method, points on the same plane are extracted from the point cloud in the 3D depth map. After segmenting the plane from the point cloud, dimensions of the planes are calculated. The accuracy of the extracted plane is evaluated with experimental results, which show the effectiveness of the proposed method to extract planes around a humanoid robot in unstructured environment.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.3
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• pp.277-282
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• 2011

This paper describes a sensor fusion-based semantic map building which can improve the capabilities of a mobile robot in various domains including localization, path-planning and mapping. To build a semantic map, various environmental information, such as doors and cliff areas, should be extracted autonomously. Therefore, we propose a method to detect doors, cliff areas and robust visual features using a laser scanner and a vision sensor. The GHT (General Hough Transform) based recognition of door handles and the geometrical features of a door are used to detect doors. To detect the cliff area and robust visual features, the tilting laser scanner and SIFT features are used, respectively. The proposed method was verified by various experiments and showed that the robot could build a semantic map autonomously in various indoor environments.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.3
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• pp.146-154
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• 2019

Recently, a system has been developed and applied to establish and utilize HD maps through R&D. The biggest problem, however, is the lack of a proper HD map update system, which requires the development and adoption of such a system as soon as possible. In addition, in the case of updating HD maps for automated driving, integrity and accuracy of maps are required for safe driving, so an test of these technologies and data quality is required. In April 2018, the Ministry of Land, Infrastructure and Transport implemented a project to 'Develop Technology to Demonstrate and Share the Instant Road Change Detection and Update Technology for automated driving. This paper analyzed the technology for updating map based on the investigation and analysis of relevant technology trends for the development of integrated demonstration and sharing technology of road change rapid detection and updating map technology, and put forward the criteria for road change rapid detection, integrated quality verification of update technology. It is expected that the results of this study will contribute to quality assurance of HD maps that support safety driving for automated vehicles.

• Journal of Software Assessment and Valuation
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• v.16 no.2
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• pp.99-107
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• 2020

An underground integrated map is being built for underground safety management and is being updated periodically. The map update proceeds with the procedure of deleting all previously stored objects and saving newly entered objects. However, even unchanged objects are repeatedly stored, deleted, and stored. That causes the delay of the update time. In this study, in order to shorten the update time of the integrated map, an updated object and an unupdated object are separated, and only updated objects are reflected in the underground integrated map, and a system implementing this technology is described. For the updated object, an object comparison method using the center point of the object is used, and a quad tree is used to improve the search speed. The types of updated objects are classified into addition and deletion using the shape of the object, and change using its attributes. The proposed system consists of update object detection, extraction, conversion, storage, and history management modules. This system has the advantage of being able to update the integrated map about four times faster than the existing method based on the data used in the experiment, and has the advantage that it can be applied to both ground and underground facilities.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2002.11a
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• pp.445-453
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• 2002

Past and current customer behavior is the best predicator of future customer behavior. This paper introduces a procedure on personalized defection detection and prevention for an online game site. The basic idea for our defection detection and prevention is adopted from the observation that potential defectors have a tendency to take a couple of months or weeks to gradually change their behavior (i.e. trim-out their usage volume) before their eventual withdrawal. For this purpose, we suggest a SOM (Self-Organizing Map) based procedure to determine the possible states of customer behavior from past behavior data. Based on this representation of the state of behavior, potential defectors are detected by comparing their monitored trajectories of behavior states with frequent and confident trajectories of past defectors. The key feature of this study includes a defection prevention procedure which recommends the desirable behavior state for the ext period so as to lower the likelihood of defection. The defection prevention procedure can be used to design a marketing campaign on an individual basis because it provides desirable behavior patterns for the next period. The experiments demonstrate that our approach is effective for defection prevention and efficient for defection detection because it predicts potential defectors without deterioration of prediction accuracy compared to that of the MLP (Multi-Layer Perceptron) neural network.

• Journal of Korea Multimedia Society
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• v.19 no.4
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• pp.736-746
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• 2016

In this paper, we propose an automatic prostate cancer detection method using position, signal intensity and texture feature based on SVM in multi-parametric MR images. First, to align the prostate on DWI and ADC map to T2wMR, the transformation parameters of DWI are estimated by normalized mutual information-based rigid registration. Then, to normalize the signal intensity range among inter-patient images, histogram stretching is performed. Second, to detect prostate cancer areas in T2wMR, SVM classification with position, signal intensity and texture features was performed on T2wMR, DWI and ADC map. Our feature classification using multi-parametric MR imaging can improve the prostate cancer detection rate on T2wMR.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.8 no.5
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• pp.1135-1140
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• 2007

This paper proposes an edge-based text region detection algorithm using the adaptive character-edge maps which are independent of the size of characters and the orientation of character string in natural images. First, labeled images are obtained from edge images and in order to search for characters, adaptive character-edge maps by way grammar are applied to labeled images. Next, selected label images are clustered as for distance of its neighbors. And then, text region candidates are obtained. Finally, text region candidates are verified by using the empirical rules and horizontal/vertical projection profiles based on the orientation of text region. As the results of experiments, a text region detection algorithm turned out to be robust in the matter of various character size, orientation, and the complexity of the background.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.5
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• pp.439-448
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• 2022

MV (Moving Vehicle) detection using satellite imagery is important for traffic monitoring and provides a wide range of observations. Specifically, MV detection methods utilizing the time lag in single-pass optical satellite images have been studied for detecting MVs from a single set of images. Because of limitations in detecting MVs outside of roads, most previous studies required road information to limit the moving object to cars on the road. However, it is difficult to obtain road information from inaccessible areas. Therefore, this study proposed a new method for detecting MVs regardless of their locations from single-pass optical satellite images without using additional data. WV-3 (Worldview-3) satellite images were used, and a spatial correlation coefficient map was proposed to detect spatial displacement which denotes MVs across two WV-3 MS images. Finally, evaluation was performed through quantitative metrics and visual inspection. The evaluation results revealed that the proposed method can detect MV movements from the single-pass satellite images. On the contrary, misdetected or undetected MVs due to radiometric differences between the images could be identified by visual inspection. The performance of the proposed method can be improved by minimizing radiometric variations and adding conditions that are robust to radiometric differences between the images.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.422-424
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• 2021

In this paper, it leads the approach of fusing multiple RGB cameras for visual objects recognition based on deep learning with convolution neural network and 3D Light Detection and Ranging (LiDAR) to observe the environment and match into a 3D world in estimating the distance and position in a form of point cloud map. The goal of perception in multiple cameras are to extract the crucial static and dynamic objects around the autonomous vehicle, especially the blind spot which assists the AV to navigate according to the goal. Numerous cameras with object detection might tend slow-going the computer process in real-time. The computer vision convolution neural network algorithm to use for eradicating this problem use must suitable also to the capacity of the hardware. The localization of classified detected objects comes from the bases of a 3D point cloud environment. But first, the LiDAR point cloud data undergo parsing, and the used algorithm is based on the 3D Euclidean clustering method which gives an accurate on localizing the objects. We evaluated the method using our dataset that comes from VLP-16 and multiple cameras and the results show the completion of the method and multi-sensor fusion strategy.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.109-111
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• 2021

Recently, computer vision research using light field cameras has been actively conducted. Since light field cameras have spatial information, various studies are being conducted in fields such as depth map estimation, super resolution, and 3D object detection. In this paper, we propose a method for detecting objects in blur images through a 7×7 array of images acquired through a light field camera. The blur image, which is weak in the existing camera, is detected through the light field camera. The proposed method uses the SSD algorithm to evaluate the performance using blur images acquired from light field cameras.