• ETRI Journal
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• v.37 no.3
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• pp.606-616
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• 2015

In this paper, we propose a novel method for road recognition using 3D point clouds based on a Markov random field (MRF) framework in unstructured and complex road environments. The proposed method is focused on finding a solution for an analysis of traversable regions in challenging environments without considering an assumption that has been applied in many past studies; that is, that the surface of a road is ideally flat. The main contributions of this research are as follows: (a) guidelines for the best selection of the gradient value, the average height, the normal vectors, and the intensity value and (b) how to mathematically transform a road recognition problem into a classification problem that is based on MRF modeling in spatial and visual contexts. In our experiments, we used numerous scans acquired by an HDL-64E sensor mounted on an experimental vehicle. The results show that the proposed method is more robust and reliable than a conventional approach based on a quantity evaluation with ground truth data for a variety of challenging environments.

• Journal of the Korean Society for Precision Engineering
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• v.30 no.6
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• pp.601-606
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• 2013

3D scanning is a technique to measure the 3D shape information of the object. Shape information obtained by 3D scanning is expressed either as point cloud or as polygon mesh type data that can be widely used in various areas such as reverse engineering and quality inspection. 3D scanning should be performed as accurate as possible since the scanned data is highly required to detect the features on an object in order to scan the shape of the object more precisely. In this study, we propose the method on finding the location of feature more accurately, based on the extended Biplane SNAKE with global optimization. In each iteration, we project the feature lines obtained by the extended Biplane SNAKE into each image plane and move the feature lines to the features on each image. We have applied this approach to real models to verify the proposed optimization algorithm.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.773-775
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• 2003

From the view point of data fusion, we integrate LIDAR data and digital aerial images to perform 3D building modeling in this study. The proposed scheme comprises two major parts: (1) building block extraction and (2) building model reconstruction. In the first step, height differences are analyzed to detect the above ground areas. Color analysis is then performed for the exclusion of tree areas. Potential building blocks are selected first followed by the refinement of building areas. In the second step, through edge detection and extracting the height information from LIDAR data, accurate 3D edges in object space is calculated. The accurate 3D edges are combined with the already developed SMS method for building modeling. LIDAR data acquired by Leica ALS 40 in Hsin-Chu Science-based Industrial Park of north Taiwan will be used in the test.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.11 no.1
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• pp.1-9
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• 2002

Reverse engineering is an emerging technology to obtain CAD models from existing physical parts in the case that CAD models are not available or paras are changed an(1 modified so that new CAD models for final parts are necessary. Reverse engineering helps designers to quickly generate computer interpretable data from existing Physical objects So it is applying for field of Rapid Prototyping NC Processing CAE, Inspection and so on. The objective of this study is to develop the software that deals with unorganized point data and quickly obtains CAD model. In this paper, several models such as human\`s bone, car, are experimented by the proposed methods.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.482-484
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• 2019

Learning and analyzing 3D point clouds with deep networks is challenging due to the limited and irregularity of the data. In this paper, we present a data-driven point cloud augmentation technique. The key idea is to learn multilevel features per point and to reconstruct to a similar point set. Our network is applied to a projection loss function that encourages the predicted points to remain on the geometric shapes with a particular target. We conduct various experiments using ShapeNet part data to evaluate our method and demonstrate its possibility. Results show that our generated points have a similar shape and are located closer to the object.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.505-507
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• 2003

This paper describes techniques for the automated creation of geometric 3D models of the urban area us ing two 2D laser scanners and aerial images. One of the laser scanners scans an environment horizontally and the other scans vertically. Horizontal scanner is used for position estimation and vertical scanner is used for building 3D model. Aerial image is used for registration with scan data. Those models can be used for virtual reality, tele-presence, digital cinematography, and urban planning applications. Results are shown with 3D point cloud in urban area.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.3
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• pp.238-243
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• 2009

This paper present same thodology for the classification of power system voltage stability, the trajectory of which to instability is monotonic, using an interior point method based support vector machine(IPMSVM). The SVM based voltage stability classifier canp rovide real-time stability identification only using the local measurement data, without the topological information conventionally used.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.1
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• pp.206-221
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• 2019

Source localization in three-dimensional (3-D) wireless sensor networks (WSNs) is becoming a major research focus. Due to the complicated air-ground environments in 3-D positioning, many of the traditional localization methods, such as received signal strength (RSS) may have relatively poor accuracy performance. Benefit from prior learning mechanisms, fingerprinting-based localization methods are less sensitive to complex conditions and can provide relatively accurate localization performance. However, fingerprinting-based methods require training data at each grid point for constructing the fingerprint database, the overhead of which is very high, particularly for 3-D localization. Also, some of measured data may be unavailable due to the interference of a complicated environment. In this paper, we propose an efficient kernel based 3-D localization algorithm via tensor completion. We first exploit the spatial correlation of the RSS data and demonstrate the low rank property of the RSS data matrix. Based on this, a new training scheme is proposed that uses tensor completion to recover the missing data of the fingerprint database. Finally, we propose a kernel based learning technique in the matching phase to improve the sensitivity and accuracy in the final source position estimation. Simulation results show that our new method can effectively eliminate the impairment caused by incomplete sensing data to improve the localization performance.

• 전자공학회논문지 IE
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• v.46 no.1
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• pp.37-43
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• 2009

In this paper, we propose a new algorithm for 3D model retrieval based on spherical coordinate system. We obtains sample points in a polygons on 3D model. We convert a point in cartesian coordinates(x, y, z) to it in spherical coordinate. 3D shape features are achieved by adopting distribution of zenith of sample point in spherical coordinate. We used Osada's method for obtaining sample points on 3D model and the PCA method for the pose standardization 3D model. Princeton university's benchmark data was used for this research. Experimental results numerically show the precision improvement of proposed algorithm 12.6% in comparison with Vranic's depth buffer-based feature vector algorithm.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.1
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• pp.161-168
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• 2010

This study aims to propose a method that shall rapidly acquire 3D information of the fast and frequently changing city areas by using the images taken by the UAV photogrammetric method, and to develop the process of the acquired data. For this study's proposed UAV photogrammetric method, low-cost UAV and non-metric digital camera were used. The elements of interior orientation were acquired through camera calibration. The artificial 3D model of the artificial structures was constructed using the image data photographed at the target area and the results of the ground control point survey. The digital surface model was created for areas that were changed due to a number of civil works. This study also analyzes the proposed method's application possibility by comparing a 1/1,000 scale digital map and the results of the ground control point survey. Through the above studies, the possibilities of constructing a 3D virtual city model renewal of 3D GIS database, abstraction of changed information in geographic features and on-demand updating of the digital map were suggested.