• The KIPS Transactions:PartB
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• v.15B no.5
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• pp.435-448
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• 2008

3D posture recognition is a solution to overcome the limitation of 2D posture recognition. There are many researches carried out for 3D posture recognition using 3D data. The 3D data consist of massive surface points which are rich of information. However, it is difficult to extract the important features for posture recognition purpose. Meanwhile, it also consumes lots of processing time. In this paper, we introduced a dimension reduction method that transform 3D surface points of an object to 2D data representation in order to overcome the issues of feature extraction and time complexity of 3D posture recognition. For a better feature extraction and matching process, a cylindrical boundary is introduced in meshless parameterization, its offer a fast processing speed of dimension reduction process and the output result is applicable for recognition purpose. The proposed approach is applied to hand and human posture recognition in order to verify the efficiency of the feature extraction.

• Journal of IKEEE
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• v.13 no.2
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• pp.181-185
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• 2009

In this paper, we are concerned with a 3D line segment extraction method by area-based stereo matching technique. The main idea is based on line fitting of elevation data on 2D line coordinates of ortho-image. Elevation data and ortho-image can be obtained by well-known area-based stereo matching technique. In order to use elevation in line fitting, the elevation itself should be reliable. To measure the reliability of elevation, in this paper, we employ the concept of self-consistency. We test the effectiveness of the proposed method with a quantitative accuracy analysis using synthetic images generated from Avenches data set of Ascona aerial images. Experimental results indicate that our method generates 3D line segments almost 7.5 times more accurate than raw elevations obtained by area-based method.

• Journal of KIBIM
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• v.9 no.3
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• pp.30-40
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• 2019

Extraction of fabrication documents is very important because it provides information related to tasks of fabrication and construction. Therefore, in the case of a prefabricated member such as a steel structure, it is necessary to improve the productivity of fabrication documents through 3D BIM. However, research and evidence data on direct comparison analysis of 3D BIM-based documents extraction versus 2D CAD-based documents extraction are hard to find. Thus, this study focuses on productivity analysis of 3D BIM based fabrication documents extraction. In this study, the productivity data of fabrication documents extraction for module construction of EPC project was analyzed. For the productivity analysis, a case study on the fabrication documents of Module A (1,965 sheets) and Module B (1,216 sheets) was conducted. Fabrication documents for each module include general arrangement drawing, assembly drawing, single part drawing and single plate drawing. Comparison of 2D CAD based fabrication documents extraction and 3D BIM based fabrication documents extraction, the productivity for the entire work was improved from 17 hours to 16 hours for Module A and 12 hours to 7 hours for Module B. Especially, the productivity of the assembly drawings, which occupies a large part of the fabrication documents, was improved by about 48.75% from the total time required from 281 hours to 144 hours.

• Journal of Ship and Ocean Technology
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• v.12 no.3
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• pp.1-13
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• 2008

In shipbuilding, diverse manufacturing techniques for automation have been developed and used in practice. Among them, however, the hull forming automation is the one that has not been of major concern compared with others such as welding and cutting. The basis of the development of this process is to find out how to extract thermal forming information. There exist various methods to obtain such information and the 3D design shape that needs to be formed should be extracted first for getting the necessary thermal forming information. Except well-established shipyards which operate 3D design systems, most of the shipyards only rely on 2.5D design systems and do not have an easy way to obtain 3D surface design data. So in this study, various shipbuilding design systems used by shipyards are investigated and a 3D design surface data extraction method is proposed from those design systems. Then an example is presented to show the extraction of real 3D surface data using the proposed method and computation of thermal forming information using the data.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.163-167
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• 2009

3D human body scan technique is known to be practically useful in industrial field as the technique becomes more precise and cheaper. Landmark extraction is essential for full utilization of the scan data. In this paper, we suggest an algorithm for automatic landmark extraction. For this purpose, we perform markerless matching to the target data using PCA analysis and quasi-Newton optimization. Landmarks are extracted from the topology of resulting body.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.859-861
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• 2003

In order to present a space in details, it is indispensable to acquire 3D shape and texture simultaneously from the same platform. 3D shape is acquired by Laser Scanner as point cloud data, and texture is acquired by CCD sensor. Positioning data is acquired by IMU (Inertial Measurement Unit). All the sensors and equipments are assembled on a hand-trolley. In this research, a method of integrating the 3D shape and texture for automated construction of Digital Surface Model is developed. This Digital Surface Model is applied for efficient feature extraction. More detailed extraction is possible , because 3D Digital Surface Model has both 3D shape and texture information.

• The Journal of the Korea Contents Association
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• v.8 no.12
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• pp.10-19
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• 2008

In this paper, we propose an automated silhouette extraction method that can effectively extract inner-patterns and silhouettes from 3D scan data of cultural asset. First of all, after creating the edge list of 3D vector data, we decide contour edge and crease edge according to viewpoint. In the next step, after extracting surface silhouette by investigating the vector variation of adjacent faces in crease edge, we finally extract the contour silhouette and surface silhouette for generating the blueprint of cultural asset. To evaluate the performance of the proposed silhouette extraction method, we performed experiments of silhouette extraction using a traditional tile model, a car model and a stone monument model. Comparing with the conventional threshold-based silhouette extraction method, the proposed method extracted more distinct and clear surface silhouettes and inner-patterns by effectively removing meaningless edges, such as noise.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.150-152
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• 2003

Laser scanned lidar data record 3D surface information in detail. Exploring valuable spatial information from lidar data is a prerequisite task for its applications, such as DEM generation and 3D building model reconstruction. However, the inherent spatial information is implicit in the abundant, densely and randomly distributed point cloud. This paper proposes a novel method to organize point cloud data, so that further analysis or feature extraction can proceed based on a well organized data model. The principle of the proposed algorithm is to segment point cloud into 3D planes. A split and merge segmentation based on the octree structure is developed for the implementation. Some practical airborne and ground lidar data are tested for demonstration and discussion. We expect this data organization could provide a stepping stone for extracting spatial information from lidar data.

• Korean Journal of Remote Sensing
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• v.29 no.5
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• pp.569-579
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• 2013

In this study, land-cover classification was implemented using features extracted from multi-temporal SAR data through 3D wavelet transform and the applicability of the 3D wavelet transform as a feature extraction approach was evaluated. The feature extraction stage based on 3D wavelet transform was first carried out before the classification and the extracted features were used as input for land-cover classification. For a comparison purpose, original image data without the feature extraction stage and Principal Component Analysis (PCA) based features were also classified. Multi-temporal Radarsat-1 data acquired at Dangjin, Korea was used for this experiment and five land-cover classes including paddy fields, dry fields, forest, water, and built up areas were considered for classification. According to the discrimination capability analysis, the characteristics of dry field and forest were similar, so it was very difficult to distinguish these two classes. When using wavelet-based features, classification accuracy was generally improved except built-up class. Especially the improvement of accuracy for dry field and forest classes was achieved. This improvement may be attributed to the wavelet transform procedure decomposing multi-temporal data not only temporally but also spatially. This experiment result shows that 3D wavelet transform would be an effective tool for feature extraction from multi-temporal data although this procedure should be tested to other sensors or other areas through extensive experiments.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.301-303
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• 2003

The extraction of 3D road network from high-resolution aerial images is still one of the current challenges in digital photogrammetry and computer vision. For many years, there are many researcher groups working for this task, but unt il now, there are no papers for doing this with TLS (Three linear scanner), which has been developed for the past several years, and has very high-resolution (about 3 cm in ground resolution). In this paper, we present a methodology of road extraction from high-resolution digital imagery taken over urban areas using this modern photogrammetry’s scanner (TLS). The key features of the approach are: (1) Because of high resolution of TLS image, our extraction method is especially designed for constructing 3D road map for next -generation digital navigation map; (2) for extracting road, we use the global context of the intensity variations associated with different features of road (i.e. zebra line and center line), prior to any local edge. So extraction can become comparatively easy, because we can use different special edge detector according different features. The results achieved with our approach show that it is possible and economic to extract 3D road data from Three Linear Scanner to construct next -generation digital navigation road map.