• Korean Journal of Geomatics
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• v.2 no.1
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• pp.31-35
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• 2002

This paper describes an experiment for three-dimensional positioning for a pair of KOMPSAT stereostrips using the ancillary data and a single ground control point. The photogrammetric model for three-dimensional positioning was performed as follows: first, initialization of orbital and attitude parameters derived from ancillary data; second, adjustment of orbital and attitude parameters for the satellite to minimize the ground position error with respect to a GCP using the collinearity condition; third, determination of actual satellite position; and lastly, space intersection. This model was tested for a pair of stereo strips with 0.6 base-to-height ratio and GCPs identified from a 1:5,000 scale digital map. As the result, the satellite position of offset was corrected by only one GCP and the accuracy for the geometric modeling showed 38.89m RMSE.

• 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.

• Journal of Multimedia Information System
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• v.8 no.4
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• pp.211-220
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• 2021

The process of inspecting SMDs on the PCB boards improves the product quality, performance and reduces frequent issues in this field. However, undesirable scenarios such as assembly failure and device breakdown can occur sometime during the assembly process and result in costly losses and time-consuming. The detection of these components with a model based on deep learning may be effective to reduce some errors during the inspection in the manufacturing process. In this paper, YOLO models were used due to their high speed and good accuracy in classification and target detection. A SMD detection and classification method using YOLO networks based on robust data preprocessing and augmentation techniques to deal with various types of variation such as illumination and geometric changes is proposed. For 9 different components of data provided from a PCB manufacturer company, the experiment results show that YOLOv4 is better with fast detection and classification than YOLOv3.

• Journal of Auto-vehicle Safety Association
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• v.12 no.4
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• pp.70-77
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• 2020

In this study, we estimate the vehicle speed by analyzing the acoustic data recorded in a single microphone of a surveillance camera. The frequency analysis of the acoustic data corrects the Doppler effect, which is a characteristic of the moving sound source, and reflects the geometric relationship according to the location of the sound source and the microphone on the two-dimensional plane. The acoustic data is selected from the horn sound that is mainly observed in an urgent situation among various sound sources that may occur in a traffic accident, and the characteristics of the monotone source are considered. We verified the reliability of the proposed method by time domain acoustic analysis and actual vehicle evaluation. This method is effective and can be used for traffic accident analysis in the blind spot of the camera using a single microphone built into the existing surveillance camera.

• International journal of advanced smart convergence
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• v.13 no.1
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• pp.77-84
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• 2024

This study delves into the subtle sounds emanating from beneath the earth's surface to unveil hidden messages and the movements of life. It transforms these acoustic phenomena into digital data and reimagines them as visual elements. By employing Sismophone microphones and utilizing the FFT function in p5.js, it analyzes the intricate frequency components of subterranean sounds and translates them into various visual elements, including 3D geometric shapes, flowing lines, and moving particles. This project is grounded in the sounds recorded in diverse 'spaces of death,' ranging from the tombs of Joseon Dynasty officials to abandoned areas in modern cities. We leverage the power of sound to transcend space and time, conveying the concealed narratives and messages of forgotten places .Through the visualization of these sounds, this research blurs the boundaries between 'death' and 'life,' 'past' and 'present,' aiming to explore new forms of artistic expression and broaden perceptions through the sensory connection between sound and vision.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.9
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• pp.768-773
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• 2005

A new approach for human foot modelling and last design based on the cross sectional method is presented in this paper. The proposed last design method utilizes the dynamic trimmed parametric patches for the foot 3D data and last 3D data. The cross section a surface of 3D foot for the 3D last, design modeling of free form geometric last shapes. The proposed last design scheme wraps the 3D last data surrounding the measured 3D foot data with the effect of deforming the last design rule The last design rule of the FFD is constructed on the FFD lattice based on foot-last shape analysis. In addition, the control points of FFD lattice are constructed with cross sectional data interpolation methods from the a finite set of 3D foot data. The deformed 3D last result obtained from the proposed FFD is saved as a 3D dxf foot data. The experimental results demonstrate that the last designed with the proposed scheme has good performance.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.1
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• pp.47-56
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• 2010

The use of airborne LiDAR data obtained by airborne laser scanners has increased in the field of spatial information such as building modeling. LiDAR data consist of irregularly distributed 3D coordinates and lack visual and semantic information. Therefore, LiDAR data processing is complicate. This study suggested a method of LiDAR data segmentation using roof surface patches from aerial images. Each segmented patch was modeled by analyzing geometric characteristics of the LiDAR data. The optimal functions could be determined with segmented data that fits various shapes of the roof surfaces as flat and slanted planes, dome and arch types. However, satisfiable segmentation results were not obtained occasionally due to shadow and tonal variation on the images. Therefore, methods to remove unnecessary edges result in incorrect segmentation are required.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1177-1179
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• 2003

Recently, the ground-based laser profiler is used for acquisition of 3D spatial information of a rchaeological objects. However, it is very difficult to measure complicated objects, because of a relatively low-resolution. On the other hand, texture mapping can be a solution to complement the low resolution, and to generate 3D model with higher fidelity. But, a huge cost is required for the construction of textured 3D model, because huge labor is demanded, and the work depends on editor's experiences and skills . Moreover, the accuracy of data would be lost during the editing works. In this research, using the laser profiler and a non-calibrated digital camera, a method is proposed for the automatic generation of 3D model by integrating these data. At first, region segmentation is applied to laser range data to extract geometric features of an object in the laser range data. Various information such as normal vectors of planes, distances from a sensor and a sun-direction are used in this processing. Next, an image segmentation is also applied to the digital camera images, which include the same object. Then, geometrical relations are determined by corresponding the features extracted in the laser range data and digital camera’ images. By projecting digital camera image onto the surface data reconstructed from laser range image, the 3D texture model was generated automatically.

• Korean Journal of Computational Design and Engineering
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• v.13 no.6
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• pp.459-468
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• 2008

A web-based Computer-Aided Engineering (CAE) supporting system is connected with a Product Data Management (PDM) system for Small and Medium Enterprises (SMEs) suffering from the lack of building hardware, software and related experts. An analysis of current business models and worksite requirements provides an improved process model and data to be shared between the PDM system and the CAE supporting system. Since all engineering tasks such as geometric modeling, mesh generation, static stress and modal analysis, and fatigue durability analysis are automated in the CAE supporting system, the user in charge of the CAE have only to configure the concerned values of design variables and result data through the web page. The existing Change Management module of the PDM system is modified for seamless data exchange, i.e. sending the Engineering Change Order (ECO) data to the CAE supporting system and receiving the CAE result data bark. The hi-directional data transfers between the PDM system and the CAE supporting system is made possible by adaptors bused on the Simple Object Access Protocol (SOAP). The current approach will be very helpful for SMEs that only have the PDM system and have no adequate infrastructure for CAE.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.6_2
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• pp.643-651
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• 2012

Object recognition belongs to high-level processing that is one of the difficult and challenging tasks in computer vision. Digital photogrammetry based on the computer vision paradigm has begun to emerge in the middle of 1980s. However, the ultimate goal of digital photogrammetry - intelligent and autonomous processing of surface reconstruction - is not achieved yet. Object recognition requires a robust shape description about objects. However, most of the shape descriptors aim to apply 2D space for image data. Therefore, such descriptors have to be extended to deal with 3D data such as LiDAR(Light Detection and Ranging) data obtained from ALS(Airborne Laser Scanner) system. This paper introduces extension of chain code to 3D object space with hierarchical approach for segmenting point cloud data. The experiment demonstrates effectiveness and robustness of the proposed method for shape description and point cloud data segmentation. Geometric characteristics of various roof types are well described that will be eventually base for the object modeling. Segmentation accuracy of the simulated data was evaluated by measuring coordinates of the corners on the segmented patch boundaries. The overall RMSE(Root Mean Square Error) is equivalent to the average distance between points, i.e., GSD(Ground Sampling Distance).