• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.3
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• pp.249-255
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• 2011

The free-stereo mosaics image without GPS/INS and ground control data can be generated by using relative orientation parameters on the 3D model coordinate system. Its origin is located in one reference frame image. A 3D coordinate calculated by conjugate points on the free-stereo mosaic images is represented on the 3D model coordinate system. For determining 3D coordinate on the 3D absolute coordinate system utilizing conjugate points on the free-stereo mosaic images, transformation methodology is required for transforming 3D model coordinate into 3D absolute coordinate. Generally, the 3D similarity transformation is used for transforming each other 3D coordinates. Error of 3D model coordinates used in the free-stereo mosaic images is non-linearly increased according to distance from 3D model coordinate and origin point. For this reason, 3D model coordinates used in the free-stereo mosaic images are difficult to transform into 3D absolute coordinates by using linear transformation. Therefore, methodology for transforming nonlinear 3D model coordinate into 3D absolute coordinate is needed. Also methodology for resampling the free-stereo mosaic image to the geo-stereo mosaic image is needed for overlapping digital map on absolute coordinate and stereo mosaic images. In this paper, we propose a 3D non-linear transformation for converting 3D model coordinate in the free-stereo mosaic image to 3D absolute coordinate, and a 2D non-linear transformation based on 3D non-linear transformation converting the free-stereo mosaic image to the geo-stereo mosaic image.

• Korean Journal of Remote Sensing
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• v.20 no.2
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• pp.125-137
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• 2004

In case of using image sequence taken from a moving camera along a road in an urban area, general video mosaicing technique based on a single baseline cannot create 2-D image mosaics. To solve the drawback, this paper proposed a new image mosaicing technique through 3-D multi-baselines that can create image mosaics in 3-D space. The core of the proposed method is that each image frame has a dependent baseline, an equation of first order, calculated by using ground control point (GCP) of optical flows. The proposed algorithm consists of 4 steps: calculation of optical flows using hierarchical strategy, calculation of camera exterior orientation, determination of multi-baselines, and seamless image mosaics. This paper realized and showed the proposed algorithm that can create efficient image mosaics in 3-D space from real image sequence.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1999.11a
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• pp.239-243
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• 1999

Mosaic image involves the information of 2D images about objects and background. In this paper, we describe how to construct the information from the mosaic image. We propose the animation method through which it efficiently changes a camera angle from a 2D scene to a 3D scene after it presumes a 3D scene in an assigned place in based on the information of 2D image.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.4 s.304
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• pp.87-94
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• 2005

3D image mosaicking is useful for 3D visualization of the roadside scene of urban area by projecting 2D images to the 3D planes. When a sequence of images are filmed from a side-looking video camera passing long distance areas, the ghost effect in which same objects appear repeatively occurs. To suppress such ghost effect, the long distance range areas are detected by using the distance between the image frame and the 3D coordinate of tracked optical flows. The ghost effects are suppressed by projecting the part of image frames onto 3D multiple planes utilizing vectors passing the focal point of frames and a virtual focal point. The virtual focal point is calculated by utilizing the first and last frames of the long distance range areas. We demonstrate algorithm that creates efficient 3D Panoramic mosaics without the ghost effect at the long distance area.

• Journal of Broadcast Engineering
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• v.11 no.2 s.31
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• pp.164-173
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• 2006

The data structure of the concentric mosaic can be regarded as a video sequence with a slowly panning camera. We take a concentric mosaic with match or alignment of video sequences. Also the concentric mosaic required for huge memory. Thus, compressing is essential in order to use the concentric mosaic. Therefore we need the algorithm that compressed data structure was maintained and the scene was decoded. In this paper, we used 3D lifting transform to compress concentric mosaic. Lifting transform has a merit of wavelet transform and reduces computation quantities and memory. Because each frame has high correlation, the complexity which a scene is detected form 3D transformed bitstream is increased. Thus, in order to have higher performance and decrease the complexity of detecting of a scene we executed 3D lifting and then transformed data set was sequently compressed with each frame unit. Each frame has a flexible bit rate. Also, we proposed the algorithm that compressed data structure was maintained and the scene was decoded by using property of lifting structure.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.27 no.4
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• pp.453-460
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• 2009

For constructing 3D information using aerial photograph or video sequences, left and right stereo images having different viewing angle should be prepared in overlapping area. In video sequences, left and right stereo images would be generated by mosaicing left and right slice images extracted in consecutive video sequences. Therefore, this paper is focused on generating left and right stereo mosaic images that are able to construct 3D information and video sequences could be made for the best use. In the stereo mosaic generation, motion parameters between video sequences should be firstly determined. In this paper, to determine motion parameters, free mosaic method using geometric relationship, such as relative orientation parameters, between consecutive frame images without GPS/INS geo-data have applied. After determining the motion parameters, the mosaic image have generated by 4 step processes: image registration, image slicing, determining on stitching line, and 3D image mosaicking. As the result of experiment, generated stereo mosaic image and analyzed result of x, y-parallax have showed.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.3 s.303
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• pp.41-50
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• 2005

A novel image mosaicking technique suitable for 3D urban visualization is proposed. It is not effective to apply 2D image mosaicking techniques for urban visualization when, for example, one is filming a sequence of images from a side-looking video camera along a road in an urban area. The proposed method presents the roadside scene captured by a side-looking video camera as a continuous set of textured planar faces, which are termed 'multiple planes' in this paper. The exterior parameters of each frame are first calculated through automatically selected matching feature points. The matching feature points are also used to estimate a plane approximation of the scene geometry for each frame. These planes are concatenated to create an approximate model on which images are back-projected as textures. Here, we demonstrate algorithm that creates efficient image mosaics in 3D space from a sequence of real images.

• Journal of the Institute of Convergence Signal Processing
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• v.10 no.1
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• pp.13-22
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• 2009

In this paper, we propose the 3D shape reconstruction method that combine the mosaic method and the active stereo matching using the laser beam. The active stereo matching method detects the position information of the irradiated laser beam on object by analyzing the color and brightness variation of left and right image, and acquires the depth information in epipolar line. The mosaic method extracts feature point of image by using harris comer detection and matches the same keypoint between the sequence of images using the keypoint descriptor index method and infers correlation between the sequence of images. The depth information of the sequence image was calculated by the active stereo matching and the mosaic method. The merged depth information was reconstructed to the 3D shape information by wrapping and blending with image color and texture. The proposed reconstruction method could acquire strong the 3D distance information, and overcome constraint of place and distance etc, by using laser slit beam and stereo camera.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.1
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• pp.91-96
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• 2010

Concentric mosaics are made with arranging and summing of video frames by using common spacial standards. Compared with previous works on 3-D wavelet transform coding, we have made important design considerations to enable flexible partial decoding and bit-stream random access. A just-in-time(JIT) rendering engine of the compressed concentric mosaic is developed. However, computationally, it is still demanding to accomplish the real-time rendering. Only the contents for specific scene representation are need to be decoded by maintaining compressed data. Thus our proposed algorithm is able to render real concentric mosaic by using lifting scheme instead of wavelet transform.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.6
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• pp.427-434
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• 2019

In the present study, we assessed the parallel computing performance of Agisoft Metashape for orthomosaic generation, which can implement aerial triangulation, generate a three-dimensional point cloud, and make an orthomosaic based on SfM (Structure from Motion) technology. Due to the nature of SfM, most of the time is spent on Align photos, which runs as a relative orientation, and Build dense cloud, which generates a three-dimensional point cloud. Metashape can parallelize the two processes by using multi-cores of CPU (Central Processing Unit) and GPU (Graphics Processing Unit). An orthomosaic was created from large UAV (Unmanned Aerial Vehicle) images by six conditions combined by three parallel methods (CPU only, GPU only, and CPU + GPU) and two operating systems (Windows and Linux). To assess the consistency of the results of the conditions, RMSE (Root Mean Square Error) of aerial triangulation was measured using ground control points which were automatically detected on the images without human intervention. The results of orthomosaic generation from 521 UAV images of 42.2 million pixels showed that the combination of CPU and GPU showed the best performance using the present system, and Linux showed better performance than Windows in all conditions. However, the RMSE values of aerial triangulation revealed a slight difference within an error range among the combinations. Therefore, Metashape seems to leave things to be desired so that the consistency is obtained regardless of parallel methods and operating systems.