• Journal of KIISE:Computer Systems and Theory
• /
• v.30 no.3_4
• /
• pp.168-185
• /
• 2003

This paper presents a progressive algorithm that not only can narrow down the search domain in the course of face identification but also can fast reconstruct various 3D objects from a sketch drawing. The sketch drawing, edge-vertex graph without hidden line removal, which serves as input for reconstruction process, is obtained from an inaccurate freehand sketch of a 3D wireframe object. The algorithm is executed in two stages. In the face identification stage, we generate and classify potential faces into implausible, basis, and minimal faces by using geometrical and topological constraints to reduce search space. The proposed algorithm searches the space of minimal faces only to identify actual faces of an object fast. In the object reconstruction stage, we progressively calculate a 3D structure by optimizing the coordinates of vertices of an object according to the sketch order of faces. The progressive method reconstructs the most plausible 3D object quickly by applying 3D constraints that are derived from the relationship between the object and the sketch drawing in the optimization process. Furthermore, it allows the designer to change viewpoint during sketching. The progressive reconstruction algorithm is discussed, and examples from a working implementation are given.

• Applied Microscopy
• /
• v.46 no.2
• /
• pp.71-75
• /
• 2016

The scanning electron microscope (SEM) offers two-dimensional (2D) micrographs of three-dimensional (3D) objects due to its inherent operating mechanisms. To overcome this limitation, other devices have been used for quantitative morphological analysis. Many efforts have been made on the applications of software-based approaches to 3D reconstruction and measurements by SEM. Based on the acquisition of two stereo images, a multi-view technique consists of two parts: (i) geometric calibration and (ii) image matching. Quantitative morphological parameters such as height and depth could be nondestructively measured by SEM combined with special software programs. It is also possible to obtain conventional surface parameters such as roughness and volume of biomedical specimens through 3D SEM surface reconstruction. There is growing evidence that conventional 2D SEM without special electron detectors can be transformed to 3D SEM for quantitative measurements in biomedical research.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2012.05a
• /
• pp.155-157
• /
• 2012

In this paper, we propose a nonlinear 3D image correlator using computational reconstruction of 3D images based on integral imaging. In the proposed method, the elemental images for reference 3D object and target 3D object are recorded through the lens array. The recorded elemental images are reconstructed as reference plane image and target plane images using the computational integral imaging reconstruction algorithm and the nonolinear correlation between them is performed for object recognition. To show the usefulness of the proposed method, the preliminary experiments are carried out and the experimental results are presented compared with the conventional results.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
• /
• 2004.11a
• /
• pp.529-534
• /
• 2004

The increasing need and use of 3D GIS particularly in urban areas has produced growing attention on building reconstruction. Nowadays, the use of close-range data for building reconstruction has been intensively emphasized since they can provide higher resolution and more complete coverage than airborne sensory data. We developed a fusion approach for building reconstruction from both points and images. The proposed approach was then applied to reconstructing a building model from real data sets acquired from a large existing building. Based on the experimental results, we assured that the proposed approach cam achieve high resolution and accuracy in building reconstruction. The proposed approach can effectively contribute in developing an operational system producing large urban models for 3D GIS.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.44 no.4 s.316
• /
• pp.1-7
• /
• 2007

In this paper, we analyze geometrical relations of 3D shape reconstruction from 2D images taken under anne projection. The purpose of this research is to contribute to more accurate 3-D reconstruction under noise distribution by analyzing geometrically the 2D to 3D relationship. In situation for no missing feature points (FPs) or no noise in 2D image plane, the accurate solution of 3D shape reconstruction is blown to be provided by Singular Yalue Decomposition (SVD) factorization. However, if several FPs not been observed because of object occlusion and image low resolution, and so on, there is no simple solution. Moreover, the 3D shape reconstructed from noise-distributed FPs is peturbed because of the influence of the noise. This paper focuses on analysis of geometrical properties which can interpret the missing FPs even though the noise is distributed on other FPs.

• Journal of Institute of Convergence Technology
• /
• v.10 no.1
• /
• pp.7-12
• /
• 2020

We describe a efficient surface reconstruction method that reconstructs a 3D manifold polygonal mesh approximately passing through a set of 3D oriented points. Our algorithm includes 3D convex hull, octree data structure, signed distance function (SDF), and marching cubes. The 3D convex hull provides us with a fast computation of SDF, octree structure allows us to compute a minimal distance for SDF, and marching cubes lead to iso-surface generation with SDF. Our approach gives us flexibility in the choice of the resolution of the reconstructed surface, and it also enables to use on low-level PCs with minimal peak memory usage. Experimenting with publicly available scan data shows that we can reconstruct a polygonal mesh from point cloud of sizes varying from 10,000 ~ 1,000,000 in about 1~60 seconds.

• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 2001.10a
• /
• pp.93-100
• /
• 2001

The three dimensional(3D) reconstruction from two dimensional(2D) image data is using in many fields such as RPD(Rapid Product Development) and reverse engineering. In this paper, the main step of 3D reconstruction is comprised of two steps : image processing step and B-spline surface approximation step. In the image processing step, feature points of each cross-section are obtained by means of several image processing technologies. In the B-spline surface approximation step, using the data of feature points obtained in the image processing step, the control points of B-spline surface are obtained, which are used for IGES file of 3D CAD model.

• Journal of Radiopharmaceuticals and Molecular Probes
• /
• v.3 no.2
• /
• pp.65-71
• /
• 2017

To assess the effects of filter and reconstruction of Cu-64 PET data on Siemens scanner, the various reconstruction algorithm with various filters were assessed in terms of spatial resolution, non-uniformity (NU), recovery coefficient (RC), and spillover ratio (SOR). Image reconstruction was performed using filtered backprojection (FBP), 2D ordered subset expectation maximization (OSEM), 3D reprojection algorithm (3DRP), and maximum a posteriori algorithms (MAP). For the FBP reconstruction, ramp, butterworth, hamming, hanning, or parzen filters were used. Attenuation or scatter correction were performed to assess the effect of attenuation and scatter correction. Regarding spatial resolution, highest achievable volumetric resolution was $3.08mm^3$ at the center of FOV when MAP (${\beta}=0.1$) reconstruction method was used. SOR was below 4% for FBP when ramp, Hamming, Hanning, or Shepp-logan filter were used. The lowest NU (highest uniform) after attenuation & scatter correction was 5.39% when FBP (parzen filter) was used. Regarding RC, 0.9 < RC < 1.1 was obtained when OSEM (iteration: 10) was used when attenuation and scatter correction were applied. In this study, image quality of Cu-64 on Siemens Inveon PET was investigated. This data will helpful for the quantification of Cu-64 PET data.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.12 no.10
• /
• pp.1853-1859
• /
• 2008

In this paper, we propose a novel method to improve the visual quality of reconstructed images for 3D pattern recognition based on the computational integral imaging reconstruction (CIIR). The proposed CIIR method provides improved 3D reconstructed images by superimposing magnified elemental images by a combination of a round mapping model and image interpolation algorithms. To objectively evaluate the proposed method, we introduce an experimental framework for a computational pickup process and a CIIR process using a Gaussian function and evaluate the proposed method. We also carry out experiments on 3D objects and present their results.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.55 no.4
• /
• pp.173-180
• /
• 2006

A key component of autonomous navigation of intelligent home robot is localization and map building with recognized features from the environment. To validate this, accurate measurement of relative location between robot and features is essential. In this paper, we proposed relative localization algorithm based on 3D reconstruction of scale invariant features of two images which are captured from two parallel cameras. We captured two images from parallel cameras which are attached in front of robot and detect scale invariant features in each image using SIFT(scale invariant feature transform). Then, we performed matching for the two image's feature points and got the relative location using 3D reconstruction for the matched points. Stereo camera needs high precision of two camera's extrinsic and matching pixels in two camera image. Because we used two cameras which are different from stereo camera and scale invariant feature point and it's easy to setup the extrinsic parameter. Furthermore, 3D reconstruction does not need any other sensor. And the results can be simultaneously used by obstacle avoidance, map building and localization. We set 20cm the distance between two camera and capture the 3frames per second. The experimental results show :t6cm maximum error in the range of less than 2m and ${\pm}15cm$ maximum error in the range of between 2m and 4m.