• 한국정보디스플레이학회:학술대회논문집
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• 2008.10a
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• pp.730-733
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• 2008

In this paper, a novel CII method using a depth conversion technique is proposed. The proposed method can move a far 3D object near lenslet array and reduce the computation cost dramatically. To show the usefulness of the proposed method, we carry out the preliminary experiment and its results are presented.

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

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.5
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• pp.537-542
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• 2011

A mirror neuron is a neuron fires both when an animal acts and when the animal observes the same action performed by another. We propose a method of 3D reconstruction for occluded object motion tracking like Mirror Neuron System to fire in hidden condition. For modeling system that intention recognition through fire effect like Mirror Neuron System, we calculate depth information using stereo image from a stereo camera and reconstruct three dimension data. Movement direction of object is estimated by optical flow with three-dimensional image data created by three dimension reconstruction. For three dimension reconstruction that enables tracing occluded part, first, picture data was get by stereo camera. Result of optical flow is made be robust to noise by the kalman filter estimation algorithm. Image data is saved as history from reconstructed three dimension image through motion tracking of object. When whole or some part of object is disappeared form stereo camera by other objects, it is restored to bring image date form history of saved past image and track motion of object.

• ETRI Journal
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• v.42 no.3
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• pp.420-432
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• 2020

The reconstruction of archaeological fragments in 3D geometry is an important problem in pattern recognition and computer vision. Therefore, we implement an algorithm with the help of a 3D model to perform reconstruction from the real datasets using the slope features. This approach avoids the problem of gaps created through the loss of parts of the artifacts. Therefore, the aim of this study is to assemble the object without previous knowledge about the form of the original object. We utilize the edges of the fragments as an important feature in reconstructing the objects and apply multiple procedures to extract the 3D edge points. In order to assign the positions of the unknown parts that are supposed to match, the contour must be divided into four parts. Furthermore, to classify the fragments under reconstruction, we apply a backpropagation neural network. We test the algorithm on several models of ceramic fragments. It achieves highly accurate results in reconstructing the objects into their original forms, in spite of absent pieces.

• The Journal of the Korea Contents Association
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• v.13 no.11
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• pp.523-532
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• 2013

Since conventional methods for the reconstruction of 3D objects used a number of cameras or pictures, they required specific hardwares or they were sensitive to the photography environment with a lot of processing time. In this paper, we propose a 3D object reconstruction method using one photograph based on structured light in ubiquitous environments. We use color pattern of the conventional method for structured light. In this paper, we propose a novel pipeline consisting of various image processing techniques for line pattern extraction and matching, which are very important for the performance of the object reconstruction. And we propose the optimal cost function for the pattern matching. Using our method, it is possible to reconstruct a 3D object with efficient computation and easy setting in ubiquitous or mobile environments, for example, a smartphone with a subminiature projector like Galaxy Beam.

• Journal of Biomedical Engineering Research
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• v.15 no.2
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• pp.201-208
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• 1994

Many three-dimensional object modeling and display methods for computer graphics and computer vision have been developed. Recently, with the help of medical imaging devices such as computerized tomography, magnetic resonance image, etc., some of those object modeling and display methods have been widely used for capturing the shape, structure and other properties of real objects in many medical applications. In this paper, we propose the reconstruction and display method of the three-dimensional object from a series of the cross sectonal image. It is implemented by using the automatic threshold selection method and the contour following algorithm. The combination of curvature and distance, we select feature points. Those feature points are the candidates for the tiling method. As a results, it is proven that this proposed method is very effective and useful in the comprehension of the object's structure. Without the technician's responce, it can be automated.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.927-930
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• 1998

This paper presents a new method for reconstructing 3 dimensional object model using a zoom camera. The proposed method uses zoom images to find the distance(D) between camera and object. Also the method uses images obtained around the object to find an $angle(\theta)$ between two connected planes of the object. With the D and $\theta,$ we can reconstruct the real sized 3-D model of object with less errors without stereo camera or rangefinder.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.1145-1148
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• 2002

This paper presents a new method which is calculated to use only three vanishing points in order to compute the dimensions of object and its pose from a single image of perspective projection taken by a camera and the problem of recovering 3D models from three vanishing points of box scene. Our approach is to compute only three vanishing points without this information such as the focal length, rotation matrix, and translation from images in the case of perspective projection. We assume that the object can be modeled as a linear function of a dimension vector ν. The input of reconstruction is a set of correspondences between features in the model and features in the image. To minimize each the dimensions of the parameterized models, this reconstruction of optimization can be solved by the standard nonlinear optimization techniques with a multi-start method which generates multiple starting points for the optimizer by sampling the parameter space uniformly.

• Journal of the Optical Society of Korea
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• v.12 no.3
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• pp.131-135
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• 2008

Computational integral imaging(CII) has the advantage of generating the volumetric information of the 3D scene without optical devices. However, the reconstruction process of CII requires increasingly larger sizes of reconstructed images and then the computational cost increases as the distance between the lenslet array and the reconstructed output plane increases. In this paper, to overcome this problem, we propose a novel CII method using a depth conversion technique. The proposed method can move a far 3D object near the lenslet array and reduce the computational cost dramatically. To show the usefulness of the proposed method, we carry out the preliminary experiment and its results are presented.

• Journal of Mechanical Science and Technology
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• v.17 no.11
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• pp.1665-1673
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• 2003

We propose an image-based three-dimensional shape determination system. The shape, and thus the three-dimensional coordinate information of the 3-D object, is determined solely from captured images of the 3-D object from a prescribed set of viewpoints. The approach is based on the shape-from-silhouette (SFS) technique, and the efficacy of the SFS method is tested using a sample data set. The extracted three-dimensional shape is modeled with polygons generated by a new iterative triangulation algorithm, and the polygon model can be exported to commercial software. The proposed system may be used to visualize the 3-D object efficiently, or to quickly generate initial CAD data for reverse engineering purposes, including three dimensional design applications such as 3-D animation and 3-D games.