• Journal of information and communication convergence engineering
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• 제7권1호
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• pp.1-6
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• 2009

In order to obtain a 3-D urban model, an abstraction of the surface model is required. This paper describes works on the 3D reconstruction and modeling by the grouping 3D line segments extracted from the stereo matching of edges, which is derived from multiple images. The grouping is achieved by conditions of degrees and distances between lines. Building objects are determined by the junction combinations of the grouped line segments. The proposed algorithm demonstrates effective results of 3D reconstruction of buildings with 2D aerial images.

• 한국정보통신학회논문지
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• 제19권12호
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• pp.2943-2948
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• 2015

경제성을 얻기 위해 단일 카메라를 이용하여 3차원 복원을 수행한 뒤 그 정보를 토대로 운전자에게 전방에 존재하는 장애물의 통과 여부를 알려줄 수 있는 운전 보조 장치에 관한 연구를 진행한다. 그 결과 depth 정보는 떨어지나 직진상의 장애물을 통과 할 수 있는 정보를 제공할 수 있다. 3차원 복원을 위해서는 내부파라미터를 측정하고, 특징점을 찾아 매칭하여 기본행렬을 계산하고 이를 토대로 삼각측량을 수행하여 얻는다. 실험을 통해 결과를 확인해 보면 depth 정보는 불완전하나, 장애물 통과 여부를 판단 할 수 있는 X, Y 축의 정보는 신뢰성을 가진다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권2호
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• pp.871-891
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• 2019

Visual tracking is a challenging task that needs learning an effective model to handle the changes of target appearance caused by factors such as pose variation, illumination change, occlusion and motion blur. In this paper, a novel tracking algorithm based on weighted local sub-space reconstruction error is presented. First, accounting for the appearance changes in the tracking process, a generative weight calculation method based on structural reconstruction error is proposed. Furthermore, a template update scheme of occlusion-aware is introduced, in which we reconstruct a new template instead of simply exploiting the best observation for template update. The effectiveness and feasibility of the proposed algorithm are verified by comparing it with some state-of-the-art algorithms quantitatively and qualitatively.

• Journal of Information Processing Systems
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• 제15권2호
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• pp.410-421
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• 2019

Distributed compressed sensing (DCS) states that we can recover the sparse signals from very few linear measurements. Various studies about DCS have been carried out recently. In many practical applications, there is no prior information except for standard sparsity on signals. The typical example is the sparse signals have block-sparse structures whose non-zero coefficients occurring in clusters, while the cluster pattern is usually unavailable as the prior information. To discuss this issue, a new algorithm, called backtracking-based adaptive orthogonal matching pursuit for block distributed compressed sensing (DCSBBAOMP), is proposed. In contrast to existing block methods which consider the single-channel signal reconstruction, the DCSBBAOMP resorts to the multi-channel signals reconstruction. Moreover, this algorithm is an iterative approach, which consists of forward selection and backward removal stages in each iteration. An advantage of this method is that perfect reconstruction performance can be achieved without prior information on the block-sparsity structure. Numerical experiments are provided to illustrate the desirable performance of the proposed method.

• Journal of Information Processing Systems
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• 제14권6호
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• pp.1445-1456
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• 2018

Environment perception and three-dimensional (3D) reconstruction tasks are used to provide unmanned ground vehicle (UGV) with driving awareness interfaces. The speed of obstacle segmentation and surrounding terrain reconstruction crucially influences decision making in UGVs. To increase the processing speed of environment information analysis, we develop a CPU-GPU hybrid system of automatic environment perception and 3D terrain reconstruction based on the integration of multiple sensors. The system consists of three functional modules, namely, multi-sensor data collection and pre-processing, environment perception, and 3D reconstruction. To integrate individual datasets collected from different sensors, the pre-processing function registers the sensed LiDAR (light detection and ranging) point clouds, video sequences, and motion information into a global terrain model after filtering redundant and noise data according to the redundancy removal principle. In the environment perception module, the registered discrete points are clustered into ground surface and individual objects by using a ground segmentation method and a connected component labeling algorithm. The estimated ground surface and non-ground objects indicate the terrain to be traversed and obstacles in the environment, thus creating driving awareness. The 3D reconstruction module calibrates the projection matrix between the mounted LiDAR and cameras to map the local point clouds onto the captured video images. Texture meshes and color particle models are used to reconstruct the ground surface and objects of the 3D terrain model, respectively. To accelerate the proposed system, we apply the GPU parallel computation method to implement the applied computer graphics and image processing algorithms in parallel.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권4호
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• pp.2028-2041
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• 2019

Compressed sensing (CS) is a new theory. With regard to the sparse signal, an exact reconstruction can be obtained with sufficient CS measurements. Nevertheless, in practical applications, the transform coefficients of many signals usually have weak sparsity and suffer from a variety of noise disturbances. What's worse, most existing classical algorithms are not able to effectively solve this issue. So we proposed an efficient algorithm based on smoothed ${\ell}_0$ norm for sparse signal reconstruction. The direct ${\ell}_0$ norm problem is NP hard, but it is unrealistic to directly solve the ${\ell}_0$ norm problem for the reconstruction of the sparse signal. To select a suitable sequence of smoothed function and solve the ${\ell}_0$ norm optimization problem effectively, we come up with a generalized approximate function model as the objective function to calculate the original signal. The proposed model preserves sharper edges, which is better than any other existing norm based algorithm. As a result, following this model, extensive simulations show that the proposed algorithm is superior to the similar algorithms used for solving the same problem.

• Journal of information and communication convergence engineering
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• 제19권2호
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• pp.102-107
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• 2021

In this paper, we propose a new three-dimensional (3D) photon-counting integral imaging reconstruction method using a merging reconstruction process and maximum likelihood estimation (MLE). The conventional 3D photon-counting reconstruction method extracts photons from elemental images using a Poisson random process and estimates the scene using statistical methods such as MLE. However, it can reduce the photon levels because of an average overlapping calculation. Thus, it may not visualize 3D objects in severely low light environments. In addition, it may not generate high-quality reconstructed 3D images when the number of elemental images is insufficient. To solve these problems, we propose a new 3D photon-counting merging reconstruction method using MLE. It can visualize 3D objects without photon-level loss through a proposed overlapping calculation during the reconstruction process. We confirmed the image quality of our proposed method by performing optical experiments.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2012년도 춘계학술발표대회
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• pp.336-339
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• 2012

VTK is a free but professional development platform for images three-dimensional (3D) reconstruction and processing. It is powerful, open-source, and users can customize their own needs by self-development of great flexibility. To give the doctors more and detailed information by simulate dissection to the 3-D brain MRI image after reconstruction. A Visualization System (VS) is proposed to achieve 3D brain reconstruction and virtual dissection functions. Based on the free VTK visualization development platform and Visual Studio 2010 IDE development tools, through C++ language, using real people's MRI brain dataset, we realized the images 3D reconstruction and also its applications and extensions correspondingly. The display effect of the reconstructed 3D image is well and intuitive. With the related operations such as measurement, virtual dissection and so on, the good results we desired could be achieved.

• 한국정보디스플레이학회:학술대회논문집
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• 한국정보디스플레이학회 2008년도 International Meeting on Information Display
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• pp.1131-1134
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• 2008

In this invited paper, numerical reconstruction and pattern recognition using integral imaging are overviewed. The computational integral imaging method reconstructs three-dimensional information at arbitrary depth-levels. Photon-counting nonlinear matched filtering combined with the computational reconstruction provides promising results for the application of low-light level recognition.

• 천문학회지
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• 제49권4호
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• pp.157-162
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• 2016

High resolution reconstruction technology is developed to help enhance the spatial resolution of observational images for ground-based solar telescopes, such as speckle masking. Near real-time reconstruction performance is achieved on a high performance cluster using the Message Passing Interface (MPI). However, much time is spent in reconstructing solar subimages in such a speckle reconstruction. We design and implement a novel parallel method for speckle masking reconstruction of solar subimage on a shared memory machine using the OpenMP. Real tests are performed to verify the correctness of our codes. We present the details of several parallel reconstruction steps. The parallel implementation between various modules shows a great speed increase as compared to single thread serial implementation, and a speedup of about 2.5 is achieved in one subimage reconstruction. The timing result for reconstructing one subimage with 256×256 pixels shows a clear advantage with greater number of threads. This novel parallel method can be valuable in real-time reconstruction of solar images, especially after porting to a high performance cluster.