• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.5
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• pp.179-186
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• 2021

For stereo matching based on deep learning, the design of network structure is crucial to the calculation of matching cost, and the time-consuming problem of convolutional neural network in image processing also needs to be solved urgently. In this paper, a method of stereo matching using sparse loss volume in parallax dimension is proposed. A sparse 3D loss volume is constructed by using a wide step length translation of the right view feature map, which reduces the video memory and computing resources required by the 3D convolution module by several times. In order to improve the accuracy of the algorithm, the nonlinear up-sampling of the matching loss in the parallax dimension is carried out by using the method of multi-category output, and the training model is combined with two kinds of loss functions. Compared with the benchmark algorithm, the proposed algorithm not only improves the accuracy but also shortens the running time by about 30%.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.7C
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• pp.628-633
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• 2010

This paper proposes a block matching algorithm to improve the sub-block matching algorithm that uses partial sub-blocks being a great contribution to the block matching. Unlike the conventional algorithm using the one sub-block group the proposed algorithm uses two sub-block groups. The matching using the small group selects a candidate block to be a good possibility of a similar block with a small computation cost and the additional matching using the large group in the selected block decreases a wrong matching. Simulation results show that the proposed algorithm always has good image quality at the same computation cost as compared to the conventional algorithm and it has an outstanding performance at the matching using a few sub-blocks.

• Korean Journal of Remote Sensing
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• v.34 no.6_2
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• pp.1251-1260
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• 2018

High-quality sea-ice surface models generated from aerial images can be used effectively as field data for developing satellite-based remote sensing methods but also as analysis data for understanding geometric variations of Arctic sea-ice. However, the lack of texture information on sea-ice surfaces can reduce the accuracy of image matching. In this paper, we analyze the performance of matching cost functions for homogeneous sea-ice surfaces as a part of high-quality sea-ice surface model generation. The matching cost functions include sum of squared differences (SSD), normalized cross-correlation (NCC), and zero-mean normalized cross-correlation (ZNCC) in image domain and phase correlation (PC), orientation correlation (OC), and gradient correlation (GC) in frequency domain. In order to analyze the matching performance for texture changes clearly and objectively, a new evaluation methodology based on the principle of object-space matching technique was introduced. Experimental results showed that it is possible to secure reliability and accuracy of image matching only when optimal search windows are variably applied to each matching point in textureless regions such as sea-ice surfaces. Among the matching cost functions, NCC and ZNCC showed the best performance for texture changes.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.4
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• pp.387-394
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• 2016

A stereo matching method is one of the ways to obtain a depth value from two dimensional images. This method estimates the depth value of target images using stereo images which have two different viewpoints. In the result of stereo matching, the depth value is represented by a disparity value. The disparity means a distance difference between a current pixel in one side of stereo images and its corresponding point in the other side of stereo images. The stereo matching in a homogeneous region is always difficult to find corresponding points because there are no textures in that region. In this paper, we propose a novel matching equation using the distance transform to estimate accurate disparity values in the homogeneous region. The distance transform calculates pixel distances from the edge region. For this reason, pixels in the homogeneous region have specific values when we apply this transform to pixels in that region. Therefore, the stereo matching method using the distance transform improves the matching accuracy in the homogeneous regions. In addition, we also propose an adaptive matching cost computation using a kernel of one dimensional array depending on the characteristic of regions in the image. In order to aggregate the matching cost, we apply a cross-scale cost aggregation method to our proposed method. As a result, the proposed method has a lower average error rate than that of the conventional method in all regions.

• Korean Journal of Remote Sensing
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• v.35 no.3
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• pp.401-413
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• 2019

The purpose of this study is to generate DSM using the stereo matching algorithm of worldview-1 stereo images and verify the accuracy of the generated DSM. To generate DSM, RPC block modeling was performed to correct RPC errors, and image matching was performed using SGM, which is a stereo matching algorithm after the epipolar image was generated. The COST for SGM was calculated by using CENSUS, and 4-paths and 8-paths were applied for COST aggregation in SGM. To verify the quality and accuracy of the generated DSM, it was compared with the LiDAR-derived DSM and the DSM generated by commercial SW. The results showed that the vertical accuracy of the generated DSM using 4-paths of COST aggregation was 1.647 m to 3.689 m (RMSE). In case of using 8-paths of COST aggregation was 1.550 m to 3.106 m (RMSE).

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.589-592
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• 1999

Matching is a key problem in computer vision, image analysis and pattern recognition. In this paper a multiscale image matching algorithm by wavelet local extrema is proposed. This algorithm is based on the multiscale wavelet transform of the curvature which can utilize both the information of local extrema positions and magnitudes of transform results. This method has advantages in computational cost to a single scale image matching. It is also rotation-, translation-, and scale-independent image matching method. This matching can be used for the recognition of occluded objects.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.6
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• pp.1194-1199
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• 2009

Stereo vision system is useful method for inferring 3D depth information from two or more images. So it has been the focus of attention in this field for a long time. Stereo matching is the process of finding correspondence points in two or more images. A central problem in a stereo matching is that it is difficult to satisfy both the computation time problem and accuracy at the same time. To resolve this problem, we proposed a new stereo matching technique using weighted cost aggregation. To begin with, we extract the weight in given stereo images based on features. We compute the costs of the pixels in a given window using correlation of weighted color and brightness information. Then, we match pixels in a given window between the reference and target images of a stereo pair. To demonstrate the effectiveness of the algorithm, we provide experimental data from several synthetic and real scenes. The experimental results show the improved accuracy of the proposed method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.396-399
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• 2009

Stereo vision system is useful method for inferring 3D depth information from two or more images. So it has been the focus of attention in this field for a long time. Stereo matching is the process of finding correspondence points in two or more images. A central problem in a stereo matching is that it is difficult to satisfy both the computation time problem and accuracy at the same time. To resolve this problem, we proposed a new stereo matching technique using weighted cost aggregation. To begin with, we extract the weight in given stereo images based on features. We compute the costs of the pixels in a given window using correlation of weighted color, brightness and distance information. Then, we match pixels in a given window between the reference and target images of a stereo pair. To demonstrate the effectiveness of the algorithm, we provide experimental data from several synthetic and real scenes. The experimental results show the improved accuracy of the proposed method.

• Korean Journal of Remote Sensing
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• v.12 no.1
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• pp.43-59
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• 1996

The objective of this research is to investigate the potential of relational matching in one of the fundamental photogrammetric processes, that is initial approximation problem. The automatic relative orientation procedures of aerial stereopairs have been investigated. The fact that the existing methods suffer from approximations, distortions (geometric and radiometric), occlusions, and breaklines is the motivation to investigate relational matching which appears to be a much more general solution. An elegant way of solving the initial approximation problem by using distinct(special) relationship from relational description is suggested and experimented. As for evaluation function, the cost function was implemented. The detection of erroneous matching is incorporated as a part of proposed relational matching scheme. Experiments with real urban area images where large numbers of repetitive patterns, breaklines, and occluded areas are present prove the feasibility of implementation of the proposed relational matching scheme. The investigation of relational matching in the domain of image matching problem provides advantages and disadvantages over the existing image matching methods and shows the future area of development and implementation of relational matching in the field of digital photogrammetry.

• Journal of Broadcast Engineering
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• v.20 no.4
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• pp.641-644
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• 2015

In this paper, we propose a precise frame-level near-duplicate video matching algorithm. First, a binary frame descriptor for near-duplicate video matching is proposed. The binary frame descriptor divides a frame into patches and represent the relations between patches in bits. Seconds, we formulate a cost function for the matching, composed of matching costs and compensatory costs. Then, we roughly determine initial matchings and refine the matchings iteratively to minimize the cost function. Experimental results demonstrate that the proposed algorithm provides efficient performance for frame-level near duplicate video matching.