• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.5
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• pp.357-364
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• 2003

The main difficulty in estimating the fundamental matrix stems from the unavoidable outliers inherent in the given correspondence matches. Several researches showed that the estimation results are much dependent on selecting the corresponding points. These represent that it is important to solve the problems due to errors on the point locations and mismatches. In this paper, our analysis shows that if the evenly distributed corresponding points are selected, we can estimate a more precise fundamental matrix. This paper presents novel approaches to estimate the fundamental matrix by considering the inlier distributions. In order to select evenly distributed points, we divide the entire image into the subregions, and then examine the number of the inliers in each subregion and the area of each region. The simulation results showed that our consideration of the inlier distribution can provide a more precise estimation of the fundamental matrix.

• Journal of Korean Society for Geospatial Information Science
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• v.21 no.2
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• pp.11-17
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• 2013

Nowadays 3D models are used in diverse sectors. The 3D maps provide better reality than existing plane maps as well as diverse pieces of information that cannot be expected from the limited plane maps. A process proposed in this paper enables easy and quick production by replacing the expensive laser scanners for modeling by an improved digital camera stereo matching algorithm. The algorithm used in this study was a SURF algorithm contained in the OpenCV library. The unconformity points of the algorithm were eliminated using the homography conversion and epipolar lines. In addition, the improved algorithm was compared with the commercial program, and it showed a better performance than the commercial program. It is expected that the proposed method can contribute to the digital maps and 3D virtual reality because it enables easy and quick 3D modeling provided that the stereo matching conditions are met.

• Korean Journal of Remote Sensing
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• v.19 no.2
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• pp.159-169
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• 2003

This paper presents a method for automatic 3-d building reconstruction using high resolution aerial imagery. First, by using edge preserving filtering, noise is eliminated and then images are segmented by watershed algorithm, which preserves location of edge pixels. To extract line segments between control points from boundary of each region, we calculate curvature of each pixel on the boundary and then find the control points. Line segment linking is performed according to direction and length of line segments and the location of line segments is adjusted using gradient magnitudes of all pixels of the line segment. Coplanar grouping and pplygonal patch formation are performed per region by selecting 3-d line segments that are matched using epipolar geometry and flight information. The algorithm has been applied to high resolution aerial images and the results show accurate 3D building reconstruction.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.6
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• pp.228-237
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• 2013

In this paper, we propose a normalized cross correlation(NCC)-based multiview background subtraction method which is robust when an object and background have similar color. When the background of the capturing environment is not artificially composed, the regions in the background images which would be occluded by an object tends to have difference colors. The colors of those regions, however, becomes similar when an object enters the capturing environment. Based on this assumption, this paper proposes a concept of GoNCC(Graph of Normalized Cross Correlation). GoNCC is the distribution of NCC between a pixel in an image and pixels related by epipolar constraints with the pixel. The proposed multiview background subtraction method is performed by comparing GoNCC of the current images with the background images. To reduce computational complexity, we perform multiview background subtraction only to the pixels undetermined by single view background subtraction. Experimental results show that the proposed method is more robust to color similarity between an object and background than a single-view background subtraction method and a previous multiview background subtraction method.