• Journal of Korea Society of Industrial Information Systems
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• v.13 no.1
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• pp.57-67
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• 2008

In this paper, we present an adaptive block matching motion estimation using optical flow. In the proposed algorithm, we calculate the temporal and spatial gradient value for each pixel value from tile differential filter, and estimate the optical flow which is used to decide the location and the size of the search region from the gradient values by least square optical flow algorithm. In particular, the proposed algorithm showed a excellent performance with fast and complex motion sequences. From the computer simulation for various motion characteristic sequences. The proposed algorithm shows a significant enhancement of PSNR over previous blocking matching algorithms.

• The Journal of Korea Robotics Society
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• v.8 no.2
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• pp.82-91
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• 2013

This paper proposes a building recognition algorithm using watershed image segmentation algorithm and integrated region matching (IRM). To recognize a building, a preprocessing algorithm which is using Gaussian filter to remove noise and using canny edge extraction algorithm to extract edges is applied to input building image. First, images are segmented by watershed algorithm. Next, a region adjacency graph (RAG) based on the information of segmented regions is created. And then similar and small regions are merged. Second, a color distribution feature of each region is extracted. Finally, similar building images are obtained and ranked. The building recognition algorithm was evaluated by experiment. It is verified that the result from the proposed method is superior to color histogram matching based results.

• Journal of Information Technology Services
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• v.2 no.2
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• pp.111-119
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• 2003

In this paper, an efficient block matching algorithm which is based on the Block Sum Pyramid Algorithm (BSPA) is presented. The cost of BSPA[1] was reduced in the proposed algorithm by using l2 norm and partial distortion elimination technique. Motion estimation accuracy of the proposed algorithm is equal to that of BSPA. The efficiency of the proposed algorithm was verified by experimental results.

• The Journal of the Korea Contents Association
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• v.8 no.2
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• pp.33-40
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• 2008

In various manners, string pattern matching algorithm has been proven for prominence in speed of searching particular queries and keywords. Whereas, the existing algorithms are limited in terms of various pattern. In this paper, regular expression has been utilized to improve efficiency of pattern matching through efficient execution towards various pattern of queries including particular keywords. Such as this research would enable to search various harmful string pattern more efficiently, rather than matching simple keywords, which also implies excellent speed of string pattern matching compared to that of those existing algorism. In this research, the proposed string search engine generated from the LEX are more efficient than BM & AC algorithm for a string patterns search speed in cases of 1000 with more than patterns, but we have got similar results for the keywords pattern matching.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.8C
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• pp.680-688
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• 2010

In this paper, we present a dynamic programming(DP)-based stereo matching method using image segmentation algorithm. DP has been a classical and popular optimization method for various computer vision problems including stereo matching. However, the performance of conventional DP has not been satisfactory when it is applied to the stereo matching since the vertical correlation between scanned lines has not been properly considered. In the proposed algorithm, accurate edge information is first obtained from segmented image information then we considers the discontinuity of disparity and occlusions region based on the obtained edge information. The experimental results applied to the Middlebury stereo images demonstrate that the proposed algorithm has better performances in stereo matching than the previous DP based algorithms.

• Journal of Korea Multimedia Society
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• v.13 no.6
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• pp.825-832
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• 2010

This paper presents only a matching algorithm based on Delaunay triangulation and Parametrization from the extracted minutiae points. This method maps local neighborhood of points of two different point sets to unit-circle using topology information by Delaunay triangulation method from feature points of real fingerprint. Then, a linked convex polygon that includes an interior point is constructed as one-ring which is mapped to unit-circle using Parametrization that keep shape preserve. In local matching, each area of polygon in unit-circle is compared. If the difference of two areas are within tolerance, two polygons are consider to be matched and then translation, rotation and scaling factors for global matching are calculated.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.8
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• pp.1471-1476
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• 2010

This paper proposes the robust matching algorithm of the optical images. To do this, we propose two algorithms, that is, matching algorithms when the silhouette of images is detected and when it is not detected. Also, we propose the method for detecting the matching template and non-matching template from each optical image. And then, optical images are matched according to the coefficient of correlation represented by the similar degree between optical images. Finally, we show the effectiveness and feasibility of the proposed method through some experiments.

• Journal of Information Display
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• v.5 no.4
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• pp.31-37
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• 2004

In this paper, an effective method for reconstruction of stereoscopic image pair through the regularized adaptive disparity estimation is proposed. Although the conventional adaptive disparity window matching can sharply improve the PSNR of a reconstructed stereo image, but there still exist some problems of overlapping between the matching windows and disallocation of the matching windows, because the size of the matching window tend to changes adaptively in accordance with the magnitude of the feature values. In the proposed method, the problems relating to the conventional adaptive disparity estimation scheme can be solved and the predicted stereo image can be more effectively reconstructed by regularizing the extimated disparity vector with the neighboring disparity vectors. From the experimental results, it is found that the proposed algorithm show improvements the PSNR of the reconstructed right image by about 2.36${\sim}$2.76 dB, on average, compared with that of conventional algorithms.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.3
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• pp.74-83
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• 1997

In this paper, a new stereo matching algorithm is proposed which uses th econstrainted optimization technique and the matching measures between the segments extracted from zero-crossing edges. The initial matching measures and average disparities are calculated by the features of segments on the searching window of the left and right images. The matching measure is calculated by applying an exponential function using the differences of slope, overlapped length and intensity. The coherency constraint is that neighbouring image points corresponding to the same object should have nearly the same disparities. The matching measures are iteratively updated by applying the coherency constraint. Simulation results on various images show that the proposed algorithm more acculately extracts the segment disparity.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.414-417
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• 1993

The methods for stereo matching are divided into two techniques: area-based matching and feature-based matching. To find corresponding points by area-based method, it takes a lot of time because there are many points to be matched. Feature-based matching algorithm is often used because with this method it matches only some feature points so that the processing time is fast even though it requires interpolation after matching. In this paper, we propose the smart technique by which we makes features simpler than conventional methods to match an image pair by feature-based matching algorithm.