• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.9B
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• pp.1697-1703
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• 1999

In this paper, we propose the fast block matching algorithm with half pel accuracy using the lower bound of mean absolute difference (MAD) at search point of half pel accuracy motion estimation. The proposed method uses the lower bound of MAD at search point of half pel accuracy which calculated from MAD's at search points of integer pel accuracy. We can reduce the computational complexity by executing the block matching operation only at the necessary search point. The points are selected when the lower bound of MAD at that point is smaller than reference MAD of integer pel motion estimation. Experimental results show that the proposed method can reduce the computational complexity considerably and keeping the same performance with conventional method.

• Proceedings of the IEEK Conference
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• 2000.06d
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• pp.43-46
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• 2000

In this paper, we propose a fast adaptive diamond search algorithm(FADS) for block matching motion estimation. Fast motion estimation algorithms reduce the computational complexity by using the UESA (Unimodal Error Search Assumption) that the matching error monotonically increases as the search moves away from the global minimum error. Recently many fast BMAs(Block Matching Algorithms) make use of the fact that the global minimum points in real world video sequences are centered at the position of zero motion. But these BMAs, especially in large motion, are easily trapped into the local minima and result in poor matching accuracy. So, we propose a new motion estimation algorithm using the spatial correlation among the adjacent blocks. We change the origin of search window according to the spatially adjacent motion vectors and their MAE(Mean Absolute Error). The computer simulation shows that the proposed algorithm has almost the same computational complexity with UCBDS(Unrestricted Center-Biased Diamond Search)〔1〕, but enhance PSNR. Moreover, the proposed algorithm gives almost the same PSNR as that of FS(Full Search), even for the large motion case, with half the computational load.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.935-936
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• 2008

Usually stereo matching methods have some limitations in finding out matching points specially at the boundaries of objects This paper proposes a new method which reduces noises incurred in the process of determining disparity with our energy-based stereo matching method. Our method exploits segmentation results so that pixels within the intra-region have similar disparity values.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.179-182
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• 2003

This paper proposes a new stereo matching algorithm using both the divide-and-conquer method and the DSI(Disparity Space Image) technique. Firstly, we find salient feature points on the each scanline of the left image and find the corresponding feature point at the right image. Then the problem of a scanline is divided into several subproblems. By this way, matching of the subintervals is implemented by using the DSI technique. The DSI technique for stereo matching process is a very efficient solution to find matches and occlusions simultaneously and it is very speedy. In addition, we apply three occluding patterns to process occluded regions, as a result, we reduce mismatches at the disparity discontinuity.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.11a
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• pp.226-230
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• 2002

This paper proposes a method of improvement of accuracy of projective transformation matrix for Image Mosaicing. Using shift theorem, we extracted global translation components between images and using translation components, we found matching points between images so we solve general matching point problem we extracted highly trusted matching point using RANSAC algorithm. we normalized matching point coordinates and improved accuracy of projective transformation matrix.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.10
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• pp.957-966
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• 2014

Video Stabilization For Vehicular Applications Is An Important Method Of Removing Unwanted Shaky Motions From Unstable Videos. In This Paper, An Improved Video Stabilization Method With Image Stitching Has Been Proposed. Scale Invariant Feature Transform (Sift) Matching Is Used To Calculate The New Position Of The Points In Next Frame. Image Stitching Is Done In Every Frame To Get Stabilized Frames To Provide Stable Video As Well As A Better Understanding Of The Previous Frame'S Position And Show The Surrounding Objects Together. The Computational Complexity Of Sift (Scale-Invariant Feature Transform) Is Reduced By Reducing The Sift Descriptors Size And Resticting The Number Of Keypints To Be Extracted. Also, A Modified Matching Procedure Is Proposed To Improve The Accuracy Of The Stabilization.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.920-923
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• 1996

This paper presents a new algorithm for the self-localization of a mobile robot using perspective invariant(Cross Ratio). Most of conventional model-based self-localization methods have some problems that data structure building, map updating and matching processes are very complex. Use of the simple cross ratio can be effective to the above problems. The algorithm is based on two basic assumptions that the ground plane is flat and two parallel walls are available. Also it is assumed that an environmental map is available for matching between the scene and the model. To extract an accurate steering angle for a mobile robot, we take advantage of geometric features such as vanishing points(V.P). Point features for computing cross ratios are extracted robustly using a vanishing point and the intersection points between floor and the vertical lines of door frames. The robustness and feasibility of our algorithms have been demonstrated through experiments in indoor environments using an indoor mobile robot, KASIRI-II(KAist SImple Roving Intelligence).

• Journal of Korea Game Society
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• v.17 no.4
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• pp.17-24
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• 2017

This paper describes the general process of making augmented reality game for real objects without markers. In this paper, point cloud data created by using slam technology is edited using a separate editing tool to optimize performance in mobile environment. Also, in the game execution stage, a lot of load is generated due to the extraction of feature points and the matching of descriptors. In order to reduce this, optical flow is used to track the matched feature points in the previous input image.

• International Journal of Advanced Culture Technology
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• v.9 no.4
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• pp.302-306
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• 2021

Indoor localization is one of the basic elements of Location-Based Service, such as indoor navigation, location-based precision marketing, spatial recognition of robotics, augmented reality, and mixed reality. We propose a Voxel Labeling-based visual positioning system using object simultaneous localization and mapping (SLAM). Our method is a method of determining a location through single image 3D cuboid object detection and object SLAM for indoor navigation, then mapping to create an indoor map, addressing it with voxels, and matching with a defined space. First, high-quality cuboids are created from sampling 2D bounding boxes and vanishing points for single image object detection. And after jointly optimizing the poses of cameras, objects, and points, it is a Visual Positioning System (VPS) through matching with the pose information of the object in the voxel database. Our method provided the spatial information needed to the user with improved location accuracy and direction estimation.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.41 no.1
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• pp.55-62
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• 2004

A semi-automatic registration process of determining specified points is presented, which is required to register brain MR images based on Talairach atlas. Generally, ten specified points that define Talairach coordinates are anterior commissure(AC), posterior commissure (PC), anterior feint (AP), posterior point (PP), superior point (SP), inferior point (IP), left point (LP), right point (RP) and two points for the midline of the brain. The suggested method reduces user interaction for S points, and finds the necessary points for registration in a more stable manner by finding AC and PC using two-level shape matching of the corpus callosum (CC) in an edge-enhanced brain M image. Remaining points are found using the intensity information of cutview.