• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.12
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• pp.2363-2371
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• 2015

In this paper, we propose a new feature matching algorithm which is robust to the viewpoint change by using the FAST(Features from Accelerated Segment Test) feature detector and the SIFT(Scale Invariant Feature Transform) feature descriptor. The original FAST algorithm unnecessarily results in many feature points along the edges in the image. To solve this problem, we apply the principal curvatures for refining it. We use the SIFT descriptor to describe the extracted feature points and calculate the homography matrix through the RANSAC(RANdom SAmple Consensus) with the matching pairs obtained from the two different viewpoint images. To make feature matching robust to the viewpoint change, we classify the matching pairs by calculating the Euclidean distance between the transformed coordinates by the homography transformation with feature points in the reference image and the coordinates of the feature points in the different viewpoint image. Through the experimental results, it is shown that the proposed algorithm has better performance than the conventional feature matching algorithms even though it has much less computational load.

• The KIPS Transactions:PartB
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• v.17B no.5
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• pp.333-344
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• 2010

This paper addresses the way to compose paronamic images from images taken the same objects. With the spread of digital camera, the panoramic image has been studied to generate with its interest. In this paper, we propose a panoramic image generation method using scaling and rotation invariant features. First, feature points are extracted from input images and matched with a RANSAC algorithm. Then, after the perspective model is estimated, the input image is registered with this model. Since the SURF feature extraction algorithm is adapted, the proposed method is robust against geometric distortions such as scaling and rotation. Also, the improvement of computational cost is achieved. In the experiment, the SURF feature in the proposed method is compared with features from Harris corner detector or the SIFT algorithm. The proposed method is tested by generating panoramic images using $640{\times}480$ images. Results show that it takes 0.4 second in average for computation and is more efficient than other schemes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.9
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• pp.3196-3210
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• 2014

This paper presents a novel video metrology approach based on robust tracking. From videos acquired by an uncalibrated stationary camera, the foreground likelihood map is obtained by using the Codebook background modeling algorithm, and the multiple moving objects are tracked by a combined tracking algorithm. Then, we compute vanishing line of the ground plane and the vertical vanishing point of the scene, and extract the head feature points and the feet feature points in each frame of video sequences. Finally, we apply a single view mensuration algorithm to each of the frames to obtain height measurements and fuse the multi-frame measurements using RANSAC algorithm. Compared with other popular methods, our proposed algorithm does not require calibrating the camera, and can track the multiple moving objects when occlusion occurs. Therefore, it reduces the complexity of calculation and improves the accuracy of measurement simultaneously. The experimental results demonstrate that our method is effective and robust to occlusion.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.27 no.1
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• pp.667-675
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• 2009

Image matching is widely applied in image application areas, such as remote sensing and GIS. In general, the initial set of matching points always includes outlier which affect the accuracy of image matching. The purpose of this paper is to develop a robust approach for outlier detection and removal in order to keep accuracy in image matching applications. In this paper we use three automatic outlier detection techniques of backward matching and affine transformation, and RANSAC(RANdom SAmple Consensus) algorithm. Moreover, we calculate overlapping apply and steps block-based processing for fast and efficient image matching in pre-processing steps. The suggested approach in this paper has been applied to real frame image pairs and the results have been analyzed in terms of the robustness and the efficiency.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.3
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• pp.630-638
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• 2016

In this paper, a feature based panorama image generation algorithm using FAST(Features from Accelerated Segment Test) method that is faster than SIFT(Scale Invariant Feature Transform) and SURF(Speeded Up Robust Features) is proposed. Cylindrical projection is performed to generate natural panorama images with numerous images as input. The occurred error can be minimized by applying RANSAC(Random Sample Consensus) for the matching process. When we synthesize numerous images acquired from different camera angles, we use blending techniques to compensate the distortions by the heterogeneity of border line. In that way, we could get more natural synthesized panorama image. The proposed algorithm can generate natural panorama images regardless the order of input images and tilted images. In addition, the image matching can be faster than the conventional method. As a result of the experiments, distortion was corrected and natural panorama image was generated.

• Journal of Broadcast Engineering
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• v.20 no.5
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• pp.728-737
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• 2015

Stitching algorithm obtain a descriptor of the feature points extracted from multiple images, and create a single image through the matching process between the each of the feature points. In this paper, a feature extraction and matching techniques for the creation of a high-speed panorama using video input is proposed. Features from Accelerated Segment Test(FAST) is used for the feature extraction at high speed. A new feature point matching process, different from the conventional method is proposed. In the matching process, by tracking region containing the feature point through the Mean shift vector required for matching is obtained. Obtained vector is used to match the extracted feature points. In order to remove the outlier, the RANdom Sample Consensus(RANSAC) method is used. By obtaining a homography transformation matrix of the two input images, a single panoramic image is generated. Through experimental results, we show that the proposed algorithm improve of speed panoramic image generation compared to than the existing method.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.40-44
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• 2002

Precision correction is the process of geometrically aligning images to a reference coordinate system using GCPs(Ground Control Points). Many applications of remote sensing data, such as change detection, mapping and environmental monitoring, rely on the accuracy of precision correction. However it is a very time consuming and laborious process. It requires GCP collection, the identification of image points and their corresponding reference coordinates. At typical satellite ground stations, GCP collection requires most of man-powers in processing satellite images. A method of automatic registration of satellite images is demanding. In this paper, we propose a new algorithm for automatic precision correction by GCP chips and RANSAC(Random Sample Consensus). The algorithm is divided into two major steps. The first one is the automated generation of ground control points. An automated stereo matching based on normalized cross correlation will be used. We have improved the accuracy of stereo matching by determining the size and shape of match windows according to incidence angle and scene orientation from ancillary data. The second one is the robust estimation of mapping function from control points. We used the RANSAC algorithm for this step and effectively removed the outliers of matching results. We carried out experiments with SPOT images over three test sites which were taken at different time and look-angle with each other. Left image was used to select UP chipsets and right image to match against GCP chipsets and perform automatic registration. In result, we could show that our approach of automated matching and robust estimation worked well for automated registration.

• Journal of Korean Society for Geospatial Information Science
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• v.18 no.2
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• pp.93-97
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• 2010

This paper presents the improved KLT(Kanade-Lucas-Tomasi) of registration of Image sequence captured by camera mounted on unmanned helicopter assuming without camera attitude information. It consists of following procedures for the proposed image registration. The initial interested points are detected by characteristic curve matching via dynamic programming which has been used for detecting and tracking corner points thorough image sequence. Outliers of tracked points are then removed by using Random Sample And Consensus(RANSAC) robust estimation and all remained corner points are classified as inliers by homography algorithm. The rectified images are then resampled by bilinear interpolation. Experiment shows that our method can make the suitable registration of image sequence with large motion.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.2
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• pp.154-160
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• 2012

Recently, development of technologies for autonomous vehicles has been actively carried out. This paper proposes a computer vision system to recognize lanes, crosswalks, and stop lines for autonomous vehicles. This vision system first recognizes lanes required for autonomous driving using the RANSAC algorithm and the Kalman filter, and changes the viewpoint from the perspective-angle view of the street to the top-view using the fact that the lanes are parallel. Then in the reconstructed top-view image this system recognizes a crosswalk based on its geometrical characteristics and searches for a stop line within a region of interest in front of the recognized crosswalk. Experimental results show excellent performance of the proposed vision system in recognizing lanes, crosswalks, and stop lines.

• International journal of advanced smart convergence
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• v.4 no.2
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• pp.6-13
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• 2015

With the spread of various kinds of cameras such as digital cameras and DSLR and a growing interest in high-definition and high-resolution images, a method that synthesizes multiple images is being studied among various methods. High Dynamic Range (HDR) images store light exposure with even wider range of number than normal digital images. Therefore, it can store the intensity of light inherent in specific scenes expressed by light sources in real life quite accurately. This study suggests feature points synthesis algorithm to improve the performance of HDR panorama recognition method (algorithm) at recognition and coordination level through classifying the feature points for image recognition using more than one multi frames.