• The Journal of Korea Robotics Society
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• v.5 no.2
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• pp.85-92
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• 2010

In this paper, we propose a digital image stabilization technique using edge detection and Lucas-Kanade optical flow in order to minimize the motion of the shaken image. The accuracy of motion estimation based on block matching technique depends on the size of search window, which results in long calculation time. Therefore it is not applicable to real-time system. In addition, since the size of vector depends on that of block, it is difficult to estimate the motion which is bigger than the block size. The proposed method extracts the trust region using edge detection, to estimate the motion of some critical points in trust region based on Lucas-Kanade optical flow algorithm. The experimental results show that the proposed method stabilizes the shaking of motion image effectively in real time.

• Journal of Korea Multimedia Society
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• v.10 no.11
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• pp.1407-1416
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• 2007

This paper describes an algorithm for arbitrary point tracking using pyramidal optical flow. The optical flow is calculated based on the Lucas-Kanade's optical flow estimation in this paper. The image pyramid is employed to calculate a big motion while being sensitive to a small motion. Furthermore, a rectification process is proposed to reduce the error which is increased as it goes down to the lower level of the image pyramid. The accuracy of the optical flow estimation was increased by using some constraints and sub-pixel interpolation of the optical flow and this makes our algorithm to track points in which they do not have features such as edges or corners. The proposed algorithm is implemented and primary results are shown in this paper.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.835-838
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• 2008

In this paper, we present automatic face detection and tracking which is robustness in rotation and translation. Detecting a face image, we used Haar-like feature, which is fast detect facial image. Also tracking, we applied Lucas-Kanade feature tracker and KLT algorithm, which has robustness for rotated facial image. In experiment result, we confirmed that face detection and tracking which is robustness in rotation and translation.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.2 s.314
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• pp.93-101
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• 2007

To jointly optimize the spatial registration and the exposure compensation, an iterative registration algorithm, the Lucas-Kanade algorithm, is combined with an exposure compensation algorithm, which is based on the histogram transformation function. Based on a simple regression model, a nonparametric estimator, the empirical conditional mean, and its polynomial fitting are used as histogram transformation functions for the exposure compensation. Since the proposed algorithm is composed of separable optimization phases, the proposed algorithm is more advantageous than the joint approaches of Mann and Candocia in the aspect of implementation flexibility. The proposed algorithm performs a better registration for real images than the case of registration that does not consider the exposure difference.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.47 no.5
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• pp.24-32
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• 2010

In this study, we have proposed a pre-processing algorithm newly developed for improving the tracking efficiency of the optical flow method. The developed pre-processing algorithm consists of a median filter, binarization, morphology, canny edge, contour detecting and an approximation method. In order to evaluate whether the optical flow tracking capacity increases, this study applied the pre-processing algorithm to the Lucas-Kanade(LK) optical flow algorithm, and comparatively analyzed its images and tracking results with those of optical flow without the pre-processing algorithm and with the existing pre-processing algorithm(composed of median filter and histogram equalization). As a result, it was observed that the tracking performance derived from the LK optical flow algorithm with the pre-processing algorithm, shows better tracking accuracy, compared to the one without the pre-processing algorithm and the one with the existing pre-processing algorithm. It seems to have resulted by successful segmentation for characteristic areas and subdivision into inner and outer contour lines.

• IEMEK Journal of Embedded Systems and Applications
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• v.9 no.4
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• pp.219-227
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• 2014

Natural disasters such as a ground collapse and a landslide have broken out due to the climate change of the Korea and the reckless expansion of cities and roads. The climate changes and the reckless urbanization have made the ground weak. Thus, it is important to keep a close eye on the highly weakened landslide and to prevent its natural disasters. In order to prevent these disasters, this paper presents a system of recognizing the road slide condition by measuring the displacements using laser scanner instrument. The previous system of monitoring the road slide has some problems as inaccurate recognition due to using only images from a camera, or expensive system such as artificial satellites and aircraft systems. To solve this problem, our proposed system uses the 3D range data from the laser scanner for measuring the accurate displacement of the road slide and optical flows from the Lucas-Kanade algorithm for recognizing the road slide in the image.

• Smart Structures and Systems
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• v.31 no.4
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• pp.409-419
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• 2023

Structural integrity can be accessed from dynamic deformations of structures. Moreover, dynamic deformations can be acquired from non-contact sensors such as video cameras. Kanade-Lucas-Tomasi (KLT) algorithm is one of the commonly used methods for motion tracking. However, averaging throughout the extracted features would induce bias in the measurement. In addition, pixel-wise measurements can be converted to physical units through camera intrinsic. Still, the depth information is unreachable without prior knowledge of the space information. The assigned homogeneous coordinates would then mismatch manually selected feature points, resulting in measurement errors during coordinate transformation. In this study, a two-stage optimization method for video-based measurements is proposed. The manually selected feature points are first optimized by minimizing the errors compared with the homogeneous coordinate. Then, the optimized points are utilized for the KLT algorithm to extract displacements through inverse projection. Two additional criteria are employed to eliminate outliers from KLT, resulting in more reliable displacement responses. The second-stage optimization subsequently fine-tunes the geometry of the selected coordinates. The optimization process also considers the number of interpolation points at different depths of an image to reduce the effect of out-of-plane motions. As a result, the proposed method is numerically investigated by using a truss bridge as a physics-based graphic model (PBGM) to extract high-accuracy displacements from recorded videos under various capturing angles and structural conditions.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.40 no.1
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• pp.35-42
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• 2012

In this study, a feature-points-tracking algorithm is suggested using a particle filter for vision based navigation system. By applying a dynamic model of the feature point, the tracking performance is increased in high dynamic condition, whereas a conventional KLT (Kanade-Lucas-Tomasi) cannot give a solution. Futhermore, the particle filter is introduced to cope with irregular characteristics of vision data. Post-processing of recorded vision data shows that the tracking performance of suggested algorithm is more robust than that of KLT in high dynamic condition.

• The Journal of the Korea Contents Association
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• v.17 no.3
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• pp.42-52
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• 2017

Panorama image is a single image obtained by combining images taken at several viewpoints through matching of corresponding points. Existing panoramic image generation methods that find the corresponding points are extracting local invariant feature points in each image to create descriptors and using descriptor matching algorithm. In the case of video sequence, frames may be a lot, so therefore it may costs significant amount of time to generate a panoramic image by the existing method and it may has done unnecessary calculations. In this paper, we propose a method to quickly create a single panoramic image from a video sequence. By assuming that there is no significant changes between frames of the video such as in locally, we use the FAST algorithm that has good repeatability and high-speed calculation to extract feature points and the Lucas-Kanade algorithm as each feature point to track for find the corresponding points in surrounding neighborhood instead of existing descriptor matching algorithms. When homographies are calculated for all images, homography is changed around the center image of video sequence to warp images and obtain a planar panoramic image. Finally, the spherical panoramic image is obtained by performing inverse transformation of the spherical coordinate system. The proposed method was confirmed through the experiments generating panorama image efficiently and more faster than the existing methods.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.1
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• pp.70-77
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• 2014

This paper proposes a novel localization algorithm based on ego-motion which used Lucas-Kanade Optical Flow and warping image obtained through fish-eye lenses mounted on the robots. The omnidirectional image sensor is a desirable sensor for real-time view-based recognition of a robot because the all information around the robot can be obtained simultaneously. The preprocessing (distortion correction, image merge, etc.) of the omnidirectional image which obtained by camera using reflect in mirror or by connection of multiple camera images is essential because it is difficult to obtain information from the original image. The core of the proposed algorithm may be summarized as follows: First, we capture instantaneous $360^{\circ}$ panoramic images around a robot through fish-eye lenses which are mounted in the bottom direction. Second, we extract motion vectors using Lucas-Kanade Optical Flow in preprocessed image. Third, we estimate the robot position and angle using ego-motion method which used direction of vector and vanishing point obtained by RANSAC. We confirmed the reliability of localization algorithm using ego-motion based on fisheye warping image through comparison between results (position and angle) of the experiment obtained using the proposed algorithm and results of the experiment measured from Global Vision Localization System.