• Journal of Korean Society for Geospatial Information Science
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• v.22 no.2
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• pp.17-24
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• 2014

This paper proposes an automatic extraction method of control point based on Geospatial Web Service. The proposed method consists of 3 steps. 1) The first step is to acquires reference data using the Geospatial Web Service. 2) The second step is to finds candidate control points in reference data and the target image by SURF algorithm. 3) By using RANSAC algorithm, the final step is to filters the correct matching points of candidate control points as final control points. By using the Geospatial Web Service, the proposed method increases operation convenience, and has the more extensible because of following the OGC Standard. The proposed method has been tested for SPOT-1, SPOT-5, IKONOS satellite images and has been used military standard data as reference data. The proposed method yielded a uniform accuracy under RMSE 5 pixel. The experimental results proved the capabilities of continuous improvement in accuracy depending on the resolution of target image, and showed the full potential of the proposed method for military purpose.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.4
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• pp.116-123
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• 2009

Feature point matching, which is finding the corresponding points from two images with different viewpoint, has been used in various vision-based applications and the demand for the real-time operation of the matching is increasing these days. This paper presents a real-time feature point matching method by using a local descriptor derived by Zernike moments. From an input image, we find a set of feature points by using an existing fast corner detection algorithm and compute a local descriptor derived by Zernike moments at each feature point. The local descriptor based on Zernike moments represents the properties of the image patch around the feature points efficiently and is robust to rotation and illumination changes. In order to speed up the computation of Zernike moments, we compute the Zernike basis functions with fixed size in advance and store them in lookup tables. The initial matching results are acquired by an Approximate Nearest Neighbor (ANN) method and false matchings are eliminated by a RANSAC algorithm. In the experiments we confirmed that the proposed method matches the feature points in images with various transformations in real-time and outperforms existing methods.

• Journal of Electrical Engineering and Technology
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• v.12 no.1
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• pp.428-435
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• 2017

In this paper, we propose a novel motion field estimation algorithm for which a U-disparity map and forward-and-backward error removal are applied in a vehicular environment. Generally, a motion exists in an image obtained by a camera attached to a vehicle by vehicle movement; however, the obtained motion vector is inaccurate because of the surrounding environmental factors such as the illumination changes and vehicles shaking. It is, therefore, difficult to extract an accurate motion vector, especially on the road surface, due to the similarity of the adjacent-pixel values; therefore, the proposed algorithm first removes the road surface region in the obtained image by using a U-disparity map, and uses then the optical flow that represents the motion vector of the object in the remaining part of the image. The algorithm also uses a forward-backward error-removal technique to improve the motion-vector accuracy and a vehicle's movement is predicted through the application of the RANSAC (RANdom SAmple Consensus) to the previously obtained motion vectors, resulting in the generation of a motion field. Through experiment results, we show that the performance of the proposed algorithm is superior to that of an existing algorithm.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.7
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• pp.685-692
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• 2016

In this paper, we not only take advantage of the SIFT features in banknote recognition, which has robustness to illumination changes, geometric rotation as well as scale changes, but also propose the hierarchical banknote recognition algorithm, which comprised of feature vector extraction from the frame grabbed image of the banknotes, and matching to the prepared data base of multinational banknotes by ANN algorithm. The images of banknote under the developed UV, IR and white illumination are used so as to extract the SIFT features peculiar to each banknotes. These SIFT features are used in recognition of the nationality as well as face value. We confirmed successful function of the proposed algorithm by applying the proposed algorithm to the banknotes of Korean and USD as well as EURO.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.5
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• pp.457-461
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• 2013

This paper proposes an image feature-based real-time RGB-D (Red-Green-Blue Depth) 3D SLAM (Simultaneous Localization and Mapping) system. RGB-D data from Kinect style sensors contain a 2D image and per-pixel depth information. 6-DOF (Degree-of-Freedom) visual odometry is obtained through the 3D-RANSAC (RANdom SAmple Consensus) algorithm with 2D image features and depth data. For speed up extraction of features, parallel computation is performed with GPU acceleration. After a feature manager detects a loop closure, a graph-based SLAM algorithm optimizes trajectory of the sensor and builds a 3D point cloud based map.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.1
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• pp.17-23
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• 2016

This paper presents the implementation of altitude and attitude measurement algorithm using stereo camera for an unmanned aerial vehicle (UAV). Depth images are generated by calibrating the stereo cameras, and converted into 3D point cloud data. By applying a plane fitting algorithm to the resultant point cloud, altitude from ground level, and roll and pitch angles are extracted. To verify the performance, experimental results are provided by comparing with those of the motion caption system.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.1468-1471
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• 2012

It is always a dream to recreate the experience of a particular place, the Panorama Virtual Reality has been interpreted as a kind of technology to create virtual environments and the ability to maneuver angle for and select the path of view in a dynamic scene. In this paper we examined an efficient algorithm for Image registration and stitching of captured imaged from a video stream. Two approaches are studied in this paper. First, dynamic programming is used to spot the ideal key points, match these points to merge adjacent images together, later image blending is use for smooth color transitions. In second approach, FAST and SURF detection are used to find distinct features in the images and a nearest neighbor algorithm is used to match corresponding features, estimate homography with matched key points using RANSAC. The paper also covers the automatically choosing (recognizing, comparing) images to stitching method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.68 no.1
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• pp.182-188
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• 2019

Autonomous mobile robots need SLAM (simultaneous localization and mapping) to look for the location and simultaneously to make the map around the location. In order to achieve visual SLAM, it is necessary to form an algorithm that detects and extracts feature points from camera images, and gets the camera pose and 3D points of the features. In this paper, we propose MPROSAC algorithm which combines MSAC and PROSAC, and compare the performance of optimization method and the filtering method for feature-based monocular visual SLAM. Sparse Bundle Adjustment (SBA) is used for the optimization method and the extended Kalman filter is used for the filtering method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.11
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• pp.5419-5435
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• 2017

The construction of panoramic images on smartphones and low-powered devices is a challenging task. In this paper, we propose a new approach for smoothly stitching images on mobile phones in the presence of moving objects in the scene. Our main contributions include handling moving object problems, reducing processing time, and generating rectangular panoramic images. First, unique and robust feature points are extracted using fast ORB method and a feature matching technique is applied to match the extracted feature points. After obtaining good matched feature points, we employ the non-deterministic RANSAC algorithm to discard wrong matches, and the hommography transformation matrix parameters are estimated with the algorithm. Afterward, we determine precise overlap regions of neighboring images and calculate their absolute differences. Then, thresholding operation and noise removal filtering are applied to create a mask of possible moving object regions. Sequentially, an optimal seam is estimated using dynamic programming algorithm, and a combination of linear blending with the mask information is applied to avoid seam transition and ghosting artifacts. Finally, image-cropping operation is utilized to obtain a rectangular boundary image from the stitched image. Experiments demonstrate that our method is able to produce panoramic images quickly despite the existence of moving objects.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.1
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• pp.1-7
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• 2016

ADAS (Advanced Driver Assistance Systems) requires not only real-time robust lane detection, both straight and curved, but also predicting upcoming steering direction by detecting the curvature of lanes. In this paper, a curvature lane detection algorithm is proposed to enhance the accuracy and detection rate based on using inverse perspective images and Gaussian Mixture Model (GMM) to segment the lanes from the background under various illumination condition. To increase the speed and accuracy of the lane detection, this paper used template matching, RANSAC and proposed post processing method. Through experiments, it is validated that the proposed algorithm can detect both straight and curved lanes as well as predicting the upcoming direction with 92.95% of detection accuracy and 50fps speed.