• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
• /
• 2005.11a
• /
• pp.59-80
• /
• 2005

This Paper Presents a Rational Function Model (RFM)-based image matching technique for IKONOS satellite imagery. This algorithm adopts the object-space approach and reduces the search space within the confined line-shaped area called the Piecewise Matching Line (PLM). Also, the detailed procedure of generating 3-D surface information using the Rational Function model Coefficients (RFCs) is introduced as an end-user point of view. As a result, the final generated Digital Elevation Model (DEM) using the proposed scheme shows a mean error of 2$\cdot$2 m and RMSE of 3$\cdot$8 m compared with that from 1:5000 digital map.

• Journal of the Korean Society for Precision Engineering
• /
• v.21 no.7
• /
• pp.83-91
• /
• 2004

The determination of camera position and orientation from known correspondences of 3D reference points and their images is known as pose estimation in computer vision or space resection in photogrammetry. This paper discusses estimation of transform parameters using the pattern matching method with 2D images only. In general, the 3D reference points or lines are needed to find out the 3D transform parameters, but this method is applied without the 3D reference points or lines. It uses only two images to find out the transform parameters between two image. The algorithm is simulated using Visual C＋＋ on Windows 98.

• Korean Journal of Remote Sensing
• /
• v.22 no.5
• /
• pp.439-443
• /
• 2006

Image co-registration is the process of overlaying two images of the same scene. One of which is a reference image, while the other (sensed image) is geometrically transformed to the one. Numerous methods were developed for the automated image co-registration and it is known as a timeconsuming and/or computation-intensive procedure. In order to improve efficiency and effectiveness of the co-registration of satellite imagery, this paper proposes a pre-qualified area matching, which is composed of feature extraction with Laplacian filter and area matching algorithm using correlation coefficient. Moreover, to improve the accuracy of co-registration, the outliers in the initial matching point should be removed. For this, two outlier detection techniques of studentized residual and modified RANSAC algorithm are used in this study. Three pairs of Landsat images were used for performance test, and the results were compared and evaluated in terms of robustness and efficiency.

• Proceedings of the KSRS Conference
• /
• 2002.10a
• /
• pp.34-39
• /
• 2002

Road information is very important for topographic mapping, transportation application, urban planning and other related application fields. Therefore, automatic detection of road networks from spatial imagery, such as aerial photos and satellite imagery can play a central role in road information acquisition. In this paper, we use least squares correlation matching alone for road center tracking and show that it works. We assumed that (bright) road centerlines would be visible in the image. We further assumed that within a same road segment, there would be only small differences in brightness values. This algorithm works by defining a template around a user-given input point, which shall lie on a road centerline, and then by matching the template against the image along the orientation of the road under consideration. Once matching succeeds, new match proceeds by shifting a matched target window further along road orientation at the target window. By repeating the process above, we obtain a series of points, which lie on a road centerline successively. A 1m resolution IKONOS images over Seoul and Daejeon were used for tests. The results showed that this algorithm could extract road centerlines in any orientation and help in fast and exact he ad-up digitization/vectorization of cartographic images.

• Proceedings of the Korea Contents Association Conference
• /
• 2003.11a
• /
• pp.290-297
• /
• 2003

A time-series database is a set of data sequences, each of which is a list of changing values corresponding to an object. Subsequence matching under time warping is defined as an operation that finds such subsequences whose time warping distance to a given query sequence are below a tolerance from a time-series database. In this paper, we first point out the characteristics of the previous methods for time-series sequence matching under time warping, and then discuss the approaches for applying them to whole matching as well as subsequence matching. Also, we perform quantitative performance evaluation via a series of experiments with real-life data. There have not been such researches in the literature that compare the performances of all the previous methods of subsequence matching under time warping. Thus, our results would be used as a good reference for showing their relative performances.

• ETRI Journal
• /
• v.34 no.1
• /
• pp.87-97
• /
• 2012

This study presents an automatic matching method for generating a dense, accurate, and discontinuity-preserved digital surface model (DSM) using multiple images acquired by an aerial digital frame camera. The proposed method consists of two main procedures: area-based multi-image matching (AMIM) and stereo-pair epipolar line matching (SELM). AMIM evaluates the sum of the normalized cross correlation of corresponding image points from multiple images to determine the optimal height of an object point. A novel method is introduced for determining the search height range and incremental height, which are necessary for the vertical line locus used in the AMIM. This procedure also includes the means to select the best reference and target images for each strip so that multi-image matching can resolve the common problem over occlusion areas. The SELM extracts densely positioned distinct points along epipolar lines from the multiple images and generates a discontinuity-preserved DSM using geometric and radiometric constraints. The matched points derived by the AMIM are used as anchor points between overlapped images to find conjugate distinct points using epipolar geometry. The performance of the proposed method was evaluated for several different test areas, including urban areas.

• The KIPS Transactions:PartB
• /
• v.11B no.3
• /
• pp.247-256
• /
• 2004

In this paper, we propose the prediction search algorithm for block matching using the temporal/spatial correlation of the video sequence and the renter-biased property of motion vectors The proposed algorithm determines the location of a better starting point for the search of an exact motion vector using the point of the smallest SAD(Sum of Absolute Difference) value by the predicted motion vector from the same block of the previous frame and the predictor candidate pint in each search region and the predicted motion vector from the neighbour blocks of the current frame. And the searching process after moving the starting point is processed a adaptive search pattern according to the magnitude of motion vector Simulation results show that PSNR(Peak-to-Signal Noise Ratio) values are improved up to the 0.75dB as depend on the video sequences and improved about 0.05∼0.34dB on an average except the FS (Full Search) algorithm.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.14 no.1
• /
• pp.402-410
• /
• 2013

In this paper, we propose a new fast block matching algorithm for block matching using the temporal and spatially correlation of the video sequence and local statistics of neighboring motion vectors. Since the temporal correlation of the video sequence between the motion vector of current block and the motion vector of previous block. The proposed algorithm determines the location of a better starting point for the search of an exact motion vector using the point of the smallest SAD(sum of absolute difference) value by the predicted motion vectors of neighboring blocks around the same block of the previous frame and the current frame and the predictor candidate point on each division region by binary-tree structure. Experimental results show that the proposed algorithm has the capability to dramatically reduce the search points and computing cost for motion estimation, comparing to fast FS(full search) motion estimation and other fast motion estimation.

• Korean Journal of Remote Sensing
• /
• v.34 no.6_1
• /
• pp.893-903
• /
• 2018

The necessity of automatic precise georeferencing is increasing with the increase applications of high-resolution satellite imagery. One of the methods for collecting ground control points (GCPs) for precise georeferencing is to use chip images obtained by extracting a subset of an image map such as an ortho-aerial image, and can be automated using an image matching technique. In this case, the importance of the image matching success rate is increased due to the limitation of the number of the chip images for the known reference points such as the unified control point. This study aims to propose a method to improve the success rate of image matching between KOMPSAT-3A images and GCP chip images from aerial ortho-images. We performed the image matching with 7 cases of band pair using KOMPSAT-3A panchromatic (PAN), multispectral (MS), pansharpened (PS) imagery and GCP chip images, then compared matching success rates. As a result, about 10-30% of success rate is increased to about 40-50% when using PS imagery by using PAN and MS imagery. Therefore, using PS imagery for image matching of KOMPSAT-3A images and aerial ortho-images would be helpful to improve the matching success rate.

• Korean Journal of Construction Engineering and Management
• /
• v.5 no.5 s.21
• /
• pp.151-162
• /
• 2004

Current methods for construction site modeling employ large, expensive laser range scanners that produce dense range point clouds of a scene from different perspectives. Days of skilled interpretation and of automatic segmentation may be required to convert the clouds to a finished CAD model. The dynamic nature of the construction environment requires that a real-time local area modeling system be capable of handling a rapidly changing and uncertain work environment. However, in practice, large, simple, and reasonably accurate embodying volumes are adequate feedback to an operator who, for instance, is attempting to place materials in the midst of obstacles with an occluded view. For real-time obstacle avoidance and automated equipment control functions, such volumes also facilitate computational tractability. In this research, a human operator's ability to quickly evaluate and associate objects in a scene is exploited. The operator directs a laser range finder mounted on a pan and tilt unit to collect range points on objects throughout the workspace. These groups of points form sparse range point clouds. These sparse clouds are then used to create geometric primitives for visualization and modeling purposes. Experimental results indicate that these models can be created rapidly and with sufficient accuracy for automated obstacle avoidance and equipment control functions.