• Korean Journal of Remote Sensing
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• v.36 no.4
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• pp.545-555
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• 2020

This paper focuses on automatic image registration between EO (Electro-Optical) and IR (InfraRed) satellite images with different spectral properties using block-based approach and simple preprocessing technique to enhance the performance of feature matching. If unpreprocessed EO and IR images from Kompsat-3A satellite were applied to local feature matching algorithms(Scale Invariant Feature Transform, Speed-Up Robust Feature, etc.), image registration algorithm generally failed because of few detected feature points or mismatched pairs despite of many detected feature points. In this paper, we proposed a new image registration method which improved the performance of feature matching with block-based registration process on 9-divided image and pre-processing technique based on adaptive histogram equalization. The proposed method showed better performance than without our proposed technique on visual inspection and I-RMSE. This study can be used for automatic image registration between various images acquired from different sensors.

• 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.

• The KIPS Transactions:PartB
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• v.12B no.3 s.99
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• pp.265-272
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• 2005

In this paper, we propose a technique which is speedily searching for correspondent points of two images using Voronoi-Distance, as an image registration method for feature based image mosaics. It extracts feature points in two images by the SUSAN corner detector, and then create not only the Voronoi Surface which has distance information among the feature points in the base image using a priority based Voronoi distance algorithm but also select the model area which has the maximum variance value of coordinates of the feature points in the model image. We propose a method for searching for the correspondent points in the Voronoi surface of the base image overlapped with the model area by use of the partitive search algorithm using queues. The feature of the method is that we can rapidly search for the correspondent points between adjacent images using the new Voronoi distance algorithm which has $O(width{\times}height{\times}logN)$ time complexity and the the partitive search algerian using queues which reduces the search range by a fourth at a time.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.6
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• pp.176-184
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• 2000

Reverse engineering (RE) is a process to create computer aided design (CAD) models from the scanned data of an existing part acquired using 3D position scanners. This paper proposes a novel methodology of extracting geometric features directly from a set of 3D scanned points, which utilizes the concepts of feature-based technology and artificial neural networks (ANNs). The use of ANN has enabled the development of a flexible feature-based RE application that can be trained to deal with various features. The following four main tasks were mainly investigated and implemented: (1) Data reduction; (2) edge detection; (3) ANN-based feature recognition; (4) feature extraction. This approach was validated with a variety of real industrial components. The test results show that the developed feature-based RE application proved to be suitable for reconstructing prismatic features such as block, pocket, step, slot, hole, and boss, which are very common and crucial in mechanical engineering products.

• Proceedings of the KIEE Conference
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• 1987.07b
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• pp.1157-1159
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• 1987

A syntactic and semantic approach is used to make type classification based on feature points(whorl, delta, core) and the shape of flow line around feature points. The image is divided into 30 by 30 subregions which are represented in the average direction and 4-tuple direction component. Next the relaxation process with singularity detection and convergency checking is performed. A set of semantic languages is used to describe the major flow line around the extracted feature points. LR(1) parser and feature transfer function are used to recognize the coded flow patterns. The 72 fingerprint impressions is used to test the proposed approach and the rate of the classification is about 93 percentages.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.6
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• pp.929-936
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• 1990

This paper describes the method of the Korean character recognition by means of feature points extraction. Also, the preprocessing neural chip for noise elimination, smoothing, thinning and feature point extraction has been designs. The subpatterns were separated by means of advanced index algorithm using mask, and recognized by means of feature points classification. The separation of the Korean character subpatterns was abtained about 97%, and the recognition of the Korean characters was abtained about 95%. The preprocessing neural chip was simulated on SPICE and layouted by double CMOS 2\ulcorner design rule.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.7
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• pp.767-778
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• 1992

Is discussed. A method to extract feature points and to synthesize human facial images In 3-Dmodel-based ceding system, faciai feature points are extracted automatically using some image processing techniques and the known knowledge for human face. A wire frame model matched to human face Is transformed according to the motion of point using the extracted feature points. The synthesized Image Is produced by mapping the texture of initial front view Image onto the trarnsformed wire frame. Experinent results show that the synthesitzed image appears with little unnaturalness.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.1
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• pp.179-186
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• 2010

In this research, an effective method for absolute positioning of feature points is proposed, which is applicable to geo-referencing of terrestrial LiDAR data scanned in dense urban areas. GPS positioning, common in absolute positioning, is apt to fail in the presence of signal disturbancein dense urban circumstances, while traditional surveying methods, including traversing and leveling, are generally more costly for wider areas. The idea is that reference points, marked on top of buildings, are surveyed by GPS positioning and then feature points are relatively positioned from the reference points. The present method, if laser scanning is accompanied, gets two advantages; one is that less feature points need to be surveyed because they can be substituredby reference points, and the other is that laser scanning can be more stably carried out. The present method was shown, from the experiments, to be cost-effective against traditional ones.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.9
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• pp.3177-3195
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• 2014

In many moving object detection problems of an aerial video, accurate and robust stabilization is of critical importance. In this paper, a novel accurate image alignment algorithm for aerial electronic image stabilization (EIS) is described. The feature points are first selected using optimal derivative filters based Harris detector, which can improve differentiation accuracy and obtain the precise coordinates of feature points. Then we choose the Delaunay Triangulation edges to find the matching pairs between feature points in overlapping images. The most "useful" matching points that belong to the background are used to find the global transformation parameters using the projective invariant. Finally, intentional motion of the camera is accumulated for correction by Sage-Husa adaptive filtering. Experiment results illustrate that the proposed algorithm is applied to the aerial captured video sequences with various dynamic scenes for performance demonstrations.

• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.628-631
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• 1988

This paper describes a fingerprint classification on the basis of feature points(whorl, core) and feature vector and uses a syntactic approach to identify the shape of flow line around the core. Fingerprint image is divided into 8 by 8 subregions and fingerprint region is separated from background. For each subregion of fingerprint region, the dominant ridge direction is obtained to use the slit window quantized in 8 direction and relaxation is performed to correct ridge direction code. Feature points(whorl, core, delta) are found from the ridge direction code. First classification procedure divides the types of fingerprint into 4 class based on whorl and cores. The shape of flow line around the core is obtained by tracing for the fingerprint which has one core or two core and is represented as string. If the string is acceptable by LR(1) parser, feature vector is obtained from feature points(whorl, core, delta) and the shape of flow line around the core. Feature vector is used hierarchically and linearly to classify fingerprint again. The experiment resulted in 97.3 percentages of sucessful classification for 71 fingerprint impressions.