• Journal of Korean Society for Geospatial Information Science
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• v.23 no.1
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• pp.81-88
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• 2015

Various satellite sensors having ranges of the visible, infrared, and thermal wavelengths have been launched due to the improvement of hardware technologies of satellite sensors development. According to the development of satellite sensors with various wavelength ranges, the fusion and integration of multisensor images are proceeded. Image matching process is an essential step for the application of multisensor images. Some algorithms, such as SIFT and SURF, have been proposed to co-register satellite images. However, when the existing algorithms are applied to extract matching points between optical and thermal images, high accuracy of co-registration might not be guaranteed because these images have difference spectral and spatial characteristics. In this paper, location of control points in a reference image is extracted by SURF, and then, location of their corresponding pairs is estimated from the correlation of the local similarity. In the case of local similarity, phase correlation method, which is based on fourier transformation, is applied. In the experiments by simulated, Landsat-8, and ASTER datasets, the proposed algorithm could extract reliable matching points compared to the existing SURF-based method.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.6
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• pp.58-67
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• 2009

A correspondence matching is one of the important tasks in computer vision, and it is not easy to find corresponding points in variable environment where a scale, rotation, view point and illumination are changed. A SURF(Speeded Up Robust Features) algorithm have been widely used to solve the problem of the correspondence matching because it is faster than SIFT(Scale Invariant Feature Transform) with closely maintaining the matching performance. However, because SURF considers only gray image and local geometric information, it is difficult to match corresponding points on the image where similar local patterns are scattered. In order to solve this problem, this paper proposes an extended SURF algorithm that uses the invariant color and global geometric information. The proposed algorithm can improves the matching performance since the color information and global geometric information is used to discriminate similar patterns. In this paper, the superiority of the proposed algorithm is proved by experiments that it is compared with conventional methods on the image where an illumination and a view point are changed and similar patterns exist.

• Proceedings of the Korean Society of Computer Information Conference
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• 2011.01a
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• pp.55-58
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• 2011

This paper proposes a method of occluded number recognition by matching interest points. Interest points of input pattern are found via SURF features extracting and matched to interest points of clusters in database following three steps: SURF matching, coordinate matching and SURF matching on coordinate matched points. Then the satisfied interest points are counted to compute matching rate of each cluster. The input pattern will be assigned to cluster having highest matching rate. We have experimented our method to different numerical fonts and got encouraging results.

• Journal of Advanced Navigation Technology
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• v.16 no.2
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• pp.394-400
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• 2012

SURF is a robust local invariant feature descriptor and has been used in many applications such as object recognition. Even though this algorithm has similar matching accuracy compared to the SIFT, which is another popular feature extraction algorithm, it has advantage in matching time. However, these descriptors do not consider relative location information of extracted interesting points to guarantee rotation invariance. Also, since they use gray image of original color image, they do not use the color information of images, either. In this paper, we propose a method for improving matching performance of SURF descriptor using the color and relative location information of interest points. The location information is built from the angles between the line connecting the centers of interest points and the orientation line constructed for the center of each interest points. For the color information, color histogram is constructed for the region of each interest point. We show the performance of our scheme through experiments.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.3
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• pp.46-53
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• 2011

This paper proposes a SURF (Speeded Up Robust Features) based face recognition method which is one of typical interest point extraction algorithms. In general, SURF based object recognition is performed in interest point extraction and matching. In this paper, although, proposed method is employed not only in interest point extraction and matching, but also in face image rotation and interest point verification. image rotation is performed to increase the number of interest points and interest point verification is performed to find interest points which were matched correctly. Although proposed SURF based face recognition method requires more computation time than PCA based one, it shows better recognition rate than PCA algorithm. Through this experimental result, I confirmed that interest point extraction algorithm also can be adopted in face recognition.

• Journal of IKEEE
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• v.17 no.1
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• pp.51-56
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• 2013

This thesis propose a improved matching method for implementing an ORB algorithm based multi-object recognition. SURF algorithm that is well known in the object recognition algorithms is robust in object recognition. However, there is a disadvantage for real time operation because, SURF implemention requires a lot of computation. Therefore we propose a modified ORB algorithm which shows the result of almost 70% speed improvement by improving matching part to recognize multi object on real time.

• Journal of the Institute of Convergence Signal Processing
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• v.12 no.1
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• pp.26-32
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• 2011

This paper proposes a new method to process panoramic image stitching using SURF(Speeded Up Robust Features). Panoramic image stitching is considered a problem of the correspondence matching. In computer vision, it is difficult to find corresponding points in variable environment where a scale, rotation, view point and illumination are changed. However, SURF algorithm have been widely used to solve the problem of the correspondence matching because it is faster than SIFT(Scale Invariant Feature Transform). In this work, we also describe an efficient approach to decreasing computation time through the homography estimation using RANSAC(random sample consensus). RANSAC is a robust estimation procedure that uses a minimal set of randomly sampled correspondences to estimate image transformation parameters. Experimental results show that our method is robust to rotation, zoom, Gaussian noise and illumination change of the input images and computation time is greatly reduced.

• Korean Journal of Remote Sensing
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• v.30 no.4
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• pp.431-444
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• 2014

As applications of spaceborne SAR imagery are extended, there are increased demands for accurate registrations for better understanding and fusion of radar images. It becomes common to adopt multi-resolution SAR images to apply for wide area reconnaissance. Geometric correction of the SAR images can be performed by using satellite orbit and attitude information. However, the inherent errors of the SAR sensor's attitude and ground geographical data tend to cause geometric errors in the produced SAR image. These errors should be corrected when the SAR images are applied for multi-temporal analysis, change detection applications and image fusion with other sensor images. The undesirable ground registration errors can be corrected with respect to the true ground control points in order to produce complete SAR products. Speeded Up Robust Feature (SURF) technique is an efficient algorithm to extract ground control points from images but is considered to be inappropriate to apply to SAR images due to high speckle noises. In this paper, an attempt is made to apply SURF algorithm to SAR images for image registration and fusion. Matched points are extracted with respect to the varying parameters of Hessian and SURF matching thresholds, and the performance is analyzed by measuring the imaging matching accuracies. A number of performance measures concerning image registration are suggested to validate the use of SURF for spaceborne SAR images. Various simulations methodologies are suggested the validate the use of SURF for the geometric correction and image registrations and it is shown that a good choice of input parameters to the SURF algorithm should be made to apply for the spaceborne SAR images of moderate resolutions.

• Journal of Broadcast Engineering
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• v.17 no.2
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• pp.388-399
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• 2012

In the paper, we propose enhanced feature extraction and matching methods for a mobile environment based on modified SURF. We propose three methods to reduce the computational complexity in a mobile environment. The first is to reduce the dimensions of the SURF descriptor. We compare the performance of existing 64-dimensional SURF with several other dimensional SURFs. The second is to improve the performance using the sign of the trace of the Hessian matrix. In other words, feature points are considered as matched if they have the same sign for the trace of the Hessian matrix, otherwise considered not matched. The last one is to find the best distance-ratio which is used to determine the matching points. We find the best distance-ratio through experiments, and it gives the relatively high accuracy. Finally, existing system which is based on normal SURF method is compared with our proposed system which is based on these three proposed methods. We present that our proposed system shows reduced response time while preserving reasonably good matching accuracy.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.2
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• pp.79-89
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• 2022

Images taken using drones have been applied to fields that require rapid decision-making as they can quickly construct high-quality 3D spatial information for small regions. To construct spatial information based on drone images, it is necessary to determine the relationship between images by extracting keypoints between adjacent drone images and performing image matching. Therefore, in this study, three study regions photographed using a drone were selected: a region where parking lots and a lake coexisted, a downtown region with buildings, and a field region of natural terrain, and the performance of AKAZE (Accelerated-KAZE), BRISK (Binary Robust Invariant Scalable Keypoints), KAZE, ORB (Oriented FAST and Rotated BRIEF), SIFT (Scale Invariant Feature Transform), and SURF (Speeded Up Robust Features) algorithms were analyzed. The performance of the keypoints extraction algorithms was compared with the distribution of extracted keypoints, distribution of matched points, processing time, and matching accuracy. In the region where the parking lot and lake coexist, the processing speed of the BRISK algorithm was fast, and the SURF algorithm showed excellent performance in the distribution of keypoints and matched points and matching accuracy. In the downtown region with buildings, the processing speed of the AKAZE algorithm was fast and the SURF algorithm showed excellent performance in the distribution of keypoints and matched points and matching accuracy. In the field region of natural terrain, the keypoints and matched points of the SURF algorithm were evenly distributed throughout the image taken by drone, but the AKAZE algorithm showed the highest matching accuracy and processing speed.