• Journal of Internet Computing and Services
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• v.10 no.4
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• pp.13-23
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• 2009

In this paper we propose an approach for localizing objects in satellite images. Our method exploits matching features based on description vectors. We applied Scale Invariant Feature Transform (SIFT) to object localization. First, we find keypoints of the satellite images and the objects and generate description vectors of the keypoints. Next, we calculate the similarity between description vectors, and obtain matched keypoints. Finally, we weight the adjacent pixels to the keypoints and determine the location of the matched object. The experiments of object localization by using SIFT show good results on various scale and affine transformed images. In this paper the proposed methods use Google Earth satellite images.

• Journal of Korea Multimedia Society
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• v.18 no.1
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• pp.17-24
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• 2015

In this paper, a novel scheme for correcting rotated region in medical images using SIFT(Scale Invariant Feature Transform) algorithm is presented. Using the feature extraction function of SIFT, the rotation angle of rotated object in medical images is calculated as follows. First, keypoints of both reference and rotated medical images are extracted by SIFT. Second, the matching process is performed to the keypoints located at the predetermined ROI(Region Of Interest) at which objects are not cropped or added by rotating the image. Finally, degrees of matched keypoints are calculated and the rotation angle of the rotated object is determined by averaging the difference of the degrees. The simulation results show that the proposed scheme has excellent performance for correcting the rotated region in medical images.

• Journal of KIISE
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• v.42 no.1
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• pp.93-96
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• 2015

The SIFT method is well-known for robustness against various image transformations, and is widely used for image retrieval and matching. The SIFT method extracts keypoints using scale space analysis, which is different from conventional keypoint detection methods that depend only on the image space. The SIFT method has also been extended to use higher-order scale space derivatives for increasing the number of keypoints detected. Such detection of additional keypoints detected was shown to provide performance gain in image retrieval experiments. Herein, a sigma based normalization method for keypoint detection is introduced using higher-order scale space derivatives.

• Journal of Broadcast Engineering
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• v.23 no.4
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• pp.559-573
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• 2018

Recently, one of the most actively applied image media in the most fields such as virtual reality (VR) is omni-directional or panorama image. This image is generated by stitching images obtained by various methods. In this process, it takes the most time to extract keypoints necessary for stitching. In this paper, we analyze the parameters involved in the extraction of SIFT keypoints with the aim of reducing the computation time for extracting the most widely used SIFT keypoints. The parameters considered in this paper are the initial standard deviation of the Gaussian kernel used for Gaussian filtering, the number of gaussian difference image sets for extracting local extrema, and the number of octaves. As the SIFT algorithm, the Lowe scheme, the originally proposed one, and the Hess scheme which is a convolution cascade scheme, are considered. First, the effect of each parameter value on the computation time is analyzed, and the effect of each parameter on the stitching performance is analyzed by performing actual stitching experiments. Finally, based on the results of the two analyses, we extract parameter value set that minimize computation time without degrading.

• Journal of Digital Convergence
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• v.11 no.2
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• pp.299-307
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• 2013

The aim of this paper is the improvement of identification performance and the reduction of computational quantities in the face recognition system based on SIFT-Grid. Firstly, we propose a composition method of integrated template by removing similar SIFT keypoints and blending different keypoints in variety training images of one face class. The integrated template is made up of computation of similarity matrix and threshold-based histogram from keypoints in a same sub-region which divided by applying SIFT-Grid of training images. Secondly, we propose a computation method of similarity for identify of test image from composed integrated templates efficiently. The computation of similarity is performed that a test image to compare one-on-one with the integrated template of each face class. Then, a similarity score and a threshold-voting score calculates according to each sub-region. In the experimental results of face recognition tasks, the proposed methods is founded to be more accurate than both two other methods based on SIFT-Grid, also the computational quantities are reduce.

• Journal of information and communication convergence engineering
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• v.12 no.4
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• pp.263-270
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• 2014

Corner detection and feature extraction are essential aspects of computer vision problems such as object recognition and tracking. Feature detectors such as Scale Invariant Feature Transform (SIFT) yields high quality features but computationally intensive for use in real-time applications. The Features from Accelerated Segment Test (FAST) detector provides faster feature computation by extracting only corner information in recognising an object. In this paper we have analyzed the efficient object detection algorithms with respect to efficiency, quality and robustness by comparing characteristics of image detectors for corner detector and feature extractors. The simulated result shows that compared to conventional SIFT algorithm, the object recognition system based on the FAST corner detector yields increased speed and low performance degradation. The average time to find keypoints in SIFT method is about 0.116 seconds for extracting 2169 keypoints. Similarly the average time to find corner points was 0.651 seconds for detecting 1714 keypoints in FAST methods at threshold 30. Thus the FAST method detects corner points faster with better quality images for object recognition.

• Journal of Broadcast Engineering
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• v.26 no.1
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• pp.26-38
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• 2021

In this paper, DLKF (Deep Learning Keypoint Filtering), the deep learning-based keypoint filtering method for the rapidization of the image registration method for remote sensing images is proposed. The complexity of the conventional feature-based image registration method arises during the feature matching step. To reduce this complexity, this paper proposes to filter only the keypoints detected in the artificial structure among the keypoints detected in the keypoint detector by ensuring that the feature matching is matched with the keypoints detected in the artificial structure of the image. For reducing the number of keypoints points as preserving essential keypoints, we preserve keypoints adjacent to the boundaries of the artificial structure, and use reduced images, and crop image patches overlapping to eliminate noise from the patch boundary as a result of the image segmentation method. the proposed method improves the speed and accuracy of registration. To verify the performance of DLKF, the speed and accuracy of the conventional keypoints extraction method were compared using the remote sensing image of KOMPSAT-3 satellite. Based on the SIFT-based registration method, which is commonly used in households, the SURF-based registration method, which improved the speed of the SIFT method, improved the speed by 2.6 times while reducing the number of keypoints by about 18%, but the accuracy decreased from 3.42 to 5.43. Became. However, when the proposed method, DLKF, was used, the number of keypoints was reduced by about 82%, improving the speed by about 20.5 times, while reducing the accuracy to 4.51.

• Journal of KIISE:Databases
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• v.35 no.4
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• pp.345-355
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• 2008

SIFT is popularly used in computer vision application such as object recognition, motion tracking, and 3D reconstruction among various shape descriptors. However, it is not easy to apply SIFT into the image similarity search as it is since it uses many high dimensional keypoint vectors. In this paper, we present a SIFT based image similarity search method using an edge image pyramid and an interesting region detection. The proposed method extracts keypoints, which is invariant to contrast, scale, and rotation of image, by using the edge image pyramid and removes many unnecessary keypoints from the image by using the hough transform. The proposed hough transform can detect objects of ellipse type so that it can be used to find interesting regions. Experimental results demonstrate that the retrieval performance of the proposed method is about 20% better than that of traditional SIFT in average recall.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.45 no.6
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• pp.49-59
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• 2008

SIFT(Scale Invariant Feature Transform) is an algorithm to extract vectors at pixels around keypoints, in which the pixel colors are very different from neighbors, such as vortices and edges of an object. The SIFT algorithm is being actively researched for various image processing applications including 3-D image constructions, and its most computation-intensive stage is a keypoint localization. In this paper, we develope a fixed-point model of the keypoint localization and propose its efficient hardware architecture for embedded applications. The bit-length of key variables are determined based on two performance measures: localization accuracy and error rate. Comparing with the original algorithm (implemented in Matlab), the accuracy and error rate of the proposed fixed point model are 93.57% and 2.72% respectively. In addition, we found that most of missing keypoints appeared at the edges of an object which are not very important in the case of keypoints matching. We estimate that the hardware implementation will give processing speed of $10{\sim}15\;frame/sec$, while its fixed point implementation on Pentium Core2Duo (2.13 GHz) and ARM9 (400 MHz) takes 10 seconds and one hour each to process a frame.

• Journal of Korea Multimedia Society
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• v.16 no.9
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• pp.1057-1066
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• 2013

Personal verification using finger-knuckle-print(FKP) uses lines and creases at the finger-knuckle area, so the orientation information of texture is an important feature. In this paper, we propose an effective FKP verification method which extracts keypoints using SIFT algorithm and matches the keypoints by vector similarity. The vector is defined as a direction vector which connects a keypoint extracted from a query image and a corresponding keypoint extracted from a reference image. Since the direction vector is created by a pair of local keypoints, the direction vector itself represents only a local feature. However, it has an advantage of expanding a local feature to a global feature by comparing the vector similarity among vectors in two images. The experimental results show that the proposed method is superior to the previous methods based on orientation codes.