• KIPS Transactions on Computer and Communication Systems
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• v.3 no.5
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• pp.163-172
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• 2014

The Scale Invariant Feature Transform (SIFT) has been widely used in a lot of applications for image feature matching. Such a transform allows us to strong matching ability, stability in rotation, and scaling with the variety of different scales. Recently, it has been made one of the most successful algorithms in the research areas of copy-move forgery detections. Though this transform is capable of identifying copy-move forgery, it does not widely address the possibility that counter-forensics operations may be designed and used to hide the evidence of image tampering. In this paper, we propose a targeted counter-forensics method for impeding SIFT-based copy-move forgery detection by applying a semantically admissible distortion in the processing tool. The proposed method allows the attacker to delude a similarity matching process and conceal the traces left by a modification of SIFT keypoints, while maintaining a high fidelity between the processed images and original ones under the semantic constraints. The efficiency of the proposed method is supported by several experiments on the test images with various parameter settings.

• Proceedings of the Korean Information Science Society Conference
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• 2010.06c
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• pp.525-529
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• 2010

최근 많은 컴퓨팅 작업들이 모바일로 옮겨지기 시작하면서 존재하는 알고리즘을 하드웨어에 구현하여 속도를 높이는 것이 이슈가 되고 있다. 그 중 영상의 특징 점을 기반으로 두 개 이상의 영상을 매칭하는 기술을 중심으로 하는 기술이 다양한 분야에서 연구되고 있다. 본 논문에서는 다양한 분야에서 널리 활용되는 Scale Invariant Feature Transform(SIFT)라는 특징 점 추출 알고리즘의 성능을 분석하여 모바일 디바이스를 위한 비용대비 성능이 높은 최적의 매개변수를 찾는다.

• 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 the Institute of Electronics and Information Engineers
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• v.50 no.8
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• pp.187-195
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• 2013

Most of former studies for car license plate detection restrict the image acquisition environment. The aim of this research is to diminish the restrictions by proposing a new method of using SIFT and neural network. SIFT can be used in diverse situations with less restriction because it provides size- and rotation-invariance and large discriminating power. SIFT extracted from the license plate image is divided into the internal(inside class) and the external(outside class) ones and the classifier is trained using them. In the proposed method, by just putting the various types of license plates, the trained neural network classifier can process all of the types. Although the classification performance is not high, the inside class appears densely over the plate region and sparsely over the non-plate regions. These characteristics create a local feature map, from which we can identify the location with the global maximum value as a candidate of license plate region. We collected image database with much less restriction than the conventional researches. The experiment and evaluation were done using this database. In terms of classification accuracy of SIFT keypoints, the correct recognition rate was 97.1%. The precision rate was 62.0% and recall rate was 50.2%. In terms of license plate detection rate, the correct recognition rate was 98.6%.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.1
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• pp.116-122
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• 2010

This paper proposes a method for classifying targets robust to geometric transformations of targets such as rotation, scale change, translation, and pose change. Targets which have rotation, scale change, and shift is firstly classified based on CM(Confidence Map) which is generated by similarity, scale ratio, and range of orientation for SIFT(Scale-Invariant Feature Transform) feature vectors. On the other hand, DB(DataBase) which is acquired in various angles is used to deal with pose variation of targets. Range of the angle is determined by comparing and analyzing the execution time and performance for sampling intervals. We experiment on various images which is geometrically changed to evaluate performance of proposed target classification method. Experimental results show that the proposed algorithm has a good classification performance.

• Journal of IKEEE
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• v.19 no.4
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• pp.460-464
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• 2015

This paper proposes a panoramic image stitching that operates in real time at the embedded environment by applying ROI and PROSAC algorithm. The conventional panoramic image stitching applies SURF or SIFT algorithm which contains complicated operations and a lots of data, at the overall image to detect feature points. Also it applies RANSAC algorithm to remove outliers, so that an additional verification time is required due to its randomness. In this paper, unnecessary data are eliminated by setting ROI based on the characteristics of panorama images, and PROSAC algorithm is applied for removing outliers to reduce verification time. The proposed method was implemented on the ORDROID-XU board with ARM Cortex-A15. The result shows an improvement of about 54% in the processing time compared to the conventional method.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.7
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• pp.695-702
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• 2010

This paper presents an image alignment algorithm for application of AOI (Automatic Optical Inspection) based on SIFT. Since the correspondences result using SIFT descriptor have many wrong points for aligning, this paper modified and classified those points by five measures called the CCFMR (Cascade Classifier for False Matching Reduction) After reduced the false matching, rotation and translation are estimated by point selection method. Experimental results show that the proposed method has fewer fail matching in comparison to commercial software MIL 8.0, and specially, less than twice with the well-controlled environment’s data sets (such as AOI system). The rotation and translation accuracy is robust than MIL in the noise data sets, but the errors are higher than in a rotation variation data sets although that also meaningful result in the practical system. In addition to, the computational time consumed by the proposed method is four times shorter than that by MIL which increases linearly according to noise.

• Journal of Korea Society of Industrial Information Systems
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• v.13 no.2
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• pp.69-79
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• 2008

Due to popularization of the wireless Internet and mobile devices the infrastructure of the ubiquitous environment, where users can get information whatever they want anytime and anywhere, is created. Therefore, a variety of fields including the education studies methods for efficiency of information transmission using on-line and off-line contents. In this paper, we propose the Mixed Mobile Education system(MME) that improves educational efficiency using on-line and off-line contents on mobile devices. Because it is hard to input new data and cannot use similar off-line contents in systems used additional tags, the proposed system does not use additional tags but recognizes of-line contents as we extract feature points in the input image using the mobile camera. We use the Scale Invariant Feature Transform(SIFT) algorithm to extract feature points which are not affected by noise, color distortion, size and rotation in the input image captured by the low resolution camera. And we use the client-server architecture for solving the limited storage size of the mobile devices and for easily registration and modification of data. Experimental results show that compared with previous work, the proposed system has some advantages and disadvantages and that the proposed system has good efficiency on various environments.

• Journal of IKEEE
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• v.20 no.4
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• pp.420-423
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• 2016

In this paper, we proposed an implementation of panoramic image stitching that operates in real time at the embedded environment by applying ROI based PROSAC algorithm using the directionality of the image. The conventional panoramic image stitching applies SURF or SIFT algorithm which contains unnecessary computation and a lots of data to detect feature points. In this paper, we use the direction of the input image and we proposed the method of reducing the unnecessary computation by using ROI. We use a gyro sensor and an acceleration sensor. Output data from gyro and acceleration sensors can be calibrated by complementary filter. The calibration does not affect the operating time of the proposed image stitching algorithm in embedded environment. Therefore, it is possible to operate in real-time.

• Journal of Internet Computing and Services
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• v.20 no.4
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• pp.55-61
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• 2019

In this paper, if images are acquired in all-time situations through various sensors (EO/IR, SAR, GMTI, LiDAR) used for defense purposes, the images can be analyzed and expressed in augmented reality(AR). Various algorithms are used to process images with augmented reality, and depending on the situation, it is necessary to decide which algorithms to select and use. Through the performance comparison (error rate, processing time, accuracy) of SIFT, SURF, ORB, and BRISK, the representative augmented reality algorithm, it is analyzed and proposed which augmented reality algorithm is effective to use under various situations in the defense field.