• The KIPS Transactions:PartC
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• v.14C no.3 s.113
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• pp.209-220
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• 2007

IPS(Intrusion Prevention Systems), which is installed in inline mode in a network, protects network from outside attacks by inspecting the incoming/outgoing packets and sessions, and dropping the packet or closing the sessions if an attack is detected in the packet. In the signature based filtering, the payload of a packet passing through IPS is matched with some attack patterns called signatures and dropped if matched. As the number of signatures increases, the time required for the pattern matching for a packet increases accordingly so that it becomes difficult to develop a high performance US working without packet delay. In this paper, we propose a high performance IPS based on signature hashing to make the pattern matching time independent of the number of signatures. We implemented the proposed scheme in a Linux kernel module in a PC and tested it using worm generator, packet generator and network performance measure instrument called smart bit. Experimental results show that the performance of existing method is degraded as the number of signatures increases whereas the performance of the proposed scheme is not degraded.

• The KIPS Transactions:PartB
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• v.17B no.5
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• pp.345-354
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• 2010

Visual tracking techniques for Augmented Reality are classified as either a marker tracking approach or a natural feature tracking approach. Marker-based tracking algorithms can be efficiently implemented sufficient to work in real-time on mobile devices. On the other hand, natural feature tracking methods require a lot of computationally expensive procedures. Most previous natural feature tracking methods include heavy feature extraction and pattern matching procedures for each of the input image frame. It is difficult to implement real-time augmented reality applications including the capability of natural feature tracking on low performance devices. The required computational time cost is also in proportion to the number of patterns to be matched. To speed up the natural feature tracking process, we propose a novel fast tracking method based on optical flow. We implemented the proposed method on mobile devices to run in real-time and be appropriately used with mobile augmented reality applications. Moreover, during tracking, we keep up the total number of feature points by inserting new feature points proportional to the number of vanished feature points. Experimental results showed that the proposed method reduces the computational cost and also stabilizes the camera pose estimation results.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.1
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• pp.58-63
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• 2020

This paper proposes the method for improving the localization accuracy of the mobile robot based on the stereo camera. To restore the position information from stereo images obtained by the stereo camera, the corresponding point which corresponds to one pixel on the left image should be found on the right image. For this, there is the general method to search for corresponding point by calculating the similarity of pixel with pixels on the epipolar line. However, there are some disadvantages because all pixels on the epipolar line should be calculated and the similarity is calculated by only pixel value like RGB color space. To make up for this weak point, this paper implements the method to search for the corresponding point simply by calculating the gap of x-coordinate when the feature points, which are extracted by feature extraction and matched by feature matching method, are a pair and located on the same y-coordinate on the left/right image. In addition, the proposed method tries to preserve the number of feature points as much as possible by finding the corresponding points through the conventional algorithm in case of unmatched features. Because the number of the feature points has effect on the accuracy of the localization. The position of the mobile robot is compensated based on 3-D coordinates of the features which are restored by the feature points and corresponding points. As experimental results, by the proposed method, the number of the feature points are increased for compensating the position and the position of the mobile robot can be compensated more than only feature extraction.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.50-53
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• 2022

Recently, there have been cases in which counterfeit foreign ships have entered and left domestic ports several times. Vessels have a ship-specific serial number given by the International Maritime Organization (IMO) to identify the vessel, and IMO marking is mandatory on all ships built since 2004. In the case of airports and ports, which are representative logistics platforms, a security system is essential, but it is difficult to establish a security system at a port and there are many blind spots, which can cause security problems due to insufficient security systems. In this paper, a port security system is designed using deep learning object recognition and OpenCV. The security system process extracts the IMO number of the ship after recognizing the object when entering the ship, determines whether it is the same ship through feature point matching for ships with entry records, and stores the ship image and IMO number in the entry/exit DB for the first arrival vessel. Through the system of this paper, port security can be strengthened by improving the efficiency and system of port logistics by increasing the efficiency of port management personnel and reducing incidental costs caused by unauthorized entry.

• The Transactions of the Korea Information Processing Society
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• v.5 no.4
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• pp.1048-1056
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• 1998

In this paper, a fractal image coding based on discrete wavelet transform is proposed to improve PSNR at low bit rates and reduce computational complexity of encoding process. The proposed method takes the absolute value of discrete wavelet transform coefficients, and then constructs significant coefficients trees, which indicate the positions and signs of the significant coefficients. This method improves PSNR and reduces computational complexity of mapping contracted domain pool onto range block, by matching only the significant coefficients of range block to coefficients of contracted domain block. Also, this paper proposes a classification scheme which minimizes the number of contracted domain blocks compared with range block. This scheme significantly reduces the number of range and contracted domain block comparison.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.7
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• pp.1831-1853
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• 2012

By sending periodically short bursts of traffic to reduce legit transmission control protocol (TCP) traffic, the low-rate denial of service (LDoS) attacks are hard to be detected and may endanger covertly a network for a long period. Traditionally, LDoS detecting methods mainly concentrate on the attack stream with feature matching, and only a limited number of attack patterns can be detected off-line with high cost. Recent researches divert focus from the attack stream to the traffic anomalies induced by LDoS attacks, which can detect more kinds of attacks with higher efficiency. However, the limited number of abnormal characteristics and the inadequacy of judgment rules may cause wrong decision in some particular situations. In this paper, we address the problem of detecting LDoS attacks and present a scheme based on the fluctuant features of legit TCP and acknowledgment (ACK) traffic. In the scheme, we define judgment criteria which used to identify LDoS attacks in real time at an optimal detection cost. We evaluate the performance of our strategy in real-world network topologies. Simulations results clearly demonstrate the superiority of the method proposed in detecting LDoS attacks.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.4
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• pp.229-236
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• 2017

This paper presents an efficient object-based color image-retrieval algorithm that is suitable for the classification and retrieval of images from small to mid-scale datasets, such as images in PCs, tablets, phones, and cameras. The proposed method first finds salient regions by using regional feature vectors, and also finds several dominant colors in each region. Then, each salient region is partitioned into small sub-blocks, which are assigned 1 or 0 with respect to the number of pixels corresponding to a dominant color in the sub-block. This gives a binary map for the dominant color, and this process is repeated for the predefined number of dominant colors. Finally, we have several binary maps, each of which corresponds to a dominant color in a salient region. Hence, the binary maps represent the spatial distribution of the dominant colors in the salient region, and the union (OR operation) of the maps can describe the approximate shapes of salient objects. Also proposed in this paper is a matching method that uses these binary maps and which needs very few computations, because most operations are binary. Experiments on widely used color image databases show that the proposed method performs better than state-of-the-art and previous color-based methods.

• Journal of Broadcast Engineering
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• v.20 no.6
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• pp.912-920
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• 2015

Unlike general texture-based feature description algorithms, Locally Likely Arrangement Hashing (LLAH) algorithm describes a feature based on the geometric relationship between its neighbors. Thus, even in poor-textured scenes or large camera pose changes, it can successfully describe and track features and enables to implement augmented reality. This paper aims to optimize the performance of LLAH algorithm for tracking random dots (= features) with Gaussian noise. For this purpose, images with different number of features and magnitude of Gaussian noise are prepared. Then, the performance of LLAH algorithm according to the conditions: the number of neighbors, the type of geometric invariants, and the distance between features, is analyzed, and the optimal conditions are determined. With the optimal conditions, each feature could be matched and tracked in real-time with a matching rate of more than 80%.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.50 no.6
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• pp.806-811
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• 2017

A multiplex PCR system comprising 14 microsatellite markers was developed for genotyping analysis of the sea cucumber Stichopus japonicus. A total of 286 samples were used to evaluate genetic polymorphisms and forensic parameters of the microsatellite loci. In a single PCR reaction, all 14 loci were uniformly amplified and a total of 269 alleles were identified. The AJ19024 locus had the largest number of alleles (46), and its discriminatory power and exclusion power were 0.99 and 0.76, respectively. The fewest alleles (8) were present at the Psj2575 locus, which provided the lowest discriminatory power (0.81) and exclusion power (0.20). The mean number of alleles, mean heterozygosity, mean discrimination power and mean exclusion power per locus were 19.21, 0.70, 0.93, and 0.46, respectively. The combined matching probability for the 14 loci was $9.64{\times}10^{-19}$, and the combined power of exclusion was 0.999995. Thus, the forensic parameters evaluated in the present study demonstrated the utility of our multiplex PCR system for biological tracing methods, such as individual identification and paternity testing, in the sea cucumber.

• Journal of Sensor Science and Technology
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• v.15 no.1
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• pp.20-27
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• 2006

This paper presents a gas measurement system for deciding hole positions on a PU middle-sole mold from computed gas amount. The optimal number of holes and their positions on the shoe mold are decided from statistical experiment results to overcome the problem of excessive expenses in gas vent exchange. This paper also describes a gas vent exchange mechanism using computer vision system. The gas hole detecting process is based on computer vision algorithms represented as a simple Pattern Matching. The experimental result showed us that the system was useful to calculate the number of holes and their positions on the shoes mold.