• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.12
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• pp.4934-4949
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• 2015

The unstable characteristic values of communication signals along with the varying SNR (Signal Noise Ratio) environment make it difficult to identify the modulations of signals. Most of relevant literature revolves around signal recognition under stable SNR, and not applicable for signal recognition at varying SNR. To solve the problem, this research developed a novel communication signal recognition algorithm based on Holder coefficient and cloud theory. In this algorithm, the two-dimensional (2D) Holder coefficient characteristics of communication signals were firstly calculated, and then according to the distribution characteristics of Holder coefficient under varying SNR environment, the digital characteristics of cloud model such as expectation, entropy, and hyper entropy are calculated to constitute the three-dimensional (3D) digital cloud characteristics of Holder coefficient value, which aims to improve the recognition rate of the communication signals. Compared with traditional algorithms, the developed algorithm can describe the signals' features more accurately under varying SNR environment. The results from the numerical simulation show that the developed 3D feature extraction algorithm based on Holder coefficient cloud features performs better anti-noise ability, and the classifier based on interval gray relation theory can achieve a recognition rate up to 84.0%, even when the SNR varies from -17dB to -12dB.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.3
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• pp.1189-1204
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• 2018

Detecting and capturing 3D human structures from the intensity-based image sequences is an inherently arguable problem, which attracted attention of several researchers especially in real-time activity recognition (Real-AR). These Real-AR systems have been significantly enhanced by using depth intensity sensors that gives maximum information, in spite of the fact that conventional Real-AR systems are using RGB video sensors. This study proposed a depth-based routine-logging Real-AR system to identify the daily human activity routines and to make these surroundings an intelligent living space. Our real-time routine-logging Real-AR system is categorized into two categories. The data collection with the use of a depth camera, feature extraction based on joint information and training/recognition of each activity. In-addition, the recognition mechanism locates, and pinpoints the learned activities and induces routine-logs. The evaluation applied on the depth datasets (self-annotated and MSRAction3D datasets) demonstrated that proposed system can achieve better recognition rates and robust as compare to state-of-the-art methods. Our Real-AR should be feasibly accessible and permanently used in behavior monitoring applications, humanoid-robot systems and e-medical therapy systems.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.379-381
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• 2013

In this paper, we propose a method to detect the gradient of the spaces between sentences and to deskew in the document image. First, gradient is measured by pixels for spaces between sentences that has been done an edge extraction in document image and then skewed image is corrected by using the value of the gradient which has been measured. Since document image is divided into several areas, it shows a robust processing result by handling the margin, images, and multistage form in the document. Because the proposed method does not use pixel of the character region but use the blank area, degraded document image as well as vivid document image is effectively corrected than conventional method.

• The KIPS Transactions:PartB
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• v.12B no.3 s.99
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• pp.283-290
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• 2005

The mismatched allocation of watermarking position due to parallel translation, rotation, and scaling distortion is a problem that requires an answer in watermarking. In this paper, we propose a watermarking method robust enough to hold against geometrical distorting using restoration pattern matching. The proposed method defines restoration pattern, then inserts the pattern to a watermark embedded image for distribution. Geometrical distortion is verified by comparing restoration pattern extracted from distributed image and the original restoration pattern inserted to the image. If geometrical distortion is found, inverse transformation is equally performed to synchronize the watermark insertion and extraction position. To evaluate the performance of the proposed method, experiments in translation, rotation, and scaling attack are performed.

• Journal of Information Technology Services
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• v.6 no.3
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• pp.163-177
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• 2007

This research is the concept of a game addiction absorbed in the game based on the review and analysis of factors that affect the characteristics of game addiction, it is appropriate to extract the purpose of factors. Game addiction factor is composed of a total of 32 questions, and a total of 356 people, to collect data through surveys. Factor analysis of the collected data to the target as a result of physical and mental problems, loss of control, tolerance, and avoidance of real world consists of three sub-factors. Factors that affect flow of tolerance and loss of control, and avoid the real world, including two sub-factors that could determine. Diagnostic game addiction factor in the reliability coefficients (Cronbach alpha) is a robust .966 aspects in the event. The game addiction scale score of 90-game addiction by category 'regular user', 90 points and 114 between the terms 'potentially dangerous user' and 13 percent of the overall. Finally, more than 115 points in the 'high-risk user' has been classified as 5% of the overall distribution of the notice that. Such factors extract game is a game addict, addicted users of the game and tend to properly evaluate and navigate game addiction-related problems early in the game addiction and found it could be used properly.

• Journal of the Institute of Convergence Signal Processing
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• v.12 no.4
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• pp.237-241
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• 2011

Stable operation of an industry crane becomes more important as current industry facilities become larger and operate at higher speeds. This paper proposes implementing a system for monitoring moving objects within safety lines of an industry crane by camera. The cost of implementing such a system is low, since it requires only a webcam and notebook computer. The detection algorithm of moving objects uses the feature extraction method by image differential histograms. The proposed system is robust to variations in the weather and environment. The area of the inside safety lines is considered and shadow removal algorithm is used for good performance of the system. The system is valuable for practical applications in the industry.

• Transactions of the Korean Society of Automotive Engineers
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• v.16 no.6
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• pp.134-140
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• 2008

Preceding vehicle detection is a crucial issue for driver assistance system as well as for autonomous vehicle guidance function and it has to be performed with high reliability to avoid any potential collision. The vision-based preceded vehicle detection systems are regarded promising for this purpose because they require little infrastructure on a highway. However, the feasibility of these systems in passenger car requires accurate and robust sensing performance. In this paper, an preceded vehicle detection system is developed using stereo vision sensors. This system utilizes feature matching, epipoplar constraint and feature aggregation in order to robustly detect the initial corresponding pairs. After the initial detection, the system executes the tracking algorithm for the preceded vehicles including a leading vehicle. Then, the position parameters of the preceded vehicles or leading vehicles can be obtained. The proposed preceded vehicle detection system is implemented on a passenger car and its performances is verified experimentally.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.5161-5178
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• 2019

Correlation Filter was recently demonstrated to have good characteristics in the field of video object tracking. The advantages of Correlation Filter based trackers are reflected in the high accuracy and robustness it provides while maintaining a high speed. However, there are still some necessary improvements that should be made. First, most trackers cannot handle multi-scale problems. To solve this problem, our algorithm combines position estimation with scale estimation. The difference from the traditional method in regard to the scale estimation is that, the proposed method can track the scale of the object more quickly and effective. Additionally, in the feature extraction module, the feature representation of traditional algorithms is relatively simple, and furthermore, the tracking performance is easily affected in complex scenarios. In this paper, we design a novel and powerful feature that can significantly improve the tracking performance. Finally, traditional trackers often suffer from model drift, which is caused by occlusion and other complex scenarios. We introduce a relocation component to detect object at other locations such as the secondary peak of the response map. It partly alleviates the model drift problem.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.2
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• pp.143-150
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• 2010

Markerless augmented reality system must have the capability to recognize and match natural objects both in indoor and outdoor environment. In this paper, a novel approach is proposed for extracting features and recognizing natural objects using visual descriptors and codebooks. Since the augmented reality applications are sensitive to speed of operation and real time performance, our work mainly focused on recognition of multi-class natural objects and reduce the computing time for classification and feature extraction. SIFT(scale invariant feature transforms) and SURF(speeded up robust feature) are used to extract features from natural objects during training and testing, and their performance is compared. Then we form visual codebook from the high dimensional feature vectors using clustering algorithm and recognize the objects using naive Bayes classifier.

• Journal of Korean Society for Geospatial Information Science
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• v.17 no.3
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• pp.115-120
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• 2009

Point-based methods with experienced human operators are processed well in traditional photogrammetric activities but not the autonomous environment of digital photogrammetry. To develop more robust and accurate techniques, higher level objects of straight linear features accommodating element other than points are adopted instead of points in aerial triangulation. Even though recent advanced algorithms provide accurate and reliable linear feature extraction, extracting linear features is more difficult than extracting a discrete set of points which can consist of any form of curves. Control points which are the initial input data and break points which are end points of piecewise curves are easily obtained with manual digitizing, edge operators or interest operators. Employing high level features increase the feasibility of geometric information and provide the analytical and suitable solution for the advanced computer technology.