• Journal of Web Engineering
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• v.21 no.4
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• pp.1265-1282
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• 2022

The theme, background, and signal music usage history information consists of general information such as music information, platform information, and music usage information, and sensitive information such as rights management information, music usage permission range, and contract information. If sensitive information among these is disclosed, disputes such as trade secrets and infringement of personal information protection between companies or between companies and individuals may arise. We propose a selective encryption/decryption method to secure the confidentiality, integrity, reliability and non-repudiation of sensitive music usage history information used in the theme, background, and signal music blockchain environment. In the proposed method, a monitoring company encrypts sensitive information using a secret key for usage history information, which is combined with general information, and digitally signs it using a private key to register it in a block. A trust group can view and access the information at the time of inquiry by verifying the digital signature with the public key of the monitoring company and then can decrypt the sensitive information using the private key.

• Journal of information and communication convergence engineering
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• v.12 no.3
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• pp.193-197
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• 2014

Edges are a robust feature for object detection. In this paper, we present an edge-based background modeling method for the detection of moving objects. The edges in the image frames were mapped using robust Canny edge detector. Two edge maps were created and combined to calculate the ultimate moving-edge map. By selecting all the edge pixels of the current frame above the defined threshold of the ultimate moving edges, a temporary background-edge map was created. If the frequencies of the temporary background edge pixels for several frames were above the threshold, then those edge pixels were treated as background edge pixels. We conducted a performance comparison with previous works. The existing edge-based moving-object detection algorithms pose some difficulty due to the changes in background motion, object shape, illumination variation, and noises. The result of the performance evaluation shows that the proposed algorithm can detect moving objects efficiently in real-world scenarios.

• The KIPS Transactions:PartB
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• v.17B no.1
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• pp.15-22
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• 2010

Background generation is very important for accurate object tracking in video surveillance systems. Traditional background generation techniques have some problems with non-moving objects for longer periods. To overcome this problem, we propose a newbackground generation method using mean-shift and Fast Marching Method (FMM) to use pixel information along temporal and spatial dimensions. The mode of pixel value density along time axis is estimated by mean-shift algorithm and spatial information is evaluated by FMM, and then they are used together to generate a desirable background in the existence of non-moving objects during longer period. Experimental results show that our proposed method is more efficient than the traditional method.

• Journal of applied mathematics & informatics
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• v.15 no.1_2
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• pp.511-525
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• 2004

we model communication and computer systems that process interactive and several and several types of background jobs. The scheduling policy in use is to share the processor among all interactive jobs and, at most, one background job of each type at a time according to the process sharing discipline. Background jobs of each type are served on a first-come-first-served basis. Such scheduling policy is called Processor Sharing with Background jobs (PSBJ). In fact, the PSBJ policy is commonly used on many communication and computer systems that allow interactive usage of the systems and process certain jobs in a background mode. In this paper, the stability conditions for the PSBJ policy are given and proved. Since an exact analysis of the policy seems to be very difficult, an approximate analytic model is proposed to obtain the average job sojourn times. The model requires the solution of a set of nonlinear equations, for which an iterative algorithm is given and its convergence is proved. Our results reveal that the model provides excellent estimates of average sojourn times for both interactive and background jobs with a few percent of errors in most of the cases considered.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.2 no.3
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• pp.31-38
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• 2009

A process of identifying moving objects from data is typical task in many computer vision applications. In this paper, we propose a motion segmentation method that generally consists from background subtraction and foreground pixel segmentation. The Approximated Median Filter (AMF) was chosen to perform background modelling. To demonstrate the effectiveness of proposed approach, we tested it gray-scale video data as well as RGB color space.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.10
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• pp.2419-2424
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• 2013

In a conventional MOG algorithm, a small threshold for background decision causes the background recognition delay in a periodic background and a large threshold makes it recognize passing objects as background in a stationary background. This paper proposes the improved MOG algorithm using adaptive threshold. The proposed algorithm estimates changes of weight in the dominant model of the MOG algorithm both in the short and long terms, classifies backgrounds into the stationary and periodic ones, and assigns proper thresholds to them. The simulation results show that the proposed algorithm decreases the maximum number of frame in background recognition delay from 137 to 4 in the periodic background keeping the equal performance with the conventional algorithm in the stationary background.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.7 no.1
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• pp.293-317
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• 1994

The Purpose of this study is to verify the effect of the searcher's subject background on the result of online database searches. To achieve this purpose, an experimental method was adopted. 180 students performed online searches in the three different libraries chosen for this study. The subjects were classified into two groups according to the scores of the test. Data concerning processes, behavior, and results of the searches performed by the subjects in real situations were gathered. Immediately following the searches, the extent of their subject background were assessed through interview. The search effect consists of the 4 elements： search efficiency (the number of terms used per unit time), the number of relevant documents, the number of relevant documents per unit time, precision ratio. The major findings of this study are summarized as belows. 1. The searchers with strong subject background has significantly higher efficiency in searches made. Group A (of those with strong subject back-ground) use more search terms per unit time than Group B (of those with weak subject background) do. 2. In the searches made by those with strong subject background, more relevant documents art retrieved. 3. In the searches made by those with strong subject background, more relevant documents per unit time are retrieved. 4. The searchers with strong subject background has significantly higher precision ratio in searches made. In the searches made by those with strong subject background, more relevant documents of documents retrieved are retrieved.

• Journal of the Korea Society of Computer and Information
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• v.24 no.5
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• pp.149-156
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• 2019

The existing subtract image based intrusion detection system for CCTV digital images has a problem that it can not distinguish intruders from moving backgrounds that exist in the natural environment. In this paper, we tried to solve the problems of existing system by designing real - time intrusion detection system for CCTV digital image by combining subtract image based intrusion detection method and background learning artificial neural network technology. Our proposed system consists of three steps: subtract image based intrusion detection, background artificial neural network learning stage, and background artificial neural network evaluation stage. The final intrusion detection result is a combination of result of the subtract image based intrusion detection and the final intrusion detection result of the background artificial neural network. The step of subtract image based intrusion detection is a step of determining the occurrence of intrusion by obtaining a difference image between the background cumulative average image and the current frame image. In the background artificial neural network learning, the background is learned in a situation in which no intrusion occurs, and it is learned by dividing into a detection window unit set by the user. In the background artificial neural network evaluation, the learned background artificial neural network is used to produce background recognition or intrusion detection in the detection window unit. The proposed background learning intrusion detection system is able to detect intrusion more precisely than existing subtract image based intrusion detection system and adaptively execute machine learning on the background so that it can be operated as highly practical intrusion detection system.

• Journal of the Korea Society of Computer and Information
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• v.11 no.6 s.44
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• pp.151-156
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• 2006

This paper presents an background edge generation based automatic algorithm for detection of moving objects under moving camera. Background image is generated by rotating the fixed the camera on the tripod horizontally, aligning and reorganizing this images. We develop an efficient approach for robust panoramic background edge generation as well as method of edge matching between input image and background image. We applied the proposed algorithm to real image sequences. The proposed method can be successfully realized in various monitoring systems like intrusion detection as well as video surveillance.

• Journal of the Korea Society of Computer and Information
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• v.20 no.7
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• pp.17-24
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• 2015

In this paper, we propose a method to solve ghosting problem. To generate adaptive background, using an exponentially decreasing number of frames, may improve object detection performance. To extract moving objects from the background by using a differential image, detection error may be caused by object rotations or environmental changes. A ghosting problem can be issue-driven when there are outdoor environmental changes and moving objects. We studied that a differential image by adaptive background may reduce the ghosting problem. In experimental results, we test that our method can solve the ghosting problem.