• Journal of Korean Elementary Science Education
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• v.28 no.1
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• pp.24-34
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• 2009

The purpose of this study is to examine the effects of background knowledge on problem solving. To achieve this aim, I proposed the model which shows problem solving process centering around background knowledge, conducted the lessons concerning the concept 'weightlessness' on pre-service elementary teachers, and then classified the pre-service elementary teachers into several groups by the difference of the results presented in the process of solving the problems on weightlessness. And I examined qualitatively the effects of background knowledge on problem solving through the interview with 11 volunteers. On the cause of the failing the problem solving, the failure of acquiring or activating the background knowledge related to the learning concept was most frequently, secondly the use of the background knowledge unrelated to the learning concept, and thirdly the failure of understanding the teaming concept. To acquire or activate the background knowledge related to the teaming concept was more difficult than to understand the new teaming concept, and the cases that use the background knowledge unrelated to the learning concept failed to solve problem. The result of interview, all interviewee understood the learning concept correctly, but all of them who fail to acquire or activate the background knowledge related to the learning concept, or use the background knowledge unrelated to the learning concept, could not solve the problem.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.8
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• pp.67-80
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• 2017

This paper presents a hole-filling method using extrapolated spatio-temporal background information to obtain a synthesized view. A new temporal background model using non-overlapped patch based background codebook is introduced to extrapolate temporal background information In addition, a depth-map driven spatial local background estimation is addressed to define spatial background constraints that represent the lower and upper bounds of a background candidate. Background holes are filled by comparing the similarities between the temporal background information and the spatial background constraints. Additionally, a depth map-based ghost removal filter is described to solve the problem of the non-fit between a color image and the corresponding depth map of a virtual view after 3-D warping. Finally, an inpainting is applied to fill in the remaining holes with the priority function that includes a new depth term. The experimental results demonstrated that the proposed method led to results that promised subjective and objective improvement over the state-of-the-art methods.

• 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.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.631-636
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• 2009

Foreground segmentation methods have steadily been researched in the field of computer vision. Especially, background subtraction which extracts a foreground image from the difference between the current frame and a reference image, called as "background image" have been widely used for a variety of real-time applications because of low computation and high-quality. However, if the background scene was dynamically changed, the background subtraction causes lots of errors. In this paper, we propose an efficient background subtraction method in dynamic environment with both static and dynamic scene. The proposed method is a hybrid method that uses the conventional background subtraction for static scene and depth information for dynamic scene. Its validity and efficiency are verified by demonstration in dynamic environment, where a video projector projects various images in the background.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.3
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• pp.269-276
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• 2010

In this paper, we present the detection method of moving objects based on two background models. These background models support to understand multi layered environment belonged in images taken by shaking camera and each model is MBM(Multiple Background Model) and TMBM (Temporal Median Background Model). Because two background models are Pixel-based model, it must have noise by camera movement. Therefore correlation coefficient calculates the similarity between consecutive images and measures camera motion vector which indicates camera movement. For the calculation of correlation coefficient, we choose the selected region and searching area in the current and previous image respectively then we have a displacement vector by the correlation process. Every selected region must have its own displacement vector therefore the global maximum of a histogram of displacement vectors is the camera motion vector between consecutive images. The MBM classifies the intensity distribution of each pixel continuously related by camera motion vector to the multi clusters. However, MBM has weak sensitivity for temporal intensity variation thus we use TMBM to support the weakness of system. In the video-based experiment, we verify the presented algorithm needs around 49(ms) to generate two background models and detect moving objects.

• Journal of the Ergonomics Society of Korea
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• v.27 no.3
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• pp.15-22
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• 2008

The purpose of this study was to define the effects of noise and background music on the trunk muscle fatigue during dynamic lifting and lowering tasks. Six healthy male subjects with no prior history of low back disorders participated in this study. The participants were exposed to two levels of background noise such as 40dB noise and 90dB noise and three levels of background music such as no music, slow music, and fast music. Six different combinations of background noise and background music were played while the participants were performing the lifting task at 15% level of Maximum Voluntary Contraction. Electromyography signals from six muscles were collected and fatigue levels were analyzed quantitatively. In results, the 90dB noise increased trunk muscle fatigue and slowed down the recovery. The trunk muscle fatigue was the lowest when the fast music was played for as background. After recovery, the 90dB noise increased trunk muscle fatigue. The trunk muscle fatigue was the lowest when the slow music was played for as background. The results can be useful to manage the cumulative fatigue of trunk muscles due to background noise and music during repetitive lifting and lowering tasks in industry.

• Journal of Korea Multimedia Society
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• v.11 no.5
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• pp.597-609
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• 2008

Due to the recursive updating nature of background model, previous background modeling methods are often perturbed by crowd scenes where foreground pixels occurs more frequently than background pixels. To resolve this problem, an adaptive background modeling method, which is based on the well-known Gaussian mixture background model, is proposed. In the proposed method, the learning rate of background model is adaptively adjusted with respect to the crowdedness of the scene. Consequently, the learning process is suppressed in crowded scene to maintain proper background model. Experiments on real dataset revealed that the proposed method could perform background subtraction effectively even in crowd situation while the performance is almost the same to the previous method in normal scenes. Also, the F-measure was increased by 5-10% compared to the previous background modeling methods in the video of crowded situations.

• The Korean Journal of Physiology
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• v.26 no.1
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• pp.15-25
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• 1992

In order to elucidate the properties of the background current whole cell patch clamp studies were performed in rabbit ventricular cells. Ramp pulses of ${\pm}80\;mV$ from holding potential of 40 mV(or 20 mV) at the speed of 0.8 V/sec were given every 30 sec(or 10 sec) and current-voltage diagrams(I-V curve) were obtained. For the activation of the background current isoprenaline, adenosine 3',5'-cyclic monophosphate(dBcAMP), guanosine 3',5'-cyclic monophosphate(cGMP), and $N^6$-2'-o-dibutyryladenosine 3',5'-cyclic monophosphate(dBcAMP) were applied after all known current systems were blocked with 2mM Ba, 1 mM Cd ,5 mM Ni, 10 ${\mu}M$ diltiazem, 10 ${\mu}m$ ouabain, and 20 mM tetraethylammonium(TEA). The conductance of background current in control was $0.65{\pm}0.69$ nS at 0 mV, its I-V curves was almost linear and reversed near 50 mV. When there was no taurine in pipette solution, isoprenaline hardly activated the background current but when taurine existed in pipette solution, isoprenaline activated the larger background current. Cyclic AMP or cyclic GMP alone had little effect on the activation of the background current, while cGMP potentiated cGMP effect. When the background current was activated with cGMP and cAMP, isoprenaline could not further increased the background current. The background current activated by isoprenaline depended on extracellular $Cl^-$ concentration and its reversal potential was shifted according to chloride equilibrium potential. The change of extracellular $Na+$ concentration had little effect on reversal potential of the background current activated by isoprenaline.

• Smart Media Journal
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• v.4 no.3
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• pp.50-55
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• 2015

In this paper, we present background modeling method based on Gaussian mixture model to subtract background for night-time video surveillance. In night-time video, it is hard work to distinguish the object from the background because a background pixel is similar to a object pixel. To solve this problem, we change the pixel of input frame to more advantageous value to make the Gaussian mixture model using scaled histogram stretching in preprocessing step. Using scaled pixel value of input frame, we then exploit GMM to find the ideal background pixelwisely. In case that the pixel of next frame is not included in any Gaussian, the matching test in old GMM method ignores the information of stored background by eliminating the Gaussian distribution with low weight. Therefore we consider the stacked data by applying the difference between the old mean and new pixel intensity to new mean instead of removing the Gaussian with low weight. Some experiments demonstrate that the proposed background modeling method shows the superiority of our algorithm effectively.

• 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.