• Journal of Korea Multimedia Society
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• v.17 no.8
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• pp.915-922
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• 2014

We propose an adaptive zoom motion estimation method where a picture is divided into two areas based on the distance information with a depth camera : the one is object area and the other is background area. In the proposed method, the zoom motion is only applied to the object area except the background area. Further, the block size of motion estimation for the object area is set to smaller than that of background area. This adaptive zoom motion estimation method can be reduced at the complexity of motion estimation and can be improved at the motion estimation performance by reducing the block size of the object area in comparison with the conventional zoom motion estimation method. Based on the simulation results, the proposed method is compared with the conventional methods in terms of motion estimation accuracy and computational complexity.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.05a
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• pp.202-207
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• 2003

The purpose of this study was to investigate the effect of background noise on loudness magnitude estimation of Heavy-weight impact noise. Relationship between loudness magnitude estimation and estimation methods about floor impact noise had appeared low in apartment which receive much effect of background noise. Then, to need new estimation method abut effect of background noise. Masking effects by background noise is increased steadily, there is a continuous transition between an audible impact noise and one that is totally masked. Result 1 hat analyze interrelationship of phychoacoustical data and values through Zwicker Parameters, to Investigate that an estimation experiment about Annoyance need.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.8
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• pp.1423-1443
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• 2011

Background compensation plays an important role in detecting and isolating object motion in visual tracking. Here, we propose a Genetic Hough Transform, which combines the Hough Transform and Genetic Algorithm, as a method for eliminating background motion. Our method can handle cases in which the background may contain only a few, if any, feature points. These points can be used to estimate the motion between two successive frames. In addition to dealing with featureless backgrounds, our method can successfully handle motion blur. Experimental comparisons of the results obtained using the proposed method with other methods show that the proposed approach yields a satisfactory estimate of background motion.

• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06a
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• pp.740-745
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• 1994

In the actual situation of measuring the environmental noise, it is very often that only the resultant phenomenon fluctuation contaminated by the additional noise of arbitrary distribution type can be observed. Furthermore, the observed data is usually given in a sound level form the purpose of estimating only the undisturbed objective output response, some estimation method is necessary to reasonably remove the effect of the above additional noise. In this paper, first, a mathematical model of arbitrary sound insulation systems is introduced in the form of a linear system on intensity scale, by using the well-known additive property of energy quantities. Next, some estimation method of the output response under the existence of background noise is derived. Then, based on the expression of the above estimation method, a new prediction method of only the output response probability function form for arbitrary sound insulation systems without. a background noise is proposed by use of observed data contaminated by a background noise. Finally, the effectiveness of the proposed method is confirmed experimentally too by applying it to the actual various type sound wall systems.

• The Journal of Information Technology
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• v.10 no.4
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• pp.27-39
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• 2007

Object tracking in a real time environment is one of challenging subjects in computer vision area during past couple of years. This paper proposes a method of object detection and tracking using adaptive background estimation in real time environment. To obtain a stable and adaptive background, we combine 3-frame differential method and running average single gaussian background model. Using this background model, we can successfully detect moving objects while minimizing false moving objects caused by noise. In the tracking phase, we propose a matching criteria where the weight of position and inner brightness distribution can be controlled by the size of objects. Also, we adopt a Kalman Filter to overcome the occlusion of tracked objects. By experiments, we can successfully detect and track objects in real time environment.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.9
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• pp.164-171
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• 2013

XRF spectrum data of a material include a lot of background signals which are not related to its components. Since an XRF analyzer analyzes components and concentrations of an analyte using the locations and magnitudes of gaussian-shaped peaks extracted from a spectrum, its background signals need to be removed completely from the spectrum for the accurate analysis. Morphology-based method, SNIP-based method and thresholding-based method have been used to remove background signals. In the paper, a background removal method, an improved version of an interval-thresholding-based method, is proposed. The proposed method consists of interval partitioning, interval classifying, and background estimation. Experimental results show that the proposed method has better performance on background removal from the spectrum than the existing methods, morphology-based method and SNIP-based method.

• Smart Media Journal
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• v.2 no.2
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• pp.33-38
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• 2013

In this paper, we investigated baseline estimation methods for remove background noise using Raman spectra from acute alcohol liver injury and acute ethanol-induced chronic liver fibrosis. Far the baseline estimation, we applied first derivative, linear programming and rolling ball method. Optimal input parameter of each method were determined by the training rate of MAP (maximum a posteriori probability) classifier. According to the experimental results, classification results baseline estimation with the rolling ball algorithm gave about 89.4%, which is very promising results for classification of acute alcohol liver injury and acute ethanol-induced chronic liver fibrosis. From these results, to determined the appropriate methods and parameters of baseline estimation impact on classification performance was confirmed.

• Journal of Korea Multimedia Society
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• v.12 no.12
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• pp.1728-1735
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• 2009

Human action tracking plays an important place in human-computer-interaction, human action tracking is a challenging task because of the exponentially increased computational complexity in terms of the degrees of freedom of the object and the severe image ambiguities incurred by frequent self-occlusions. In this paper, we will propose a novel method to track human action, in our technique, a dynamic background estimation algorithm will be applied firstly. Based on the estimated background, we then extract the human object from the video sequence, and the skeletonization method and Hough transform method will be used to detect the main structure of human body and each part rotation angle. The calculated rotation angles will be used to control a crane in the port, thus we can just control the container crane by using signalman body. And the experimental results can show that our proposed method can get a preferable result than the conventional methods such as: MIT, JPF or MFMC.

• 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 Broadcast Engineering
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• v.19 no.6
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• pp.916-924
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• 2014

It is possible to improve the accuracy of the motion estimation for a video by applying a variable block size. However, it has limits in the zoom motion estimation. In this paper, we propose a method for estimating the zoom motion with variable block size. The proposed method separates the background within the object picture by depth information obtained from a depth camera, and only the object regions are applied to zoom scale, but the background is not applied. In addition, the object regions select efficiently variable block size mode in consideration of the generated motion vectors and the accuracy of motion estimation at the same time. Simulation results show the accuracy of the motion estimation and the number of motion vectors for the proposed method. It is verified that the proposed method can reduce the number of motion while maintaining the similar accuracy of motion estimation than the conventional motion estimation methods.