• Proceedings of the IEEK Conference
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• 2000.11d
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• pp.61-64
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• 2000

The purpose of this study is prove the effectiveness of an energy subtraction image for the detection of pulmonary nodules and the effectiveness of multi-resolutional filter on an energy subtraction image to detect pulmonary nodules. Also we examine influential factors to the accuracy of detection of pulmonary nodules from viewpoints of types of images and evaluation methods. As one type of images, we select energy subtraction X-ray images, at the same time is done ▽$^2$G filter and multi-resolutional filter. Here select two evaluation methods and make clear the effectiveness of multi-resolutional filter on an energy subtraction image.

• Journal of Information Processing Systems
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• v.15 no.4
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• pp.748-764
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• 2019

In order to deal with the filtering delay problem of least mean square adaptive filter noise reduction algorithm and music noise problem of spectral subtraction algorithm during the speech signal processing, we combine these two algorithms and propose one novel noise reduction method, showing a strong performance on par or even better than state of the art methods. We first use the least mean square algorithm to reduce the average intensity of noise, and then add spectral subtraction algorithm to reduce remaining noise again. Experiments prove that using the spectral subtraction again after the least mean square adaptive filter algorithm overcomes shortcomings which come from the former two algorithms. Also the novel method increases the signal-to-noise ratio of original speech data and improves the final noise reduction performance.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.6 no.2
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• pp.81-87
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• 2013

Motion segmentation is a fundamental technique for analysing image sequences of real scenes. A process of identifying moving objects from data is a typical task in many computer vision applications. In this paper, we propose motion segmentation that generally consists from background subtraction and foreground pixel segmentation. The Approximated Median Filter (AMF) was chosen to perform background modeling. Motion segmentation in this paper covers RGB video data.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.7 no.1
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• pp.61-67
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• 2014

Motion segmentation is a fundamental technique for analysing image sequences of real scenes. A process of identifying moving objects from data is a typical task in many computer vision applications. In this paper, we propose motion segmentation that generally consists from background subtraction and foreground pixel segmentation. The Approximated Median Filter(AMF) was chosen to perform background modeling. Motion segmentation in this paper covers RGB video data.

• The Journal of Engineering Research
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• v.3 no.1
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• pp.43-46
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• 1998

Considerable research in the last three decades has examined the problem of enhancement of speech degraded by additive background noise. We compare traditional methods such as spectral subtraction and Wiener filter, recently proposed psychoacoustic model based methods such as perceptual filter and noise suppression in EVRC in terms of performance and complexity.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.293-294
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• 2007

The purpose of this study is prove the effectiveness of an energy subtraction image for the detection of pulmonary nodules and the effectiveness of multi-resolutional filter on an energy subtraction image to detect pulmonary nodules. Also we study influential factors to the accuracy of detection of pulmonary nodules from viewpoints of types of images, types of digital filters and types of evaluation methods. As one type of images, we select an energy subtraction image, which removes bones such as ribs from the conventional X-ray image by utilizing the difference of X-ray absorption ratios at different energy between bones and soft tissue. Here we select two evaluation methods and make clear the effectiveness of multi-resolutional filter on an energy subtraction image.

• 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 Korean Society of Safety
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• v.12 no.3
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• pp.60-66
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• 1997

In this paper, a moving object surveillance system, which can extract moving object in real-time, using image subtraction method is described. This technique based on the novelty filter having the structure of neural network associative memory. Digital arithmetic and timing control parts were composed of hardwired controller to treat two-dimensional massive image information. SRAMS having 20 ns access time were used for the image buffer that has high speed write/read property. Image extraction algorithm is discussed and supported by simulation and experiments.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.4
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• pp.555-560
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• 2013

This paper first proposes a classification algorithm which detects a voiced, unvoiced, and silence signal using distance measure, logarithm power and root mean square methods at each frame, then a spectrum subtraction algorithm based on a subband filter. The proposed algorithm subtracts spectrums of white noise and street noise from noisy signal based on the subband filter at each frame. In this experiment, experimental results of the proposed spectrum subtraction algorithm demonstrate using the speech and noise data of Aurora-2 database. Based on measuring the speech-to-noise ratio (SNR), experiments confirm that the proposed algorithm is effective for the speech by contaminated the noise. From the experiments, the improvement in the output SNR values was approximately 2.1 dB and 1.91 dB better for white noise and street noise, respectively.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.10
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• pp.606-610
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• 2003

Noise subtraction using reference channel data has been used to improve signal-to-noise ratio in magnetoencephalography. In this paper, an adaptive noise subtraction model is proposed and parameters for the model are optimized. A criterion to determine an optimal update period for the filter coefficients is proposed based on the ratio of peak amplitude of evoked field (N100m) divided by the output standard deviation. Experiments are carried out using a 40 channel MEG system. From the experiments, the proposed noise subtraction method shows superior performances over existing non-adaptive methods. Two-dimensional topographic map is shown for a diagnosis with a cubic spline interpolation.