• The Journal of the Acoustical Society of Korea
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• v.18 no.5
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• pp.35-43
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• 1999

In this paper, a new robust adaptive algorithm and a post-processing method are proposed to improve the performance of AEC without computational burden. Its step-size is normalized by the sum of the powers of the reference input signal and the desired signal. When the near-end speaker's speech and noise are applied into the microphone, the step-size becomes small and the misalignment of coefficients are reduced. To reduce the residual echoes, a new post-processing method, which is co-operated with the proposed noise-robust adaptive algorithm, is proposed in this paper. The method is based on the correlation of the desired signal and the estimation error signal. The residual echoes are attenuated as proportional to the correlation normalized with the power of desired signals. The normalized correlation plays a role as Wiener filter for residual echoes. In the double-talk situation, the estimation error signals, that are residual echoes, dominantly include the near-end speaker's speech and the normalized correlation closes to 1. Therefore, the near-end speaker's speech can be transmitted without being attenuated. When the desired signals consists of only the acoustic echoes, the residual echoes are mostly attenuated and canceled by the proposed post-processor. The computation of AEC using the proposed post-processor is comparable to NLMS algorithm.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.11a
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• pp.551-554
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• 2005

The method of the reduction of the automotive induction noise can be classified by the method of passive control and the method of active control. However, the passive control method has a demerit to reduce the effect of noise reduction at low frequency (below 500Hz) range and to be limited by a space of the engine room. Whereas, the active control method can overcome the demerit of passive control method. The algorithm of active control is mostly used the LMS (Least-Mean-Square) algorithm because the LMS algorithm can easily obtain the complex transfer function in real-time. Especially, When the Filtered-X LMS (FXLMS) algorithm is applied to an ANC system. However, the convergence performance of LMS algorithm goes bad when the FXLMS algorithm is applied to an active control of the induction noise under rapidly accelerated driving conditions. Thus Normalized FXLMS algorithm was developed to improve the control performance under the rapid acceleration. The advantage of Normalized FXLMS algorithm is that the step size is no longer constant. Instead, it varies with time. But there is one additional practical difficulty that can arise when a nonstationary input is used. If the input is zero for consecutive samples, then the step size becomes unbounded. So, in order to solve this problem. the Correlation FXLMS algorithm was developed. The Correlation FXLMS algorithm is realized by using an estimate of the cross correlation between the adaptation error and the filtered input signal to control the step size. In this paper, the performance of the Correlation FXLMS Is presented in comparison with that of the other FXLMS algorithms based on computer simulations.

• Korean Journal of Remote Sensing
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• v.34 no.6_2
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• pp.1251-1260
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• 2018

High-quality sea-ice surface models generated from aerial images can be used effectively as field data for developing satellite-based remote sensing methods but also as analysis data for understanding geometric variations of Arctic sea-ice. However, the lack of texture information on sea-ice surfaces can reduce the accuracy of image matching. In this paper, we analyze the performance of matching cost functions for homogeneous sea-ice surfaces as a part of high-quality sea-ice surface model generation. The matching cost functions include sum of squared differences (SSD), normalized cross-correlation (NCC), and zero-mean normalized cross-correlation (ZNCC) in image domain and phase correlation (PC), orientation correlation (OC), and gradient correlation (GC) in frequency domain. In order to analyze the matching performance for texture changes clearly and objectively, a new evaluation methodology based on the principle of object-space matching technique was introduced. Experimental results showed that it is possible to secure reliability and accuracy of image matching only when optimal search windows are variably applied to each matching point in textureless regions such as sea-ice surfaces. Among the matching cost functions, NCC and ZNCC showed the best performance for texture changes.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.3
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• pp.287-293
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• 2012

This paper presents an efficient image registration method by measuring the similarity, which is based on multi-dimensional intensity fluctuation. Multi-dimensional intensity which considers 4 directions of the image, is applied to reflect more properties in similarity decision. And an intensity fluctuation is also applied to measure comprehensively the similarity by considering a change in brightness between the adjacent pixels of image. The normalized cross-correlation(NCC) is calculated by considering an intensity fluctuation to each of 4 directions. The 5 correlation coefficients based on the NCC have been used to measure the registration, which are total NCC, the arithmetical mean and a simple product on the correlation coefficient of each direction and on the normalized correlation coefficient by the maximum NCC, respectively. The proposed method has been applied to the problem for registrating the 22 face images of 243*243 pixels and the 9 person images of 500*500 pixels, respectively. The experimental results show that the proposed method has a superior registration performance that appears the image properties well. Especially, the arithmetical mean on the correlation coefficient of each direction is the best registration measure.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.4
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• pp.548-553
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• 2010

This paper presents an effective shape image recognition method using the correlation based on 4-dimensional histogram. The histogram-based correlation is accurately applied to express the similarity by comparing the positions of a corresponding dimension between the images, which is calculated by considering 4 directions of the shape image. The correlation measure by using the normalized cross-correlation is also applied to obtain the robust recognition to the geometrical variations such as shape, position, size, and rotation. The proposed method has been applied to the problem for recognizing the 8 shape images of 64*64 pixels and the 30 shape images of 256*256 pixels. The experimental results show that the proposed method has a superior recognition performance that appears the image characters well.

• Journal of Physiology & Pathology in Korean Medicine
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• v.35 no.6
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• pp.267-273
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• 2021

Mibyeong (sub-health) is a concept that represents the sub-health in traditional East Asian medicine. Assuming that the nose sizes and shapes are related to respiratory function, in this study, we hypothesized that the nose size and shape features are related to the self-rated health (SRH) level and self-rated Mibyeong severity, and aimed to assess this relationship using a fully automated image analysis system. The nose size features were evaluated from the frontal and profile face images of 810 participants. The nose size features consisted of five length features, one area feature, and one volume feature. The level of SRH and the Mibyeong severity were determined using a questionnaire. The normalized nasal height was negatively associated with the self-rated health score (SRHS) (partial ρ = -0.125, p = 3.53E-04) and the Mibyeong score (MBS) (partial ρ = -.172, p = 9.38E-07), even after adjustment for sex, age, and body mass index. The normalized nasal volume (ρ = -.105, p = 0.003), the normalized nasal tip protrusion length (ρ = -.087, p = 0.014), and the normalized nares width (ρ = -.086, p = .015) showed significant correlation with the SRHS. The normalized nasal area (ρ = -.118, p = 0.001), the normalized nasal volume (ρ = -.107, p = .002) showed significant correlation with the MBS. The wider, longer, and larger the nose, the lower the SRHS and MBS, indicating that health status can be estimated based on the size and shape features of the nose.

• Journal of the Korean Society of Radiology
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• v.81 no.3
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• pp.632-643
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• 2020

Purpose To investigate the correlation between magnetic resonance (MR) image-based radiomics features and the genomic features of breast cancer by focusing on biomolecular intrinsic subtypes and gene expression profiles based on risk scores. Materials and Methods We used the publicly available datasets from the Cancer Genome Atlas and the Cancer Imaging Archive to extract the radiomics features of 122 breast cancers on MR images. Furthermore, PAM50 intrinsic subtypes were classified and their risk scores were determined from gene expression profiles. The relationship between radiomics features and biomolecular characteristics was analyzed. A penalized generalized regression analysis was performed to build prediction models. Results The PAM50 subtype demonstrated a statistically significant association with the maximum 2D diameter (p = 0.0189), degree of correlation (p = 0.0386), and inverse difference moment normalized (p = 0.0337). Among risk score systems, GGI and GENE70 shared 8 correlated radiomic features (p = 0.0008-0.0492) that were statistically significant. Although the maximum 2D diameter was most significantly correlated to both score systems (p = 0.0139, and p = 0.0008), the overall degree of correlation of the prediction models was weak with the highest correlation coefficient of GENE70 being 0.2171. Conclusion Maximum 2D diameter, degree of correlation, and inverse difference moment normalized demonstrated significant relationships with the PAM50 intrinsic subtypes along with gene expression profile-based risk scores such as GENE70, despite weak correlations.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.6
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• pp.228-237
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• 2013

In this paper, we propose a normalized cross correlation(NCC)-based multiview background subtraction method which is robust when an object and background have similar color. When the background of the capturing environment is not artificially composed, the regions in the background images which would be occluded by an object tends to have difference colors. The colors of those regions, however, becomes similar when an object enters the capturing environment. Based on this assumption, this paper proposes a concept of GoNCC(Graph of Normalized Cross Correlation). GoNCC is the distribution of NCC between a pixel in an image and pixels related by epipolar constraints with the pixel. The proposed multiview background subtraction method is performed by comparing GoNCC of the current images with the background images. To reduce computational complexity, we perform multiview background subtraction only to the pixels undetermined by single view background subtraction. Experimental results show that the proposed method is more robust to color similarity between an object and background than a single-view background subtraction method and a previous multiview background subtraction method.

• Journal of information and communication convergence engineering
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• v.8 no.2
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• pp.155-158
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• 2010

In this article, we proposed a new interference canceller using the adaptive algorithm. We designed constant modulus algorithm-correlation normailized least mean square (CMA-CNLMS) for wireless system. This structure is normalized LMS algorithm using correlation between the desired and input signal for cancelling the interference signals in the wideband code division multiple access (WCDMA) system. We showed that the proposed algorithm could improve the Mean Square Error (MSE) performance of LMS algorithm. MATLAB (Matrix Laboratory) is employed to analyze the proposed algorithm and to compare it with the experimental results. The MSE value of the LMS with mu=0.0001 was measured as - 12.5 dB, and that of the proposed algorithm was -19.5 dB which showed an improvement of 7dB.

• Journal of Korea Multimedia Society
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• v.20 no.7
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• pp.979-985
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• 2017

This paper, which covers a fluorescence microscopy image of brain tumor cells, looks at drug reactions by treating different types and concentrations of drugs on a plate of $24{\times}16$ wells. Due to the limitation of the field of view, a well was taken into 9 field images, and each has an overlapping area with its neighboring fields. To analyze more precisely, image stitching is needed. The basic method is finding a similar area using normalized cross-correlation (NCC). The problem is that some overlapping areas may not have any duplicated cells that help to find the matching point. In addition, the cell objects have similar sizes and shapes, which makes distinguishing them difficult. To avoid calculating similarity between blank areas and roughly distinguishing different cells, thresholding is added. The thresholding method classifies background and cell objects based on fixed thresholds and finds the location of the first seen cell. After getting its location, NCC is used to find the best correlation point. The results are compared with a simple boundary stitched image. Our proposed method stitches images that are connected in a grid form without collision, selecting the best correlation point among areas that contain overlapping cells and ones without it.