• Journal of the Korean Institute of Intelligent Systems
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• 제22권5호
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• pp.555-562
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• 2012

This paper presents a hybrid method for improving the recognition performance, which is based on the nonlinear histogram equalization, features extraction, and statistical correlation of images. The nonlinear histogram equalization based on a logistic function is applied to adaptively improve the quality by adjusting the brightness of the image according to its intensity level frequency. The statistical correlation that is measured by the normalized cross-correlation(NCC) coefficient, is applied to rapidly and accurately express the similarity between the images. The local features based on independent component analysis(ICA) that is used to calculate the NCC, is also applied to statistically measure the correct similarity in each images. The proposed method has been applied to the problem for recognizing the 30-face images of 40*50 pixels. The experimental results show that the proposed method has a superior recognition performances to the method without performing the preprocessing, or the methods of conventional and adaptively modified histogram equalization, respectively.

• Journal of the Korean Institute of Intelligent Systems
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• 제20권5호
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• pp.701-709
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• 2010

This paper presents an efficient signature recognition method by using the hybrid similarity criterion, which is in inverse proportion to distance and in proportion to correlation between the images. The distance is applied to express the spacial property of image, and the correlation is also applied to express the statistical property. The proposed criterion provides the robust recognition to both the geometrical variations such as position, size, and rotation and the shape variation. The normalized cross-correlation(NCC), which is calculated by considering 4 directions based on the histogram of binary image, is applied to express rapidly and accurately the similarity between the images. The proposed method has been applied to the problem for recognizing the 20 truck images of 288*288 pixels and the 105(3 persons * 35 images) signature images of 256*256 pixels, respectively. The experimental results show that the proposed method has a superior recognition performance that appears the image characters well. Especially, the hybrid criterion of NCC and ordinal distance has a superior recognition performance to the hybrid criterion using city-block or Euclidean distance.

• Journal of the Korean Institute of Intelligent Systems
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• 제24권5호
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• pp.504-511
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• 2014

This paper presents a hybrid recognition method, which is based on the nonlinear histogram equalization and the multidimensional intensity variation of an images. The nonlinear histogram equalization based on a adaptively modified function is applied to improve the quality by adjusting the brightness of the image. The multidimensional intensity variation by considering the a extent of 4-step changes in brightness between the adjacent pixels is also applied to reflect accurately the attributes of image. The statistical correlation that is measured by the normalized cross-correlation(NCC) coefficient, is applied to comprehensively measure the similarity between the images. The NCC is considered by the intensity variation of each 2-direction(x-axis and y-axis) image. The proposed method has been applied to the problem for recognizing the 50-face images of 40*40 pixels. The experimental results show that the proposed method has a superior recognition performances to the method without performing the histogram equalization, or the linear histogram equalization, respectively.

• Journal of the Institute of Electronics and Information Engineers
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• 제52권6호
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• pp.107-116
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• 2015

In compliance with digital device growth, the proliferation of high-tech computers, the availability of high quality and inexpensive video cameras, the demands for automated video analysis is increasing, especially in field of intelligent monitor system, video compression and robot vision. That is why object tracking of computer vision comes into the spotlight. Tracking is the process of locating a moving object over time using a camera. The consideration of object's scale, rotation and shape deformation is the most important thing in robust object tracking. In this paper, we propose a robust object tracking scheme using Random Forest. Specifically, an object detection scheme based on region covariance and ZNCC(zeros mean normalized cross correlation) is adopted for estimating accurate object location. Next, the detected region will be divided into five regions for random forest-based learning. The five regions are verified by random forest. The verified regions are put into the model pool. Finally, the input model is updated for the object location correction when the region does not contain the object. The experiments shows that the proposed method produces better accurate performance with respect to object location than the existing methods.

• Journal of KSNVE
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• 제4권3호
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• pp.337-343
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• 1994

A method of the leak detection from the pipe system by using accelerometer is proposed. The signal detected from accelerometer is proved experimentally to be a dispersive wave. Based on the experiments, a method using the narrow band pass filter and the unit impulse response function is analyzed. The method uses the characteristics of the unit impulse response function, that the function is available evenin the narrow band signal because, unlike the cross correlation, it is normalized by the auto spectrum. The accelerometer is quite easier to use than the hydrophone in adapting to the pipe system.

• Proceedings of the KIEE Conference
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• 대한전기학회 2008년도 학술대회 논문집 정보 및 제어부문
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• pp.308-309
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• 2008

본 논문에서는 추적하고자 하는 관심객체를 일정한 크기의 블록으로 나누어 각 블록이 독립적으로 추적을 수행한다. 나누어진 각 블록들은 NCC(Normalized Cross Correlation)를 사용하여 통계적인 특성을 고려하여 움직임을 추정한다. 추정된 블록들의 움직임 벡터 중 평한 벡터보다 일정 값 이상 큰 블록은 관심객체 움직임 벡터 추정 시 제외시킴으로써 잘못된 추정으로 인한 에러를 줄인다. 선택된 블록들의 추정 에러값에 따라 추정값이 높은 블록의 움직임 벡터는 높은 가중치를 적용하고 추정값이 낮은 블록의 움직임 벡터는 낮은 가중치를 적용하여 추적 신뢰도를 높였다. 실험결과, 제안된 알고리즘은 강건한 실시간 추적이 가능함을 보여준다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국내학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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• pp.224-227
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• 1993

In this paper, a new LMS algorithm with a variable step size (VVS LMS) is presented. The change of step size .mu. at each iteration, which increases or decreases according to the misadaptation degree, is computed by a proportional fuzzy logic controller. As a result the algorithm has very good convergence speed and low steady-state misadjustment. The norm of the cross correlation between the estimation error and input signal is used. As a measure of the misadaptation degree. Simulation results are presented to compare the performance of the VSS LMS algorithm with the normalized LMS algorithm.

• Journal of Industrial Technology
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• 제13권
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• pp.33-41
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• 1993

In this paper, a new LMS algorithm with a fuzzy variable step size (FVS LMS) is presented. The change of step size ${\mu}$, at each iteration which is increases or decreases according to the misadaptation degree, is computed by a proportional fuzzy logic controller. As a result the algorithm has very good convergence speed and low steady-state misadjustment. As a measure of the misadaptation degree, the norm of the cross correlation between the estimation error and input signal is used. Simulation results are presented to compare the performance of the FVSS LMS algorithm with the normalized LMS algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• 제17권9호
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• pp.1029-1039
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• 1992

This paper presents the filtering method which is processed on the frequency domain among Hangul character recognition methods. It is processed the Hangul character parrern with spatial positioning modulation and mapped the Hangul character element which have spatial position variant feature onto frequency domain, at this time, normalized spatial position and so normalized the character size in frequency domain. And it is grouped the Hangul character element according to the generating position and set the standard pattern, and used each standard character element pattern with character element filter and filtering the character pattern of Hangul character, it is derived the normalized cross correlation function and the coherence function led to the filtering results, and calculated classification threshold.

• Journal of Korea Multimedia Society
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• 제11권3호
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• pp.281-291
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• 2008

In this paper, we propose a new double talk detection algorithm which detects near end signals with less degradation, tracking echo path variation of echo canceler simultaneously. Our method makes use of a cross-correlation between channel input signals and estimated error signals and a normalized cross-correlation between microphone input signals and estimated error signals. By combing thresholds for these cross-correlations pertinently, this algorithm discriminates between variation of echo path and occurrence of double talk. These two cross-correlation are used to detect double talk periods, tracking echo path variation. During the detection period, adjustive adaptive filter is ceased to prevent the echo canceler from being disturbed by near end signals. Also, the echo canceler will still be kept on for tracking any variation in echo path. Through computer simulation results, it was confirmed that the proposed algorithm shows better performance, tracking echo path variation and detecting the double talk periods, than the Ye et. al's and the NLMS algorithms from ERLE viewpoint.