• The KIPS Transactions:PartB
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• v.16B no.5
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• pp.367-376
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• 2009

In this paper, we propose a flame detection method using Haar wavelet and moving averages in outdoor infrared video sequences. Our proposed method is composed of three steps which are Haar wavelet decomposition, flame candidates detection, and their tracking and flame classification. In Haar wavelet decomposition, each frame is decomposed into 4 sub- images(LL, LH, HL, HH), and also computed high frequency energy components using LH, HL, and HH. In flame candidates detection, we compute a binary image by thresholding in LL sub-image and apply morphology operations to the binary image to remove noises. After finding initial boundaries, final candidate regions are extracted using expanding initial boundary regions to their neighborhoods. In tracking and flame classification, features of region size and high frequency energy are calculated from candidate regions and tracked using queues, and we classify whether the tracked regions are flames by temporal changes of moving averages.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.5
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• pp.205-211
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• 2012

By using an adaptive threshold and weight based on the wavelet transform and Haar transform, a novel image enhancement algorithm is proposed. First, a medical image was decomposed with wavelet transform and all high-frequency sub-images were decomposed with Haar transform. Secondly, noise in the frequency domain was reduced by the proposed soft-threshold method. Thirdly, high-frequency coefficients were enhanced by the proposed weight values in different sub-images. Then, the enhanced image was obtained through the inverse Haar transform and wavelet transform. But the pixel range of the enhanced image is narrower than a normal image. Lastly, the image's histogram was stretched by nonlinear histogram equalization. Experiments showed that the proposed method can be not only enhance an image's details but can also preserve its edge features effectively.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.1
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• pp.160-170
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• 2010

This paper proposes a Haar Wavelet Feature Detector (HWFD) based on the Haar wavelet transform and average box filter. By decomposing the original image using the Haar wavelet transform, the proposed detector obtains the variance information of the image, making it possible to extract more distinctive features from the original image. For detection of interest points that represent the regions whose variance is the highest among their neighbor regions, we apply the average box filter to evaluate the local variance information and use the integral image technique for fast computation. Due to utilization of the Haar wavelet transform and the average box filter, the proposed detector is robust to illumination change, scale change, and rotation of the image. Experimental results show that even though the proposed method detects fewer interest points, it achieves higher repeatability, higher efficiency and higher matching accuracy compared with the DoG detector and Harris corner detector.

• Structural Engineering and Mechanics
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• v.45 no.1
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• pp.95-110
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• 2013

In the recent decade, practical of wavelet technique is being utilized in various domain of science. Particularly, engineers are interested to the wavelet solution method in the time series analysis. Fundamentally, seismic responses of structures against time history loading such as an earthquake, illustrates optimum capability of systems. In this paper, a procedure using particularly discrete Haar wavelet basis functions is introduced, to solve dynamic equation of motion. In the proposed approach, a straightforward formulation in a fluent manner is derived from the approximation of the displacements. For this purpose, Haar operational matrix is derived and applied in the dynamic analysis. It's free-scaled matrix converts differential equation of motion to the algebraic equations. It is shown that accuracy of dynamic responses relies on, access of load in the first step, before piecewise analysis added to the technique of equation solver in the last step for large scale of wavelet. To demonstrate the effectiveness of this scheme, improved formulations are extended to the linear and nonlinear structural dynamic analysis. The validity and effectiveness of the developed method is verified with three examples. The results were compared with those from the numerical methods such as Duhamel integration, Runge-Kutta and Wilson-${\theta}$ method.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.5
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• pp.677-682
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• 2010

This paper addresses a method of estimating roughly the human pose by comparing Haar-wavelet value which is learned in face detection technology using AdaBoost algorithm. We also presents its application to face recognition. The learned weak classifier is used to a Haar-wavelet robust to each pose's feature by comparing the coefficients during the process of face detection. The Mahalanobis distance is used to measure the matching degree in Haar-wavelet selection. When a facial image is detected using the selected Haar-wavelet, the pose is estimated. The proposed pose estimation can be used to improve face recognition speed. Experiments are conducted to evaluate the performance of the proposed method for pose estimation.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.11 no.4
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• pp.667-673
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• 2000

This paper presents the modeling method of nonlinear circuits with lumped elements by using a hybrid Haar -wavelet MRTD/FDTD techniques. To analyze nonlinear circuits with lumped elements, the Haar-wavelet MRTD scheme is applied to the entire structure of interest and the conventional FDTD scheme is locally used to describe the characteristics of the lumped elements. To validate the scheme, microstrip structure with lumped elements and a single diode mixer are simulated.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.12
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• pp.1270-1277
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• 2008

A Haar wavelet based numerical method for solving singularly perturbed linear time invariant system is presented in this paper. The reduced pure slow and pure fast subsystems are obtained by decoupling the singularly perturbed system and differential matrix equations are converted into algebraic Sylvester matrix equations via Haar wavelet technique. The operational matrix of integration and its inverse matrix are utilized to reduce the computational time to the solution of algebraic matrix equations. Finally a numerical example is given to demonstrate the validity and applicability of the proposed method.

• The Korean Journal of Applied Statistics
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• v.31 no.6
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• pp.707-719
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• 2018

Various time series representation methods have been proposed for efficient time series clustering and classification. Lin et al. (DMKD, 15, 107-144, 2007) proposed a symbolic aggregate approximation (SAX) method based on symbolic representations after approximating the original time series using piecewise local mean. The performance of SAX therefore depends heavily on how well the piecewise local averages approximate original time series features. SAX equally divides the entire series into an arbitrary number of segments; however, it is not sufficient to capture key features from complex, large-scale time series data. Therefore, this paper considers data-adaptive local constant approximation of the time series using the unbalanced Haar wavelet transformation. The proposed method is shown to outperforms SAX in many real-world data applications.

• Journal of the Korea Society for Simulation
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• v.16 no.4
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• pp.13-22
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• 2007

Fault detection of cycle-based signals is typically performed using statistical approaches. Univariate SPC using few representative statistics and multivariate analysis methods such as PCA and PLS are the most popular methods for analyzing cycle-based signals. However, such approaches are limited when dealing with information-rich cycle-based signals. In this paper, process fault defection method based on wavelet analysis is proposed. Using Haar wavelet, coefficients that well reflect the process condition are selected. Next, Hotelling's $T^2$ chart using selected coefficients is constructed for assessment of process condition. To enhance the overall efficiency of fault detection, the following two steps are suggested, i.e. denoising method based on wavelet transform and coefficient selection methods using variance difference. For performance evaluation, various types of abnormal process conditions are simulated and the proposed algorithm is compared with other methodologies.

• Journal of the Korean Magnetic Resonance Society
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• v.6 no.1
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• pp.12-19
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• 2002

A shift-averaged Haar wavelet transform was introduced as a new and excellent tool to distinguish real peaks from the noise contaminated NMR signals. It is based on Haar wavelet transform and translation-invariant denoising process. Donoho＇s universal threshold was newly introduced to the shift-averaged Haar wavelet transform for the purpose of automated noise suppression, and was quantitatively compared with the conventional uniform threshold method in terms or threshold and signal to noise ratio (SNR). New algorithm was combined with a routine to suppress a large solvent peak by singular value decomposition (SVD). Combined algorithm was applied to the real spectrum that containing large solvent peak.