• Journal of Digital Convergence
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• v.11 no.2
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• pp.243-248
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• 2013

This paper proposes an effective method using Adaptive Boosting to track a person's head in complex background. By only one way to feature extraction methods are not sufficient for modeling a person's head. Therefore, the method proposed in this paper, several feature extraction methods for the accuracy of the detection head running at the same time. Feature Extraction for the imaging of the head was extracted using sub-region and Haar wavelet transform. Sub-region represents the local characteristics of the head, Haar wavelet transform can indicate the frequency characteristics of face. Therefore, if we use them to extract the features of face, effective modeling is possible. In the proposed method to track down the man's head from the input video in real time, we ues the results after learning Harr-wavelet characteristics of the three types using AdaBoosting algorithm. Originally the AdaBoosting algorithm, there is a very long learning time, if learning data was changes, and then it is need to be performed learning again. In order to overcome this shortcoming, in this research propose efficient method using cascade AdaBoosting. This method reduces the learning time for the imaging of the head, and can respond effectively to changes in the learning data. The proposed method generated classifier with excellent performance using less learning time and learning data. In addition, this method accurately detect and track head of person from a variety of head data in real-time video images.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.2
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• pp.178-186
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• 2003

Recently, many researchers have been interested in biometric systems such as fingerprint, handwriting, key-stroke patterns and human iris. From the viewpoint of reliability and robustness, iris recognition is the most attractive biometric system. Moreover, the iris recognition system is a comfortable biometric system, since the video image of an eye can be taken at a distance. In this paper, we discuss human iris recognition, which is based on accurate iris localization, robust feature extraction, and Neural Network classification. The iris region is accurately localized in the eye image using a multiresolution active snake model. For the feature representation, the localized iris image is decomposed using wavelet transform based on dyadic Haar wavelet. Experimental results show the usefulness of wavelet transform in comparison to conventional Gabor transform. In addition, we present a new method for setting initial weight vectors in competitive learning. The proposed initialization method yields better accuracy than the conventional method.

• Journal of Korea Multimedia Society
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• v.14 no.8
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• pp.992-999
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• 2011

This paper proposed a human body posture recognition program based on haar-like feature and hand activity detection. Its distinguishing features are the combination of face detection and motion detection. Firstly, the program uses the haar-like feature face detection to receive the location of human face. The haar-like feature is provided with the advantages of speed. It means the less amount of calculation the haar-like feature can exclude a large number of interference, and it can discriminate human face more accurately, and achieve the face position. Then the program uses the frame subtraction to achieve the position of human body motion. This method is provided with good performance of the motion detection. Afterwards, the program recognises the human body motion by calculating the relationship of the face position with the position of human body motion contour. By the test, we know that the recognition rate of this algorithm is more than 92%. The results show that, this algorithm can achieve the result quickly, and guarantee the exactitude of the result.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.12 no.7
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• pp.1131-1138
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• 2001

This paper presents the new Compact 2D Haar-wavelet-based MultiResolution Time-Domain method (MRTD) as an accelerating algorithm for the conventional Compact BD Finite-Difference Time-Domain method (FDTD). To validate this algorithm, we analyzed the dispersion characteristics of the hollow rectangular waveguide and dielectric slab-loaded rectangular waveguide. The results of the proposed method are very weal agreed with those of both the conventional analytic method and the Compact 2D FDTD method. The CPU time for analysis of this method is reduced to about a half of the conventional Compact 2D FDTD method. The proposed method is valuable as a fast algorithm in the research of dispersion characteristics of waveguide structures.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.10 no.6
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• pp.935-950
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• 1999

A multiresolution time-domain analysis scheme is derived for the analysis of microwave structures by using Haar wavelets to discretize the Maxwell's curl equation. This technique requires less computational effort than the conventional FDTD method because larger space grid can be used in the simulations. To validate this scheme, several 2-D·3-D microwave structures are simulated and the results are compared with those of the conventional FDTD scheme.

• Korean Journal of Remote Sensing
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• v.25 no.3
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• pp.215-223
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• 2009

The noise removal as a preprocessing was tried with various kinds of wavelet pairs. Wavelet transform for 2D images generally uses the same wavelets as basis functions in horizontal and vertical directions. A method with different wavelets was tried for each direction separately, which gives more precise interpretation of the classification. Total 486 pairs of wavelets from nine basis functions were tried to remove image noises. The classification accuracies before and after the noise removal were compared. Although all kinds of wavelet pairs showed the increased accuracies in classification, there were best and worst wavelet pairs depending on the data sets. Wavelet pairs with low energy percentage of LL band showed the high classification accuracy. A pattern was found in the results that very similar vertical accuracy was distributed for each horizontal ones. Since Haar is the shortest length filter, Haar could be a predictor wavelet to find the good wavelet pairs.

• Computers and Concrete
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• v.4 no.3
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• pp.243-257
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• 2007

A novel method for recognition, characterization, and quantification of deterioration in bridge components and laboratory concrete samples is presented in this paper. The proposed scheme is based on grey level co-occurrence matrix texture analysis using Haar's discrete wavelet transform on concrete imagery. Each image is described by a subset of band-filtered images containing wavelet coefficients, and then reconstructed images are employed in characterizing the texture, using grey level co-occurrence matrices, of the different types and degrees of damage: map-cracking, spalling and steel corrosion. A comparative study was conducted to evaluate the efficiency of the supervised maximum likelihood and unsupervised K-means classification techniques, in order to classify and quantify the deterioration and its extent. Experimental results show both methods are relatively effective in characterizing and quantifying damage; however, the supervised technique produced more accurate results, with overall classification accuracies ranging from 76.8% to 79.1%.

• Coupled systems mechanics
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• v.12 no.1
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• pp.21-40
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• 2023

In thisstudy, the vibration problem ofthermo elastic carbon nanotubes conveying pulsating viscous nano fluid subjected to a longitudinal magnetic field is investigated via Euler-Bernoulli beam model. The controlling partial differential equation of motion is arrived by adopting Eringen's non local theory. The instability domain and pulsation frequency of the CNT is obtained through the Galerkin's method. The numerical evaluation of thisstudy is devised by Haar wavelet method (HWM). Then, the proposed model is validated by analyzing the critical buckling load computed in presentstudy with the literature. Finally, the numerical calculation ofsystem parameters are shown as dispersion graphs and tables over non local parameter, magnetic flux, temperature difference, Knudsen number and viscous parameter.

• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.165-168
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• 2000

A fast estimation method using wavelet transform for a time delay system is proposed. Main point of this method is to get the wavelet transform of the correlation between the input signal and delayed signal using transformed signals. But wavelet transform using Haar wavelet functions has basis with different phases and can offers a bisection method to estimate a time delay of a signal. Selective computation of the transform of correlation is performed and the computational complexity is reduced. Computational order of this method is O(N log N) and it is much love. than a simple correlation esimation when the length of signal is long.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.2
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• pp.1-10
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• 2003

This paper presents an efficient method for image segmentation based on a multiresolution application of a wavelet transform and watershed segmentation algorithm. The procedure toward complete segmentation consists of four steps: pyramid representation, image segmentation, region merging and region projection. First, pyramid representation creates multiresolution images using a wavelet transform. Second, image segmentation segments the lowest-resolution image of the pyramid using a watershed segmentation algorithm. Third, region merging merges the segmented regions using the third-order moment values of the wavelet coefficients. Finally, the segmented low-resolution image with label is projected into a full-resolution image (original image) by inverse wavelet transform. Experimental results of the presented method can be applied to the segmentation of noise or degraded images as well as reduce over-segmentation.