• Proceedings of the Korea Contents Association Conference
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• 2004.11a
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• pp.348-351
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• 2004

In this paper, we propose the face recognition method using Harr wavelet transform and 2D PCA. While previous PCA computed the covariance matrix by using one dimensional vectors, 2D PCA computed the covarinace matrix by using direct two dimensional image and extracted feature vector by solving eigenvalue problem. To gain the face image having the low dimension and robust property, the proposed method uses wavelet transformation. We apply the LL band image data to 2D PCA for face recognition. The experimental results indicate that our method improves recognition rate than 2D PCA into original image.

• Journal of the Institute of Convergence Signal Processing
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• v.1 no.1
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• pp.82-88
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• 2000

In this paper, We compare the Harr wavelet of the S+P transform with various integer coefficient filter banks and apply 9/7 ICFB to the wavelet transform. In addition, we propose a entropy-coding method that exploits the multiresolution structure and the feedback of the prediction error, and can efficiently compress the transformed image for progressive transmission. Simulation results are included to compare to the compression ratio using the S+P transform with different types of images.

• The KIPS Transactions:PartA
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• v.13A no.7 s.104
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• pp.639-646
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• 2006

Data compression technologies enable us to store and transfer large amount of data efficiently, and become more and more important due to increasing data size and the network traffic. Moreover, as a result of the increase of computing power, volumetric data produced from various applied science and engineering fields has been getting much larger. In this Paper, we present a volume compression scheme which exploits Daubeches wavelet transform. The proposed scheme basically supports lossy compression for 3D volume data, and provides unit-wise random accessibility. Since our scheme shows far lower error rates than the previous compression methods based on Haar filter, it could be used well for interactive visualization applications as well as large volume data compression requiring image fidelity.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.2 no.4
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• pp.19-28
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• 2009

This paper is a study on method that the EZW algorithm is proposed effective compression technique of wavelet transformed image. The EZW algorithm is encoded by zerotree coding technique using self-similarity of wavelet coefficients. If the coefficient is larger than the threshold a POS coded, if the coefficients is smaller than minus the threshold a NEG is coded. If the coefficient is the root of a zerotree than a ZTR is coded and finally, if the coefficient is smaller then the threshold but it is not the root of a zerotree, than an IZ is coded. This process is repeated until all the wavelet coefficients have been encoded completely. This paper was compared to EZW algorithm and a widely available version of JPEG. As the results of compare, it is shown that the PSNR of the EZW algorithm is better than JPEG.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.42 no.6
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• pp.75-82
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• 2005

The robust feature vector selection method for the multiple frontal face detection is proposed in this paper. The proposed feature vector for the training and classification are integrated by means, amplitude projections, and its 1D Harr wavelet of the input image. And the statistical modeling is performed both for face and nonface classes. Finally, the estimated probability density functions (PDFs) are applied for the detection of multiple frontal faces in the still image. The proposed method can handle multiple faces, partially occluded faces, and slightly posed-angle faces. And also the proposed method is very effective for low quality face images. Experimental results show that detection rate of the propose method is $98.3\%$ with three false detections on the testing data, SET3 which have 227 faces in 80 images.