Design and implementation of the symmetrically extended 2-D wavelet transform

McCanny,; Masud,

doi:10.1109/icassp.2002.1005345

Cited by 7 publications

(4 citation statements)

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“…Giving a multiresolution analysis in both time and frequency domain and having different alternatives for the basis function makes wavelet transform a better candidate than Fourier transform [4,5] for many applications including JPEG2000 [6]. A growing number of publications that deal with hardware implementation of wavelet transform [7][8][9][10] are another proof of its applicability.…”

Section: Wavelet Based Feature Extractionmentioning

confidence: 98%

A robust wavelet based feature extraction method for face recognition

Makaremi

Ahmadi

2009

2009 IEEE International Conference on Systems, Man and Cybernetics

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Section: Wavelet Based Feature Extractionmentioning

confidence: 98%

A robust wavelet based feature extraction method for face recognition

Makaremi

Ahmadi

2009

2009 IEEE International Conference on Systems, Man and Cybernetics

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“…Wavelet transform [13] has been widely used in many fields such as JPEG2000 [14].A growing number of publications deal with hardware implementation of this transform [15][16][17][18] which demonstrates its utility in the area of Pattern Recognition. A multi-resolution analysis of a signal with localization in both time and frequency is the advantage of wavelet transform over Fourier and cosine transforms [19][20][21]. In order to perform feature extraction, the two-dimensional (2-D) wavelet transform of the faces is calculated using different basis functions only up to 1 scale .…”

Section: A Wavelet Transformmentioning

confidence: 99%

A novel way of face recogniton to improve the quality of features extraction

Wang

et al. 2009

2009 IEEE International Conference on Intelligent Computing and Intelligent Systems

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This paper sets to improve the representative information of the eigenvector of the face in which Haar wavelet is applied to decompose the face. The high-frequency of the face decomposed on 1 scale is preserved as a high-frequency feature-vector (HF), and the low-frequency of the face decomposition is applied for dimension reduced by Fisher's linear discriminant(FLD). Then we combined the high-frequency feature-vector and the eigenvector extracted by FLD as a stand-by recognition face eigenvector(SRFE). The data of the SRFE is used to train and to test a fuzzy neural network which is applied for face recognition. A simulation of the proposed algorithm is done on the basis of Olivetti Research Lab(ORL) face database, and the results show that the algorithm is able to recognize quickly with high recognition rate.

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“…By doing so, a simple control and routing can be achieved without the need for a N 2 memory units. The computation of different levels of decomposition is scheduled according to row-based RPA scheduling [12]. The entire row of input image (or intermediate LL, LH, HL and HH results) is fed into filter bank at a time.…”

Section: Arc2d-i: Separable Mrpa-based Architecturementioning

confidence: 99%

Framework for FPGA-based discrete biorthogonal wavelet transforms implementation

Uzun

Amira

2006

IEE Proc., Vis. Image Process.

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Abstract-During the last decade, the wavelet transform has proven to be a valuable tool in many application fields including telecommunication, numerical analysis and most notably image/video compression. This paper describes a high-level framework dedicated to the implementation of 1-D and 2-D Discrete Biorthogonal Wavelet Transforms (DBWTs) on FPGAs. The system hides the low level hardware details of the FPGA structure and thus allows the user to concentrate more on the experimentation rather than on the low-level architecture. The DBWT architectures proposed within the framework are scalable, modular and have less area and time complexity when compared with existing structures. FPGA implementation results based on a Xilinx Virtex-2000E device have shown that proposed system provides an efficient solution for the processing of DBWTs in real-time.

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Design and implementation of the symmetrically extended 2-D wavelet transform

Cited by 7 publications

References 0 publications

A robust wavelet based feature extraction method for face recognition

A robust wavelet based feature extraction method for face recognition

A novel way of face recogniton to improve the quality of features extraction

Framework for FPGA-based discrete biorthogonal wavelet transforms implementation

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