Dual-Tree Complex Wavelet Transform in the Frequency Domain and an Application to Signal Classification

Neumann, Julia; Steidl, Gabriele

doi:10.1142/s0219691305000749

Cited by 28 publications

(15 citation statements)

References 26 publications

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“…The 2D DTCWT architecture based on the discussions in [14] is shown in Figure 1. The input image is processed along the rows with the filter pair Fh={H0a, H0b} and Fg={H1a, H1b}, the filters satisfy the conditions H1x = H0x (n−1).…”

Section: Image Compression Algorithmmentioning

confidence: 99%

Design of DTCWT-DWT Image Compressor-Decompressor with Companding Algorithm

Venkateshappa¹,

P²

2017

AIVP

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Discrete Wavelet Transform (DWT) has demonstrated advantages in image compression due to its time-frequency resolution property. Dual Tree Complex Wavelet Transform (DTCWT) in addition to the advantages of DWT supports additional properties such as directionality and shift invariant. DTCWT has coefficient redundancy of 4:1 or 2 m :1 in m dimensions thus is four time complex than DWT computation. In this work, DTCWT based image compression algorithm is proposed that performs decomposition using DTCWT and reconstruction is carried out using DWT. The DTCWT bands are companded and encoded using entropy encoder and the quantization of the sub bands introduces minimum loss. The proposed algorithm achieves 77% compression with 40 dB PSNR thus suitable for real time image compression. DTCWT filters are appropriately selected to achieve perfect reconstruction.

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Section: Image Compression Algorithmmentioning

confidence: 99%

Design of DTCWT-DWT Image Compressor-Decompressor with Companding Algorithm

Venkateshappa¹,

P²

2017

AIVP

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“…These 2-norms constitute the feature vector of the signal. The 2-norm has the advantage that no back transform of the Fourier coefficients from the frequency to the time domain is necessary [7].…”

Section: Dt-cwt and Its Applicationmentioning

confidence: 99%

“…Otherwise the results will be too sensitive to time locations [6][7]. A major problem of the Discrete Wavelet Transform (DWT) is lack of shift invariance.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Roller Bearing Using Dual-Tree Complex Wavelet Transform, Rough Set and Neural Network

Zhi-xin¹,

Gao²

2013

Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)

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Abstract-In a complex field environment for modern mechanical equipment, how to identify all kinds of operational status of the rolling element bearings fastly and accurately is very important and necessary. A novel approach to automated diagnosis is introduced, which is based on feature extraction with the Dual-Tree Complex Wavelet Transform (DT-CWT), then attribute reduction with rough set theory and finally pattern recognition with Artificial Neural Network. In our experiment, 4 kinds of states on a rolling element bearing test table, including normal, pitting on inner ring, pitting on outer ring and pitting on rolling element, are adopted. The experimental results indicate that the proposed feature extraction and automated diagnosis method can extract significant feature sets from signal, and can accurately distinguish many fault pattern, and has some practical value for the on-line condition monitoring of modern industrial demands.

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“…Data compression [21], motion estimation [22], segmentation and classification [23,24] and denoising [25] are only some examples. It is perceived that the wavelet transform is an important tool for analysis and processing of signals and images.…”

Section: The Proposed Approachmentioning

confidence: 99%

A New Method for 3D Thinning of Hybrid Shaped Porous Media Using Artificial Intelligence. Application to Trabecular Bone

et al. 2010

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Curve and surface thinning are widely-used skeletonization techniques for modeling objects in three dimensions. In the case of disordered porous media analysis, however, neither is really efficient since the internal geometry of the object is usually composed of both rod and plate shapes. This paper presents an alternative to compute a hybrid shape-dependent skeleton and its application to porous media. The resulting skeleton combines 2D surfaces and 1D curves to represent respectively the plate-shaped and rod-shaped parts of the object. For this purpose, a new technique based on neural networks is proposed: cascade combinations of complex wavelet transform (CWT) and complex-valued artificial neural network (CVANN). The ability of the skeleton to characterize hybrid shaped porous media is demonstrated on a trabecular bone sample. Results show that the proposed method achieves high accuracy rates about 99.78%-99.97%. Especially, CWT (2nd level)-CVANN structure converges to optimum results as high accuracy rate-minimum time consumption.

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Dual-Tree Complex Wavelet Transform in the Frequency Domain and an Application to Signal Classification

Cited by 28 publications

References 26 publications

Design of DTCWT-DWT Image Compressor-Decompressor with Companding Algorithm

Design of DTCWT-DWT Image Compressor-Decompressor with Companding Algorithm

Fault Diagnosis of Roller Bearing Using Dual-Tree Complex Wavelet Transform, Rough Set and Neural Network

A New Method for 3D Thinning of Hybrid Shaped Porous Media Using Artificial Intelligence. Application to Trabecular Bone

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