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Texture analysis and classification with tree-structured wavelet transform
IEEE Transactions on Image Processing, 1993A multiresolution approach based on a modified wavelet transform called the tree-structured wavelet transform or wavelet packets is proposed. The development of this transform is motivated by the observation that a large class of natural textures can be ...
Tianhorng Chang, C.-C. Jay Kuo
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2002
We mentioned in the introduction to Part D the shortcomings of the windowed Fourier transform. This chapter gives another approach to the time-frequency issue of Fourier analysis. The role played in the windowed Fourier transform by the family of functions $${\omega _{v,b}}(t) = \omega (t - b){e^{ + 2i\pi vt}},\quad b,v \in \mathbb{R}$$ is ...
Pierre Brémaud, Pierre Brémaud
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We mentioned in the introduction to Part D the shortcomings of the windowed Fourier transform. This chapter gives another approach to the time-frequency issue of Fourier analysis. The role played in the windowed Fourier transform by the family of functions $${\omega _{v,b}}(t) = \omega (t - b){e^{ + 2i\pi vt}},\quad b,v \in \mathbb{R}$$ is ...
Pierre Brémaud, Pierre Brémaud
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Vigorous image steganography with transforms, wavelet transforms and hybrid wavelet transforms
2014 Annual IEEE India Conference (INDICON), 2014In this paper, performance of orthogonal transforms with their wavelet transforms and Hybrid wavelet transforms are compared for image steganography. Here, wavelet transforms and Hybrid wavelet transforms are proposed to be used for Image steganography. A set of 10 Cover images for hiding 10 varied message images has been used for research.
Smita S. Chavan, Sudeep D. Thepade
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2011
The concept of a transform was introduced in Section 24.1 and the rest of Chapter 24 discusses orthogonal transforms. The transforms dealt with in this chapter are different and are referred to as subband transforms, because they partition an image into various bands or regions that contain different features of the image.
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The concept of a transform was introduced in Section 24.1 and the rest of Chapter 24 discusses orthogonal transforms. The transforms dealt with in this chapter are different and are referred to as subband transforms, because they partition an image into various bands or regions that contain different features of the image.
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Overcomplete Wavelet Transforms
1998This chapter deals with discrete wavelet transforms that are formed from the general samples of a continuous wavelet transform. Conceptually, there are few constraints on the spacing between sample points throughout the time—scale plane; however, computational consideration is restricted here to an interesting subclass of sampling sets that allow for ...
Anthony Teolis, Anthony Teolis
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Wavelet Transforms and Wavelet Approximations
1994We summarize properties of classical wavelet transforms and Wavelet Stieltjes transforms. Wavelet approximation problems are also considered for Wavelet Stieltjes transforms. This will give rise to some characterizations of general signals.
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Discrete Lattice Wavelet Transform
IEEE Transactions on Circuits and Systems II: Express Briefs, 2007The discrete wavelet transform (DWT) has gained a wide acceptance in denoising and compression coding of images and signals. In this work we introduce a discrete lattice wavelet transform (DLWT). In the analysis part, the lattice structure contains two parallel transmission channels, which exchange information via two crossed lattice filters.
Olkkonen, H., Olkkonen, Juuso
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1993
Chapter 1 presented the concepts involved with wavelet theory, especially the scaling operation, and avoided all of the mathematical rigor. This section supports the conceptual statements by providing the mathematical justification. General, continuous-time wavelet transforms are initially discussed.
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Chapter 1 presented the concepts involved with wavelet theory, especially the scaling operation, and avoided all of the mathematical rigor. This section supports the conceptual statements by providing the mathematical justification. General, continuous-time wavelet transforms are initially discussed.
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2001
Nomenclature a scale f frequency fs sampling frequency G(f) Fourier transform of g(t) Image Fourier transform of Image(t) S(t, f) Short-time Fourier transform x(t) continuous time signal w(t) windowing function ?(n) Kronecker delta function ?f sampling intervals in frequency ?t sampling intervals in ...
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Nomenclature a scale f frequency fs sampling frequency G(f) Fourier transform of g(t) Image Fourier transform of Image(t) S(t, f) Short-time Fourier transform x(t) continuous time signal w(t) windowing function ?(n) Kronecker delta function ?f sampling intervals in frequency ?t sampling intervals in ...
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Biorthogonal Wavelet Transforms
2014Wavelets in the polynomial and discrete spline spaces were introduced in Chaps. 8 and 10, respectively. In both cases, the wavelets’ design and implementation of the transforms were associated with perfect reconstruction (PR) filter banks. In this chapter, those associations are discussed in more detail.
Amir Averbuch +2 more
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