For signal-based design of orthonormal (ON) wavelets, an optimization of a cost function over an N -dimensional angle space is required. However: (1) the N -dim space includes both smooth and non-smooth wavelets; (2) many of the smooth wavelets are similar in shape. A more practical approach for some applications may be to construct a library of smooth ON wavelets in advance—a library that cons...

In this paper, we deal with the problem of function approximation from a given set of input/output data. This problem consists of analyzing training examples, so that we can predict the output of the model given new inputs. We present a new method for function approximation of the I/O data using Wavelet Neural Networks (WNN). This method is based on a new efficient method of optimizing the posi...

In this paper an adaptive wavelet kernel based on density SVM approach for P2P traffic classification is presented. The model combines the multi-scale learning ability of wavelet kernel and the advantages of support vector machine. Mexican hat wavelet function is used to build SVM kernel function. The wavelet kernel function is tuned adaptively according to the density of samples around support...

Wavelet radiosity is, by its nature, restricted to parallelograms or triangles. This paper presents an innovative technique enabling wavelet radiosity computations on planar surfaces of arbitrary shape, including concave contours or contours with holes. This technique replaces the need for triangulating such complicated shapes, greatly reducing the complexity of the wavelet radiosity algorithm ...

For a compactly supported wavelet there are non-polynomial functions with all their wavelet coeecients vanishing, and the class U of such functions has been characterized by P.-G. Lemari e-Rieusset. In this paper we study the properties of this class U more closely, and show how U relates to the characterization of function spaces in terms of wavelet coeecients, the asymptotic behavior of the w...

Every m th order cardinal spline wavelet is a linear combination of the functions fN (l) m+l (2x?j); j 2 Zg. Here the function N m is the m th order cardinal B-spline. In this paper we prove that the single function N (l) m+l (2x), or N (l) m+l (2x ? 1) is a wavelet when m and l satisfy some mild conditions. As l decreases, so does the support of the wavelet. When l increases, the smoothness of...

Importance sampling methods of wavelet products can deal with some direct rendering applications with only two functions. For multiple functions sampling that are more useful in global rendering, we present a wavelet-based factor method to simplify multiple function integral into triple function product issues. Then an optimal wavelet product representation is introduced. Major algorithms with ...

In this paper, we propose an object recognition method for 2D objects using wavelet descriptors. The descriptors are derived from the continuous wavelet transform using the Mexican hat function as mother wavelet. In contrast to the other known methods we apply an angle function to describe object contours extracted as polygons. The contour extraction is based on the object oriented contour extr...

For a smooth bivariate function defined on a general domain with arbitrary shape, it is difficult to do Fourier approximation or wavelet approximation. In order to solve these problems, in this paper, we give an extension of the bivariate function on a general domain with arbitrary shape to a smooth, periodic function in the whole space or to a smooth, compactly supported function in the whole ...