Title :
Moment-preserving piecewise linear approximations of signals and images
Author :
Nguyen, Thai B. ; Oommen, B. John
Author_Institution :
Sch. of Comput. Sci., Carleton Univ., Ottawa, Ont., Canada
fDate :
1/1/1997 12:00:00 AM
Abstract :
Approximation techniques are an important aspect of digital signal and image processing. Many lossy signal compression procedures such as the Fourier transform and discrete cosine transform are based on the idea that a signal can be represented by a small number of transformed coefficients which are an approximation of the original. Existing approximation techniques approach this problem in either a time/spatial domain or transform domain, but not both. This paper briefly reviews various existing approximation techniques. Subsequently, we present a new strategy to obtain an approximation fˆ(x) of f(x) in such a way that it is reasonably close to the original function in the domain of the variable x, and exactly preserves some properties of the transformed domain. In this particular case, the properties of the transformed values that are preserved are geometric moments of the original function. The proposed technique has been applied to single-variable functions, two-dimensional planar curves, and two-dimensional images, and the results obtained are demonstrative
Keywords :
computational geometry; function approximation; image processing; method of moments; time-domain analysis; transforms; 2D images; 2D planar curves; digital signal processing; geometric moments; image processing; moment-preserving approximations; piecewise linear approximations; single-variable functions; time/spatial domain; transform domain; Computer science; Discrete cosine transforms; Discrete transforms; Fourier transforms; Image coding; Image processing; Image storage; Pattern recognition; Piecewise linear approximation; Piecewise linear techniques; Polynomials; Signal processing; Signal to noise ratio; Solid modeling;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on