外文翻译-基于emd的红外图像目标检测方法

外文翻译-基于emd的红外图像目标检测方法内容摘要：

alue: m in m in( ) ( ( ) ( ) ) / 2m t e t e t 5) Repeat steps 2 through 4 until the difference in T in successive iterations is smaller than predefined parameter T0. 3 EMD method Basis deposition techniques such as Fourier deposition or wavelet deposition have been used to analyze real world signals. The main shorting of these approaches is that the basis functions are fixed, and do not necessarily match the varying natures of signals. Recently, EMD method has been proposed as a new tool for data analysis. It is a signal processing technique particularly suitable for nonlinear and nonstationary series[6]. This technique performs a time selfadaptive deposition of a plex signal into elementary, almost orthogonal ponents that don‟t overlap in frequency. EMD basics EMD can break down a signal to a series of zero means “Intrinsic Mode Functions” (IMFs) which satisfy two conditions: (1) in the whole data set, the number of extreme and the number of zero crossings must either be equal or differ at most by one。 (2) at any point, the mean value of the envelope defined by the local maxima and the local minima approach zero. These two characteristics are also the criteria for sifting processes and stopping. Each sifting process contains two steps: (1) construct upper and lower envelopes by connecting all maxima or all minima with cubic splines。 (2) subtract the mean of the upper and lower envelopes from the original signal to get a ponent. While the sifting process should usually be applied several times because the ponent created by only one sifting process can hardly satisfy all the requirements of an IMF. Once an IMF has been created, the same procedure is then applied on the residual of the signal to obtain the next IMF. The later an IMF is, the lower its frequency is. The deposition will stop when no more IMFs can be created or the residual is less than a predetermined small value. Given a signal x(t), the effective algorithm of EMD can be summarized as follows [6]. 1) Identify all extremes of x(t)。 2) Interpolate between minima (resp. maxima), ending up with some “envelope” emin(t) (resp. emax(t))。 3) Compute the average m(t)= (emin(t)+ emax(t)) /2。 4) Extract the detail d(t)=x(t)m(t)。 5) Iterate on the residual d(t). By construction, the number of extremes is decreased when going from one residual to the next (thus guaranteeing the plete deposition is achieved in a finite number of steps), and the corresponding spectral supports are expected to decrease accordingly. While modes and residuals can intuitively be given a “spectral” interpretation, it is worth stressing the fact that, in the general case, their high versus lowfrequency discrimination applies only locally and corresponds by no way to a predetermined subband filtering (as, ., in a wavelet transform). Selection of modes much more corresponds to an automatic and adaptive (signaldependent) timevariant filtering. Bidimensional EMD EMD serves as a powerful tool for adaptive multiscale analysis of nonstationary signals. As far as the onedimensional (1D) case is concerned, studies were carried out to show the similarities of EMD with selective filter bank depositions[12]. Its efficiency for signal denoising was also shown in[13]. These interesting aspects of the EMD motivate the extension of this method to bidimensional signals. The basis of EMD (in 1D) is the construction of some intrinsic mode functions (IMFs) that are constructed through a socalled “sifting” process (SP). A 1D SP is an iterative procedure depending on the following four important problems:1) how to define the extremal points of signal。 2) the choice of interpolation method to interpolate those extremal points from the first step。 3) how to define a stopping criterion that ends the procedure。 4) the method of dealing with boundary data of the image. The process of bidimensional EMD is similar to onedimensional EMD, but for bidimensional EMD, those four problems still exist and will be more crucial. Since the first problem was mentioned earlier, assuming that f[m,n] is an MN image, we use the definition of extreme as follows[14]. Definition 4: f[m,n] is a maximum(resp. minimum) if it is larger(resp. lower) than the value of f at the eight nearest neighbors of [m,n]. As far as the interpolation is concerned, several techniques have been proposed, for instance, radial basis functions such as thinplate splines. These methods require the resolution of timeconsuming optimization problems, which make them hard to exploit, especially in a noisy context. Since Delaunay triangulation has good fitting characteristics for scattered or arbitrary data points, then in this paper we first dissect the maximum (resp. minimum) of the image matrix into a series of triangles based on Delaunay triangulation, then interpolate each triangle by the piecewise cubic spline to form upper and lower envelope of the image. We adopt a bidimensional EMD,whose SP is based on Delaunay triangulation, cubic interpolation on triangles and also a fixed number of iterations to build IMFs. The above method is similar to the method in literature [14]. The major advantage of the proposedmethod over existing ones is that it takes into account the geometry while preserves a low putational cost. In the paper, the number of iterations is 3. The boundary handling in bidimensional EMD is more difficult than that in onedimensional EMD, but the general approach applies only to a certain type of one or more of the borders. In addition, there is no theoretical proof to testify which kind of approach is better. In the paper, we deal with boundary issue based on the mirror reflection of image data. The ponents of image based on EMD Though the method based on EMD is similar to wavelet transformation, it can depose the image。