METHOD FOR CLASSIFYING A SIGNAL

Abstract

The present invention relates to a method for classifying a signal (S). An intermediate signal (IS) is decimated in order to obtain a processed signal (PS) as a new intermediate signal (IS). A processed signal (PS) is compared with respect to the signal (S) to be classified to thereby generate comparison data (CompDAT). Based on the comparison data (CompDAT) said signal (S) to be classified is classified, thereby classification data (ClassDAT) are generated.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

The invention will now be explained based on preferred embodiments thereof and by taking reference to the accompanying and schematical figures.

FIG. 1 is a schematical flow chart which elucidates some basic aspects of the present invention according to a preferred embodiment thereof.

FIG. 2 is a graphical representation of an original signal to be classified with an edge and homogeneous areas or signal components.

FIG. 3 is a schematical graphical representation of a processed signal which is derived from the signal of FIG. 2 by filtering and down-sampling by a factor of two.

FIG. 4 is a schematical graphical representation of a gradient which is derived from a signal shown in FIG. 2.

FIG. 5 is a schematical graphical representation of a gradient which is derived from the signal shown in FIG. 3.

FIG. 6 is a schematical graphical representation of the variance of the signal shown in FIG. 2 calculated on the basis of a window length of 20.

FIG. 7 is a schematical graphical representation which shows the variance calculated for the signal shown in FIG. 3 on the basis of a window length of 10.

FIG. 8 is a schematical graphical representation which elucidates the variance calculated from the signal shown in FIG. 6, i.e. by multiplying FIG. 5 by a reducing factor caused by a respective anti-alias filter.

FIG. 9 is a schematical block diagram for an embodiment of the inventive method for classifying a signal and for a respective system.

FIG. 10 is a schematical graphical representation of a signal to be classified comprising an edge and two homogeneous areas or signal components.

FIG. 11 is a schematical graphical representation of a signal which is obtained from the signal of FIG. 10 by filtering and down-sampling by a factor of two.

FIG. 12 is a schematical graphical representation which shows the variance calculated form the signal shown in FIG. 10 based on a window length of 100.

FIG. 13 is a schematical graphical representation elucidating the variances calculated from a decimated signal and using a transfer function of an underlying anti-alias filter based on a window length of 50.

FIG. 14 is a schematical graphical representation elucidating details of the representation shown in FIG. 13, i.e. a slice of the representation of FIG. 13.

Claims

1. Method for classifying a signal, comprising processes of:(a) providing/receiving a signal (S) to be classified as an input signal (InpS),(b) using said input signal (InpS) or a part or parts thereof as an intermediate signal (IS) or as a respective part or respective parts thereof,(c) decimating said intermediate signal (IS) or a part or parts thereof and thereby generating a processed signal (PS) and using said processed signal (PS) as a new intermediate signal (IS),(d) comparing said intermediate signal (IS) or a part or parts thereof with said signal (S) to be classified or with a respective part or with respective parts thereof and thereby generating comparison data (CompDAT) as a comparison result, and(e) classifying said signal (S) to be classified or said part or parts thereof based on said comparison data (CompDAT) and thereby generating classification data (ClassDAT) as a classification result,

2. Method according to claim 1, wherein said process (c) of decimating said intermediate signal (IS) is based on a multi rate signal processing and/or multi resolution signal processing.

3. Method according to any one of the preceding claims, wherein said process (c) of decimating said intermediate signal (IS) comprises sub-processes of:(c1) low pass filtering and/or anti-alias filtering said intermediate signal (IS), and of(c2) down-sampling said intermediate signal (IS),in particular in that given order.

4. Method according to any one of the preceding claims, wherein the process (c) of decimating said intermediate signal (IS) and in particular the respective sub-processes (c1), (c2) are carried outin order to reduce high frequency components, noise components and/or respective variances thereof andin order to keep the useful signal components of said intermediate signal (IS) essentially unchanged or to reduce said useful components of said intermediate signal (IS) only by a comparable smaller amount or by a comparable small amount, or unchanged.

5. Method according to any one of the preceding claims, wherein the processes (d) of comparing and/or (e) of classifying are based on a process of gradient estimation, e.g. on a gradient value before and after decimation processing.

6. Method according to any one of the preceding claims, wherein the process (c) of decimation said intermediate signal (IS) and in particular the respective sub-process (c1) of low pass filtering and/or of anti-alias filtering are based on a windowing process, in particular are based on a Hamming window.

7. Method according to any one of the preceding claims, wherein the processes (c) of decimating said intermediate signal (IS), (d) of comparing said intermediate signal (IS), and/or (e) of classifying said signal (S) are carried out to at least one of a next resolution, scale or rate level and/or iteratively, in particular until a certain iteration stop condition is fulfilled.

8. Method according to any one of the preceding claims, wherein said process (d) of comparing said intermediate signal (IS) with said signal (S) to be classified involves a comparison of respective noise levels, of levels of high frequency components and/or of respective variances thereof.

9. Method according to any one of the preceding claims 7 or 8, wherein an iteration—and in particular a respective iteration stop condition—and/or the processes of (d) of comparing said intermediate signal (IS) with said signal (S) to be classified are based on respective threshold values and/or on respective threshold conditions, in particular in a predefined manner.

10. Method according to any one of the preceding claims, wherein based on the comparison data (CompDAT) and/or as the classification data (ClassDAT) homogeneous areas or signal components are detected and/or are distinguished from other areas or signal components, in particular with respect to the content of noise and/or of high frequency components.

11. Method according to any one of the preceding claims, wherein the process (c) of decimating said intermediate signal (IS) and in particular the sub-process (c1) of low pass filtering and/or of anti-alias filtering said intermediate signal (IS) are pre-estimated based on a transfer function (H) given by said low pass filter and/or by said anti-alias filter which is involved.

12. Method according to claim 11, wherein the respective transfer function (H) of the underlying filter is used in order to define at least one of a change factor, a variance range and a variance tolerance range in order to decide whether an area or signal component of said signal (S) to be classified is dominated by high frequency signal components or noise.

13. Method according to claim 12, wherein an area or a signal component is classified as being dominated by noise if a variance calculated from a decimated intermediate signal (IS) is within a variance tolerance range andwherein otherwise the area or signal component in question is classified as being dominated by high frequency signal components.

14. Method according to any one of the preceding claims, wherein areas or signal components are detected as being homogenous or are distinguished as being homogeneous from other areas or signal components by a process of cascading.

15. Method according to any one of the preceding claims, wherein a tolerance range is introduced into a noise reduction factor.

16. Method according to any one of the preceding claims, wherein, if an area or signal component consists of high frequency signal components only, its noise variance is interpolated from noise variance values which are calculated from areas or signal components in the neighbourhood, and/orwherein in this case a warning message is generated which states that for such a case a reliable noise variance estimation result is not possible.

17. Method according to any one of the preceding claims, which is applied to a signal of the group which consists of 1-dimensional signals, 2-dimensional signals, 3-dimensional signals, e.g. acoustical signals, speech signals, images, sequences of images.

18. System, apparatus, or device for classifying a signal, which is adapted and which comprises means for carrying out a method for classifying a signal according to any one of the preceding claims 1 to 17 and the steps thereof.

19. Computer program product, comprising computer program means which is adapted in order to carry out the method for classifying a signal according to any one of the preceding claims 1 to 17 and the steps thereof when it is carried out on a computer or a digital signal processing means.

20. Computer readable storage medium, comprising a computer program product according to claim 19.