The traditional edge detection algorithms have certain noise amplificat ion, making there is a big error, so the edge
detection ability is limited.
In analysis of the low-frequency signal of image, wavelet analysis theory can reduce the time resolution; under high time
resolution for high-frequency signal of the image, it can be concerned about the transient characteristics of the signal to
reduce the frequency resolution. Because of the self-adaptive for signal, the wavelet transform can ext ract useful
informat ion from the edge of an image. The wavelet transform is at various scales, wavelet transform of each scale
provides certain edge informat ion, so called mult i-scale edge detection. Multi-scale edge detection is that the original
signal is first polished at different scales, and then detects the mutation of the original signal by the first or second
derivative of the polished signal, and the mutations are edges. The edge detection is equivalent to signal detection in
different frequency bands after wavelet decomposition.
This article is use of this algorithm which takes into account both details and profile of image to detect the mutation of
the signal at different scales, provided necessary edge information for image analysis, target recognition and machine
visual, and achieved good results.