Edge detection and automatic threshold based on wavelet transform in the VPPAW keyhole image processing

Abstract

In order to realize the feedback control for variable polarity plasma arc weld (VPPA W) formation in the weld process, the geometrical sizes of the keyhole image must be extracted. " t h the properties of multiscale edge through the wavelet theory, the edge points were detected by getting the maximum modulus of the gradient vector in the direction towards which the gradient vector points in the image plane. At coarse scales, the local maxima of modules have different positions and only detected the sharp edge. Atfine scale, there are many maxima created by the image noise. We must integrate this multiscale information to look for the best scale where the edges are well discriminated from noises. At last, A new method of peak analysis for threshold selection is provided. It is based on the wavelet transform which provides a multiscale analysis of the information of the histogram. Many experiments show these ways are effective for the keyhole image to get the geometry parameters of the keyhole in the real-time image processing.