COMPARATIVE STUDY OF CONTOUR FITTING METHODS IN SPECKLED IMAGES

摘要

Images obtained with the use of coherent illumination are affected by a noise called speckle, which is inherent to this type of imaging systems. In this work, speckled data have been statistically treated with a multiplicative model using the family of G distributions. One of the parameters of these distributions can be used to characterize the different degrees of roughness found in speckled data. We used this information to find boundaries between different regions within the image. Two different region contour detection methods for speckled imagery, are presented and compared. The first one maximizes a likelihood function over the speckled data and the second one uses anisotropic diffusion over roughness estimates. To represent detected contours, the B-Spline curve representation is used. In order to compare the behaviour of the two methods we performed a Monte Carlo experience. It consisted of the generation of a set of test images with a randomly shaped region, which is considered in the literature as a difficult contour to fit. Then, the mean square error was calculated for each test image, for both methods.