Bootstrap sampling applied to image analysis

We present the bootstrap sampling techniques applied to some pattern recognition algorithms. Two important procedures in image analysis are tested: a statistical segmentation based on expectation-maximisation (EM) family algorithms and two methods of invariant features extraction for gray level images. In the first case, the results we obtain show that the bootstrap sample selection method gives better results than the classical one both in the quality of the segmented image and the computing time. In the second case, the computation of the moment invariants (MI) and the analytical Fourier Mellin transform (AFMT) by the bootstrap approach using the Monte Carlo approximations are implemented. We note that this approach gives a stable approximation and reduces considerably the computing time, since we select only a small representative sample from the image. These algorithms are applied to natural image (medical image)