A neutron-spin experimental test of the quantum Zeno effect (QZE) is discussed from a practical point of view, when the nonideal efficiency of the magnetic mirrors, used for filtering the spin state, is taken into account. In the idealized case the number N of (ideal) mirrors can be indefinitely increased, yielding an increasingly better QZE. By contrast, in a practical situation with imperfect mirrors, there is an optimal number of mirrors, Nopt, at which the QZE becomes maximum: more frequent measurements would deteriorate the performance. However, a quantitative analysis shows that a good experimental test of the QZE is still feasible. These conclusions are of general validity: in a realistic experiment, the presence of losses and imperfections leads to an optimal frequency Nopt, which is in general finite. One should not increase N beyond Nopt. A convenient formula for Nopt, valid in a broad framework, is derived as a function of the parameters characterizing the experimental setup.

Higher-order active contours or snakes show much promise for the extraction of complex objects from noisy imagery. These models provide an elegant mathematical framework for specifying the desired properties of target objects through energy functionals that can be minimized with standard optimization techniques. However, techniques to allow quadratic snakes to change topology during segmentation have not been fully exploited. Additionally, external forces for improving convergence of quadratic snakes have similarly yet to be explored. In this article, we propose a model that allows multiple quadratic snakes to split, merge, and disappear. Although the separate components of our approach have been introduced elsewhere by Cohen (1991), Xu and Prince (1997), and Rochery et al. (2006), this article is the first comprehensive empirical study of their performance on real-world complex network extraction tasks. We analyze the applicability of the model to road extraction from satellite images that vary in complexity from simple networks to large networks with multiple loops. We also analyze the effects of external forces enhanced by oriented filtering, gradient vector flow fields, and Canny edge detection. In a series of experiments, we found that the multiple cooperating quadratic snake model performs well on complex, noisy images. Our experiments also establish a performance improvement when the proposed quadratic model is coupled with the Canny-based gradient vector flow technique.

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