This paper reports on experiments involving deep underwater explosion (UNDEX) that were conducted in a pressure container. The bubble pulsation behavior due to the deep UNDEX is recorded by a high-speed camera for equivalent depths up to 350 m. The bubble images show that although the shape of the explosive package affects the bubble shape at the initial moment, the bubble easily becomes spherical in shallow water which is 0.8m and 100m depth, but never becomes spherical during the whole first pulsation in deep water which is 200m, 300m and 350m in this paper. Solutions of the Rayleigh–Plesset equation fit well with the experimental data, and the value of the polytropic index γ of the gaseous detonation products changes from 1.25 to 1.3 as the depth is increased. Finally, empirical laws governing the pulsation of a deep-UNDEX bubble are established. The experimental pulsation period and that from the Rayleigh–Plesset equation agree with that obtained empirically, but the maximum radius is smaller than the empirical one. This phenomenon shows that the water depth not only creates a high hydrostatic pressure for the bubble but also changes the energy-release process of a deep UNDEX.
This paper presents a novel method to extract local features, which instead of calculating local extrema computes global maxima in a discretized scale-space representation. To avoid interpolating scales on few data points and to achieve perfect rotation invariance, two essential techniques, increasing the width of kernels in pixel and utilizing disk-shaped convolution templates, are adopted in this method. Since the size of a convolution template is finite and finite templates can introduce computational error into convolution, we sufficiently discuss this problem and work out an upper bound of the computational error. The upper bound is utilized in the method to ensure that all features obtained are computed under a given tolerance. Besides, the technique of relative threshold to determine features is adopted to reinforce the robustness for the scene of changing illumination. Simulations show that this new method attains high performance of repeatability in various situations including scale change, rotation, blur, JPEG compression, illumination change, and even viewpoint change.
This paper presents a new approach to estimate the consensus in a data set. Under the framework of RANSAC, the perturbation on data has not been considered sufficiently. We analysis the computation of homography in RANSAC and find that the variance of its estimation monotonically decreases when the size of sample increases. From this result, we carry out an approach which can suppress the perturbation and estimate the consensus set simultaneously. Different from other consensus estimators based on random sampling methods, our approach builds on the least square method and the order statistics and therefore is an alternative scheme for consensus estimation. Combined with the nearest neighbour-based method, our approach reaches higher matching precision than the plain RANSAC and MSAC, which is shown in our simulations.
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