Spatial regularity amidst a seemingly chaotic image is often meaningful. Many papers in computational geometry are concerned with detecting some type of regularity via exact solutions to problems in geometric pattern recognition. However, real-world applications often have data that is approximate, and may rely on calculations that are approximate. Thus, it is useful to develop solutions that have an error tolerance.
A solution has recently been presented by Robins et al. [Inform. Process. Lett. 69 (1999) 189–195] to the problem of finding all maximal subsets of an input set in the Euclidean plane
that are approximately equally-spaced and approximately collinear. This is a problem that arises in computer vision, military applications, and other areas. The algorithm of Robins et al. is different in several important respects from the optimal algorithm given by Kahng and Robins [Patter Recognition Lett. 12 (1991) 757–764] for the exact version of the problem. The algorithm of Robins et al. seems inherently sequential and runs in O(n5/2) time, where n is the size of the input set. In this paper, we give parallel solutions to this problem. 相似文献
An approach which combines particle swarm optimization and support vector machine (PSO–SVM) is proposed to forecast large-scale goaf instability (LSGI). Firstly, influencing factors of goaf safety are analyzed, and following parameters were selected as evaluation indexes in the LSGI: uniaxial compressive strength (UCS) of rock, elastic modulus (E) of rock, rock quality designation (RQD), area ration of pillar (Sp), the ratio of width to height of the pillar (w/h), depth of ore body (H), volume of goaf (V), dip of ore body (α) and area of goaf (Sg). Then LSGI forecasting model by PSO-SVM was established according to the influencing factors. The performance of hybrid model (PSO + SVM = PSO–SVM) has been compared with the grid search method of support vector machine (GSM–SVM) model. The actual data of 40 goafs are applied to research the forecasting ability of the proposed method, and two cases of underground mine are also validated by the proposed model. The results indicated that the heuristic algorithm of PSO can speed up the SVM parameter optimization search, and the predictive ability of the PSO–SVM model with the RBF kernel function is acceptable and robust, which might hold a high potential to become a useful tool in goaf risky prediction research. 相似文献