Automated reconstruction of building objects from aerial images is a complex problem due to the diversity of buildings as well as noise and low contrast of images, which are the results of distant photography, atmospheric effects and poor illumination. In this paper, a semi-automated approach to the reconstruction of parametric building models from aerial images is presented, which works with line segments extracted from the image. The model is selected interactively from a library of parametric models. A perceptual grouping technique is used to select the most significant image lines in terms of relations such as proximity and parallelism. Model lines are searched for the same relations as in the grouped image lines, and the corresponding lines undergo a matching procedure, which determines whether or not a match can be found between the given model and image lines. An experiment with aerial images of flat-roof and gable-roof buildings is shown and its results indicate the robustness and efficiency of the proposed approach. 相似文献
In this study, a capillary barrier system was designed and tested for an arid land environment. To simulate arid land conditions
of high temperature and sub-irrigation systems, the barrier was subjected to thermal and hydraulic gradients in opposite directions;
to test the barrier system under these severe conditions, an experimental apparatus was designed and fabricated. The multilayer
capillary barrier consisted of three layers made of silica sand, a mixture of sand and bentonite in equal portions, and a
mixture of clay (25%) and aggregate (75%). Several one dimensional coupled heat and moisture tests were performed. Temperature
variations along the thickness of the barrier were recorded as a function of time, and at the end of each test, the barrier
was sliced into small sections, for the determination of volumetric water content as a function of distance from the heat
source. The experimental results were discussed in view of the barrier's intended purpose of its ability to store moisture
for long time durations.
Coupled heat and moisture flow equations were developed and solved numerically via a finite difference method. Diffusivity
parameters were calculated by using experimental results, a numerical model, and Powell's conjugate directions method of nonlinear
optimization. The model was calibrated and the results were discussed. Good agreement between calculated and experimental
results was obtained.
This revised version was published online in July 2006 with corrections to the Cover Date. 相似文献
Rock mass classification is analogous to multi-feature pattern recognition problem. The objective is to assign a rock mass to one of the pre-defined classes using a given set of criteria. This process involves a number of subjective uncertainties stemming from: (a) qualitative (linguistic) criteria; (b) sharp class boundaries; (c) fixed rating (or weight) scales; and (d) variable input reliability. Fuzzy set theory enables a soft approach to account for these uncertainties by allowing the expert to participate in this process in several ways. Hence, this study was designed to investigate the earlier fuzzy rock mass classification attempts and to devise improved methodologies to utilize the theory more accurately and efficiently. As in the earlier studies, the Rock Mass Rating (RMR) system was adopted as a reference conventional classification system because of its simple linear aggregation.
The proposed classification approach is based on the concept of partial fuzzy sets representing the variable importance or recognition power of each criterion in the universal domain of rock mass quality. The method enables one to evaluate rock mass quality using any set of criteria, and it is easy to implement. To reduce uncertainties due to project- and lithology-dependent variations, partial membership functions were formulated considering shallow (<200 m) tunneling in granitic rock masses. This facilitated a detailed expression of the variations in the classification power of each criterion along the corresponding universal domains. The binary relationship tables generated using these functions were processed not to derive a single class but rather to plot criterion contribution trends (stacked area graphs) and belief surface contours, which proved to be very satisfactory in difficult decision situations. Four input scenarios were selected to demonstrate the efficiency of the proposed approach in different situations and with reference to the earlier approaches. 相似文献