Instead, windows are located from the holes on the wall features. Finally, outline polygons are fitted from feature segments, and combined to a complete polyhedron model. A significant advantage of this approach is that semantic feature types are extracted and linked to the resulting models, so that i) it is possible to get faster visualization by sharing the same texture for same feature type; ii) polygons can be associated with various attributes according to its feature type.Figure 2 shows a building facade model which is reconstructed with the above approach. Most facade features are successfully extracted and modeled. However, if take a close look, it is easy to identify several mistakes from the model. By analyzing more models, two main reasons for the modeling errors are deduced.
They are:Limitations of outline generation method. For example, side wall’s eave can ��attract�� the side boundary edges of the facade, and result in a slight wider polygon in horizontal direction. The almost vertical or horizontal edges are forced to be vertical or horizontal; however, this is not always beneficial.Poor scanning quality. Due to the scanning strategy of stationary laser scanner, complete scanning of a scene seems impossible. There are always some parts which contain very sparse laser points, because of the visibility is poor to all scan stations. Occluded zones without any laser points are also usual in laser point clouds. The lack of reference laser information leads to gaps in the final model. Sometimes these gaps are removed using knowledge, but this is not as accurate as data driven modeling.
Figure 2.A reconstructed building facade model, show
Detection of pathogenic bacteria in food, water, and air has been an important issue for scientists because of its critical impact on public health. Although standard microbiological methods of cell culture and plating are confirmative to identify bacterial strains [1], it often takes several days to complete the processes. In addition, most of conventional methods require intricate instrumentation and cannot be used on-site. Thus, both private and government sectors strongly need biosensors that can detect pathogens in a fast and accurate manner.Pathogen sensors must meet several requirements. First, they should show high sensitivity and a low detection limit.
Since the speed of multiplication of bacteria is very high, even low numbers of bacteria cells (<10 cells) can be a risk to a patient's health [2]. USDA requires zero tolerance of certain strains of bacteria, Brefeldin_A such as E. coli O157:H7, Salmonella, and L. monocytogenes, in food products [3,4]. Second, rapid analysis time is essential. This is especially important to take immediate measures for curing victims of pathogens and restricting the spread of pathogens. Third, simultaneous detection and identification of different strains of bacteria is also critical.