A stereo vision based road scene segment and vehicle detection method was proposed in this paper. In the method, First, dynamic programming was used for stereo matching, and then mismatching pixels were removed by leftright-consistency check; Second, V disparity was built by computing the disparity map, and a fast projection based line detection method was used to detect lines in V disparity map, after that, the road surface and vanishing line was extracted, and the intersection point between road and vehicle was computed; Third, the U disparity map was built to detect the horizontal position of vehicle. The experiment results have shown that our method not only can extract road area correctly, but also can detect vehicle position correctly too.

Contamination of soil with heavy metals has become a worldwide environmental problem, and receives great attention. In this study, we aim to investigate soil pollution level affected by an industrial district nearby. The total amount of typical heavy metals in the soils (Hengyang Songmu Industrial Park, Hunan Province, China) was analyzed. In addition, the fraction analysis and laboratory simulation leaching via different pH rainwater was carried out to study the migration and transformation of heavy metals. The main results show that the contents of Cu, Zn, Pb, Cr and Cd in the samples were higher than the soil background values in Hunan Province. The heavy metals forms, analyzed by sequential extraction method, show that the proportion of the unstable form of Cd, Zn and Pb was more than 50%. I geo values indicate that the heavy metal pollution degree of soil sample #5 at the investigated area is recorded in the order of Cd(6.42), Zn(2.28), Cu(1.82), Pb(1.63), and Cr(0.37). Cu, Zn, Pb, Cr and Cd in this area could pose a potential leaching risk to the environment which may affect the food chain and constitute a threat to human health. It would be necessary to take steps to stabilize and monitor the heavy metals in soil.

Stereo matching is one of the most important steps in computer vision systems. Broadly methods of stereo matching can be categorized into 2 types: the local support weight algorithms and global support weight algorithms. Recently adaptive local support weight algorithms have achieved state-of-art performance. However, they are still far from perfect. One of major problems of these local support weight algorithms is that they are computational complex and this complexity increases as the window size increases. In this paper we present a novel stereo matching algorithm based on Bi-Exponential Edge-Preserving Smoother (BEEPS) to make the computation efficient. The computation cost of proposed algorithm is independent of input data, filter parameters, and the degrees of smoothing. Experiments show that our algorithm greatly boost efficiency while preserve similar precision compared to state-of-art methods.