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Most of the traditional image feature point extraction and matching methods are based on a series of light properties of images. These light properties easily conflict with the distinguishability of the image features. The traditional light imaging methods focus only on a fixed depth of the target scene, and subjects at other depths are often easily blurred. This makes the traditional image feature point extraction and matching methods suffer from a low accuracy and a poor robustness. Therefore, in this paper, a light field camera is used as a sensor to acquire image data and to generate a full-focus image with the help of the rich depth information inherent in the original image of the light field. The traditional ORB feature point extraction and matching algorithm is enhanced with the goal of improving the number and accuracy of the feature point extraction for the light field full-focus images. The results show that the improved ORB algorithm extracts not only most of the features in the target scene but also covers the edge part of the image to a greater extent and produces extracted feature points which are evenly distributed for the light field full-focus image. Moreover, the extracted feature points are not repeated in a large number in a certain part of the image, eliminating the aggregation phenomenon that exists in traditional ORB algorithms. [ABSTRACT FROM AUTHOR]

Emergency response mechanisms were activated throughout China during the COVID-19 outbreak. It is different from the temporary, partial, and limited pollution control measures taken to ensure the regional environmental quality during several important events such as the 2008 Beijing Olympic Games and the 2014 Asia-Pacific Economic Cooperation (APEC). During the COVID-19 epidemic period, extensive movement of people and almost all unnecessary industrial production (necessary industrial production refers to the production of food, epidemic prevention materials, etc.) have been severely restricted, so transportation and industrial production have been greatly reduced. This is a rare extreme emission reduction scenario that presents a unique opportunity for atmospheric research. In this study, based on hourly mass concentration data of NO2 and SO2 from atmospheric monitoring sites in the Beijing-Tianjin-Hebei (BTH) region during the COVID-19 epidemic period, the changes in transportation and industrial production in the region, data statistics, and spatial analysis were used to analyze the pollution changes and their causes. The results indicate that the NO2 and SO2 concentrations in the BTH region decreased significantly during the epidemic period. The spatial distribution pattern of NO2 pollution in the BTH region was "high in the southeast and low in the northwest," and SO2 pollution in the BTH region was high in the southern and eastern parts of Hebei. The initiation of emergency response level 1 had an obvious effect on reducing NO2 and SO2 pollution in the region, while the impact of emergency response level 2 and below was limited. Compared with the single traffic control, the comprehensive control, similar to the emergency response, had a better effect on reducing NO2 pollution in the region. The control of major large cities in the region also had a certain effect on alleviating NO2 and SO2 pollution in the entire region. Moreover, for activities under short-term control, it is particularly important to guard against the "retaliatory growth" after the control is lifted. By reducing and controlling some polluting industries in industrial production, the degree of NO2 and SO2 pollution in the region can be effectively reduced. The manufacturing industry of chemical raw materials and the chemical products and non-metallic mineral products industry made a great contribution to the change in industrial source pollution emissions in the BTH region during the COVID-19 epidemic. Road traffic emissions remained an important source of NO2 emissions in the BTH region during this period. NO2 emission reduction can be effectively achieved by controlling road traffic and transportation. [ABSTRACT FROM AUTHOR]