Detection and Classification of Land Crude Oil Spills Using Color Segmentation and Texture Analysis

Formation and Schema Analysis of Oil Painting Style Based on Texture and Color Texture Features under Few Shot

Texture has strong expressiveness in picture art, and color texture features play an important role in composition. Together with texture, they can convey the artistic connotation of portrait, especially in oil painting. Therefore, in order to make the picture form oil painting style and oil painting schema, we need to study the texture and color texture in combination with the previous oil painting art images. But now, there are few samples of good oil paintings, so it is difficult to study the texture and color texture in oil paintings. Therefore, in order to form a unique artistic style of modern oil painting and promote the development of modern oil painting art, this paper studies the texture and color texture characteristics in the environment of few oil painting works. This paper establishes a model through deep neural network to extract the image incentive and color texture of oil painting art works, which provides guidance for promoting the development of oil painting art. The experiments in this paper show that the depth neural network has high definition for the extraction of texture and color texture of small sample oil painting images, which can reach more than 85%. It has high guiding significance for the research and creation of oil painting art.

A Novel Method Based on Headspace-Ion Mobility Spectrometry for the Detection and Discrimination of Different Petroleum Derived Products in Seawater

The objective of the present study is to develop an optimized method where headspace-ion mobility spectrometry is applied for the detection and discrimination between four petroleum-derived products (PDPs) in water. A Box–Behnken design with a response surface methodology was used, and five variables (incubation temperature, incubation time, agitation, sample volume, and injection volume) with influences on the ion mobility spectrometry (IMS) response were optimized. An IMS detector was used as a multiple sensor device, in which, each drift time acts as a specific sensor. In this way, the total intensity at each drift time is equivalent to multiple sensor signals. According to our results, 2.5 mL of sample incubated for 5 min at 31 °C, agitated at 750 rpm, and with an injection volume of 0.91 mL were the optimal conditions for successful detection and discrimination of the PDPs. The developed method has exhibited good intermediate precision and repeatability with a coefficient of variation lower than 5%, (RSD (Relative Standard Deviation): 2.35% and 3.09%, respectively). Subsequently, the method was applied in the context of the detection and discrimination of petroleum-derived products added to water samples at low concentration levels (2 µL·L⁻¹). Finally, the new method was applied to determine the presence of petroleum-derived products in seawater samples.