Segmentation of two-phase flow: a free representation for Level Set method with a priori knowledge

TLDR: The promising results indicate that image segmentation by the proposed approach gives good results and it can be used as an efficient method to validate other existing approaches to flow measurement instrumentation.

Abstract: In this paper, an extension of digital images segmentation approach based on active contours, or snakes approach, and an extension of shape model are unified in order to develop a free representation for level set method with a priori knowledge. The proposed method improves the average freely previously trained format, using the method of contour active for Level Set. In this case, there is no restriction to interface evolution as occurs in other approaches that combine active contours and prior knowledge. This approach is used for the correct identification of gas bubble shape in gas–liquid two-phase flow. The main objective of this work is to provide a validation system to support the development of other flow measurements tools based on widely used instrumentation. The promising results indicate that image segmentation by the proposed approach gives good results and it can be used as an efficient method to validate other existing approaches to flow measurement instrumentation.