An energy functional model by gradient vector-driven active contour for local fitted image segmentation
Year: 2019
Type: conference-paper
Abstract: To design a gradient vector-driven active contour \nlocal fitted image segmentation model based on information \nentropy for analyzing the construction of the active contour \nmodel by the variational and level set methods for validating \nthe proposed model theoretically and simulation experiments. Firstly, several important active contour models \nbased on image boundary features are introduced, and the existing problems are analyzed in depth, and the causes of the problems are pointed out. Next, the non-conservative behavior of the gradient vector flow field is studied in depth, and an important conclusion about the flow field divergence of the gradient vector is obtained in the local fitted image segmentation model. On this basis, a new energy functional is constructed to measure the flux of the gradient vector flow field through the active curve, and transform the image segmentation problem into the minimum value of the energy functional. Finally, a new active contour model is constructed \nusing the gradient flow of the above energy functional.
Authors: Jing Wang, Tao Hai, Kabir M. Nomani
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Award: G3407739236
OpenAlex ID: W3021696404