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A Binary Characterization Method for Shape Convexity and Applications
This paper focuses on characterization methods for convex objects and applications in image processing.
Convex Shape Prior for Deep Neural Convolution Network based Eye Fundus Images Segmentation
This method is flexible and it can handle multiple objects and allow some of the objects to be convex.
A level set representation method for N-dimensional convex shape and applications
We apply this new method to two applications: object segmentation with convexity prior and convex hull problem (especially with outliers).
Convex Shape Representation with Binary Labels for Image Segmentation: Models and Fast Algorithms
We present a novel and effective binary representation for convex shapes.
Convex Shape Prior for Multi-Object Segmentation Using a Single Level Set Function
It is a difficult task to have representations for convex shapes with good and fast numerical solutions.
Convex hull algorithms based on some variational models
In this model, the convex hull is characterized by the zero sublevel-set of a convex level set function, which is non-positive at every given point.
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