Convex Shape Representation with Binary Labels for Image Segmentation: Models and Fast Algorithms
We present a novel and effective binary representation for convex shapes. We show the equivalence between the shape convexity and some properties of the associated indicator function. The proposed method has two advantages. Firstly, the representation is based on a simple inequality constraint on the binary function rather than the definition of convex shapes, which allows us to obtain efficient algorithms for various applications with convexity prior. Secondly, this method is independent of the dimension of the concerned shape. In order to show the effectiveness of the proposed representation approach, we incorporate it with a probability based model for object segmentation with convexity prior. Efficient algorithms are given to solve the proposed models using Lagrange multiplier methods and linear approximations. Various experiments are given to show the superiority of the proposed methods.
Code (0)
등록된 구현이 없습니다.
Tasks
Image SegmentationSemantic SegmentationSimilar Papers 제목 키워드 기반
Convex Shape Prior for Deep Neural Convolution Network based Eye Fundus Images Segmentation
Convex Shapes (CS) are common priors for optic disc and cup segmentation in eye fundus images. It is important to design proper techniques to represent convex shapes. So far, it is still a problem to guarantee that the o…
Image SegmentationSegmentationSemantic SegmentationA Binary Characterization Method for Shape Convexity and Applications
Convexity prior is one of the main cue for human vision and shape completion with important applications in image processing, computer vision. This paper focuses on characterization methods for convex objects and applica…
Image SegmentationSegmentationSemantic SegmentationA reconstruction method for binary limited-data tomography using a dictionary-based sparse shape recovery
Binary tomography is concerned with reconstructing a binary image from a very small number or other limited CT projection data. This problem itself not only possesses several medical imaging applications but also can be …
Image ReconstructionObjectShape Complexes in Continuous Max-Flow Hierarchical Multi-Labeling Problems
Although topological considerations amongst multiple labels have been previously investigated in the context of continuous max-flow image segmentation, similar investigations have yet to be made about shape consideration…
Image SegmentationSegmentationSemantic SegmentationLearning Mesh Representations via Binary Space Partitioning Tree Networks
Polygonal meshes are ubiquitous, but have only played a relatively minor role in the deep learning revolution. State-of-the-art neural generative models for 3D shapes learn implicit functions and generate meshes via expe…
Decoder