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Found 6 papers, 0 papers with code
Higher-order Segmentation via Multicuts
Multicuts enable to conveniently represent discrete graphical models for unsupervised and supervised image segmentation, in the case of local energy functions that exhibit symmetries.
Towards Efficient and Exact MAP-Inference for Large Scale Discrete Computer Vision Problems via Combinatorial Optimization
Discrete graphical models (also known as discrete Markov random fields) are a major conceptual tool to model the structure of optimization problems in computer vision.
Symmetry-free SDP Relaxations for Affine Subspace Clustering
We consider clustering problems where the goal is to determine an optimal partition of a given point set in Euclidean space in terms of a collection of affine subspaces.
A First Derivative Potts Model for Segmentation and Denoising Using ILP
Unsupervised image segmentation and denoising are two fundamental tasks in image processing.
An ILP Solver for Multi-label MRFs with Connectivity Constraints
The method enforces connectivity priors iteratively by a cutting plane method, and provides feasible solutions with a guarantee on sub-optimality even if we terminate it earlier.
Discrete Potts Model for Generating Superpixels on Noisy Images
Many computer vision applications, such as object recognition and segmentation, increasingly build on superpixels.
