no code implementations • 20 Mar 2018 • Ruobing Shen, Xiaoyu Chen, Xiangrui Zheng, Gerhard Reinelt
Many computer vision applications, such as object recognition and segmentation, increasingly build on superpixels.
no code implementations • 16 Dec 2017 • Ruobing Shen, Eric Kendinibilir, Ismail Ben Ayed, Andrea Lodi, Andrea Tramontani, Gerhard Reinelt
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.
no code implementations • 21 Sep 2017 • Ruobing Shen, Gerhard Reinelt, Stéphane Canu
Unsupervised image segmentation and denoising are two fundamental tasks in image processing.
no code implementations • 25 Jul 2016 • Francesco Silvestri, Gerhard Reinelt, Christoph Schnörr
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.
no code implementations • 28 May 2013 • Joerg Hendrik Kappes, Markus Speth, Gerhard Reinelt, Christoph Schnoerr
Multicuts enable to conveniently represent discrete graphical models for unsupervised and supervised image segmentation, in the case of local energy functions that exhibit symmetries.
no code implementations • CVPR 2013 • Jorg Hendrik Kappes, Markus Speth, Gerhard Reinelt, Christoph Schnorr
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.