Visual reconstruction
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Experimental Comparison of Discrete and Continuous Shape Optimization Methods
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Convex Multi-class Image Labeling by Simplex-Constrained Total Variation
SSVM '09 Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision
A First-Order Primal-Dual Algorithm for Convex Problems with Applications to Imaging
Journal of Mathematical Imaging and Vision
Fast Approximate Energy Minimization with Label Costs
International Journal of Computer Vision
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We propose an algorithm for automatizing the task of "Tidying up Art" introduced by the comedian Wehrli [1]. Driven by a strong sense of order and tidyness, Wehrli systematically dissects famous artworks into their constituents and rearranges them according to certain ordering principles. The proposed algorithmic solution to this problem builds up on a number of recent advances in image segmentation and grouping. It has two important advantages: Firstly, the computerized tidying up of art is substantially faster than manual labor requiring only a few seconds on state-of-the-art GPUs compared to many hours of manual labor. Secondly, the computed part decomposition and reordering is fully reproducible. In particular, the arrangement of parts is determined based on mathematically transparent criteria rather than the invariably subjective and irreproducible human sense of order.