23 research outputs found
A Global Approach for Solving Edge-Matching Puzzles
We consider apictorial edge-matching puzzles, in which the goal is to arrange
a collection of puzzle pieces with colored edges so that the colors match along
the edges of adjacent pieces. We devise an algebraic representation for this
problem and provide conditions under which it exactly characterizes a puzzle.
Using the new representation, we recast the combinatorial, discrete problem of
solving puzzles as a global, polynomial system of equations with continuous
variables. We further propose new algorithms for generating approximate
solutions to the continuous problem by solving a sequence of convex
relaxations
Solving Jigsaw Puzzles with Eroded Boundaries
Jigsaw puzzle solving is an intriguing problem which has been explored in
computer vision for decades. This paper focuses on a specific variant of the
problem - solving puzzles with eroded boundaries. Such erosion makes the
problem extremely difficult, since most existing solvers utilize solely the
information at the boundaries. Nevertheless, this variant is important since
erosion and missing data often occur at the boundaries. The key idea of our
proposed approach is to inpaint the eroded boundaries between puzzle pieces and
later leverage the quality of the inpainted area to classify a pair of pieces
as 'neighbors or not'. An interesting feature of our architecture is that the
same GAN discriminator is used for both inpainting and classification; Training
of the second task is simply a continuation of the training of the first,
beginning from the point it left off. We show that our approach outperforms
other SOTA methodsComment: 8 page
JigsawNet: Shredded Image Reassembly using Convolutional Neural Network and Loop-based Composition
This paper proposes a novel algorithm to reassemble an arbitrarily shredded
image to its original status. Existing reassembly pipelines commonly consist of
a local matching stage and a global compositions stage. In the local stage, a
key challenge in fragment reassembly is to reliably compute and identify
correct pairwise matching, for which most existing algorithms use handcrafted
features, and hence, cannot reliably handle complicated puzzles. We build a
deep convolutional neural network to detect the compatibility of a pairwise
stitching, and use it to prune computed pairwise matches. To improve the
network efficiency and accuracy, we transfer the calculation of CNN to the
stitching region and apply a boost training strategy. In the global composition
stage, we modify the commonly adopted greedy edge selection strategies to two
new loop closure based searching algorithms. Extensive experiments show that
our algorithm significantly outperforms existing methods on solving various
puzzles, especially those challenging ones with many fragment pieces