20 research outputs found
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
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
Image Reconstruction from Bag-of-Visual-Words
The objective of this work is to reconstruct an original image from
Bag-of-Visual-Words (BoVW). Image reconstruction from features can be a means
of identifying the characteristics of features. Additionally, it enables us to
generate novel images via features. Although BoVW is the de facto standard
feature for image recognition and retrieval, successful image reconstruction
from BoVW has not been reported yet. What complicates this task is that BoVW
lacks the spatial information for including visual words. As described in this
paper, to estimate an original arrangement, we propose an evaluation function
that incorporates the naturalness of local adjacency and the global position,
with a method to obtain related parameters using an external image database. To
evaluate the performance of our method, we reconstruct images of objects of 101
kinds. Additionally, we apply our method to analyze object classifiers and to
generate novel images via BoVW