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Michal Shemesh

dblp:22/9990 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2011
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1Graphics, computer vision, multimedia, augmented reality and games · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Image and video processing · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Image and video processing
image reconstruction
0.112011
A fully automated greedy square jigsaw puzzle solver · CVPR 2011
Image and video processing
jigsaw puzzle solving
0.112011
A fully automated greedy square jigsaw puzzle solver · CVPR 2011

Methods — techniques the papers use, named apart from their topics

greedy algorithm · 0.1compatibility metrics · 0.1
YearPublicationVenuePosition
2011 A fully automated greedy square jigsaw puzzle solver
abstract
In the square jigsaw puzzle problem one is required to reconstruct the complete image from a set of non-overlapping, unordered, square puzzle parts. Here we propose a fully automatic solver for this problem, where unlike some previous work, it assumes no clues regarding parts' location and requires no prior knowledge about the original image or its simplified (e.g., lower resolution) versions. To do so, we introduce a greedy solver which combines both informed piece placement and rearrangement of puzzle segments to find the final solution. Among our other contributions are new compatibility metrics which better predict the chances of two given parts to be neighbors, and a novel estimation measure which evaluates the quality of puzzle solutions without the need for ground-truth information. Incorporating these contributions, our approach facilitates solutions that surpass state-of-the-art solvers on puzzles of size larger than ever attempted before.
Dolev Pomeranz, Michal Shemesh, Ohad Ben-Shahar
CVPR2