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While more than 310 million Americans self-isolate during the COVID-19 pandemic, technology, at the very least, is helping us work, learn and socialize. Tech is also responsible for keeping us entertained. Whether it's bingeing shows on streaming services, playing video games or downloading ebooks. Sometimes, a high-tech device can be used to create low-tech fun while its user is cooped up indoors. There are several ways this device can be used to keep the kids (and kids at heart) entertained by printing crafts, games and activities.
Valentine's Day is rapidly approaching and you, dear reader, are single. After nearly a year of wafting through the waters of pandemic dating, the wormholes of Tinder, Bumble, Hinge, and all the other mainstay apps -- you may be sick of it. Endless scrolling has its cons. The world is theoretically your oyster, but you're also left to wade through your entire region's population. Your thumbs and your brain are weary from seeing the same userface and the same Office pickup lines over and over.
In this paper we introduce new types of square-piece jigsaw puzzles, where in addition to the unknown location and orientation of each piece, a piece might also need to be flipped. These puzzles, which are associated with a number of real world problems, are considerably harder, from a computational standpoint. Specifically, we present a novel generalized genetic algorithm (GA)-based solver that can handle puzzle pieces of unknown location and orientation (Type 2 puzzles) and (two-sided) puzzle pieces of unknown location, orientation, and face (Type 4 puzzles). To the best of our knowledge, our solver provides a new state-of-the-art, solving previously attempted puzzles faster and far more accurately, handling puzzle sizes that have never been attempted before, and assembling the newly introduced two-sided puzzles automatically and effectively. This paper also presents, among other results, the most extensive set of experimental results, compiled as of yet, on Type 2 puzzles.
This paper introduces the first deep neural network-based estimation metric for the jigsaw puzzle problem. Given two puzzle piece edges, the neural network predicts whether or not they should be adjacent in the correct assembly of the puzzle, using nothing but the pixels of each piece. The proposed metric exhibits an extremely high precision even though no manual feature extraction is performed. When incorporated into an existing puzzle solver, the solution's accuracy increases significantly, achieving thereby a new state-of-the-art standard.