Just to let you know, if you buy something featured here, Mashable might earn an affiliate commission. There are two types of people in this world: puzzle lovers, and people who are wrong. However, if you are an unfortunate member of the latter group, the good news is there's a way for you to redeem yourself once and for all: by giving puzzles another chance. Check out the 1000 Vibrating Colors Puzzle from Clemens Habicht's Colour Puzzles. The color puzzles come in three variations, including two that don't involve optical illusions, but it's the incredible vibrating colors one you'll want to check out.
Just to let you know, if you buy something featured here, Mashable might earn an affiliate commission. For some, jigsaw puzzles are a slow, relaxing pastime that exercise the creative and logic centers of the mind. If you're one of the former, it's time you experienced Clemens Habicht's 1,000 Colours. This is no ordinary jigsaw puzzle -- because of the subtle changes in color, it's a true test of patience, process, and attention to detail that's not for the faint of heart (or the faint of sight). You won't have the luxury of a real-world image to focus on while you try to match the picture on the box to the jumble of pieces in front of you.
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.