This paper describes a computer vision system which can assemble canonical jigsaw puzzles. The most novel aspect of this system is that the methodology presented here derives a new set of critical points which define a feature which can be used in matching partial boundaries (or contours) of planar regions. This global feature, called an Isthmus, can be efficiently and reliably computed from th...

We demonstrate a novel strategy of self-assembly to scale up origami structures in two-dimensional (2D) space using multiple origami structures, named "2D DNA jigsaw pieces", with a specially designed shape. For execution of 2D self-assembly along the helical axis (horizontal direction), sequence-programmed tenon and mortise were introduced to promote selective connections via π-stacking intera...

For this project, I implemented the genetic algorithm-based jigsaw puzzle solver described in “A Genetic Algorithm-Based Solver for Very Large Jigsaw Puzzles” by Sholomon et al [1]. This method involves producing a “child” solution from two “parent” solutions by detecting, extracting, and combining correctly assembled puzzle pieces. The solver developed by the paper’s team is able to accurately...

Water-fat separation in magnetic resonance imaging (MRI) is of great clinical importance, and the key to uniform water-fat separation lies in field map estimation. This work deals with three-point field map estimation, in which water and fat are modelled as two single-peak spectral lines, and field inhomogeneities shift the spectrum by an unknown amount. Due to the simplified spectrum modelling...

this study aims at investigating the effect of using cooperative learning (jigsaw2) and traditional learning on the reading comprehension achievement. in the present study, experimental research model consisting of pre-test, post-test with a control group was applied. the sample of the study consisted of 64 participants in grade three in bonab high schools. to determine the effect of cooperativ...

The success of Vision Transformer (ViT) in various computer vision tasks has promoted the ever-increasing prevalence this convolution-free network. fact that ViT works on image patches makes it potentially relevant to problem jigsaw puzzle solving, which is a classical self-supervised task aiming at reordering shuffled sequential back their original form. Solving been demonstrated be helpful fo...