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

Combinational Method for Shredded Document Reconstruction

Background:Shredded document reconstruction can provided necessary information in forensic investigations but is currently time consuming and requires significant human labor. Objective:Over the past decade researchers have been improving automated reconstruction techniques but it is still far from a solved problem. Results:In this paper we propose a combinational method for reconstructing documents that are shredded by hand and by machine. Our proposed method is based on both character identification and feature matching techniques. Conclusion: Practical results of this hybrid approach are excellent. . The preliminary results reported in this paper, which take into account a limited amount of shredded pieces (10-15), demonstrate that proposed approach produces interesting results for the problem of document reconstruction

KAJIAN ALGORITMA UPGRADED ARTIFICIAL BEE COLONY DALAM PENCARIAN SOLUSI SQUARE JIGSAW PUZZLE

Pada penyelesaian square jigsaw puzzle, pencarian solusi untuk setiap potongan puzzle menjadi permasalahan utama. Dalam penyelesaiannya, informasi yang tersedia pada setiap potongan hanya berupa nilai red green blue (RGB). Dengan menggunakan nilai RGB, didapatkan nilai perbedaan antar potongan (fitness) yang menjadi dasar untuk menyelesaikan square jigsaw puzzle. Algoritma yang dapat diterapkan untuk menyelesaikan permasalahan tersebut adalah algoritma Upgraded Artificial Bee Colony (UABC) yang belum pernah menyelesaikan permasalahan citra. Pengujian dilakukan dengan mengukur ketepatan hasil penyelesaian aplikasi, mengetahui pengaruh parameter size population (SP), maximum cycle number (MCN), modification rate for employeed (MRE), dan modification rate for onlooker (MRO) pada hasil ketepatan yang diperoleh. Hasil pengujian menunjukkan algoritma UABC mampu menyelesaikan permasalahan square jigsaw puzzle dengan nilai ketepatan yang baik serta dengan meningkatkan parameter SP, MCN, MRE, dan MRO maka hasil ketepatan yang diperoleh juga semakin baik

algoritmos geneticos basados en criterios para rompecabezas np completos

El objetivo principal de esta investigacion es desarrollar un Sistema Inteligente Robotizado (SIR) resuelva un rompecabezas desconocido en un lapso de tiempo reducido. SIR aplica tecnicas de reconocimiento de patrones junto con Algoritmos Geneticos. Investigando el estado del arte, la naturaleza NP-Completa del problema aparece como el comun denominador. SIR se basa en esas experiencias para aportar una nueva aproximacion al problema. Este enfoque involucra la conversion del puzzle a un modelo de Teoria de Grafos. El estudio de este modelo sumado a la inclusion de una analogia y enfoque de solucion diferentes son los principales aportes de este trabajo. A su vez describe marcos teoricos y practicos y el estado actual del proyecto y el trabajo a futuro

A Novel Hybrid Scheme Using Genetic Algorithms and Deep Learning for the Reconstruction of Portuguese Tile Panels

This paper presents a novel scheme, based on a unique combination of genetic algorithms (GAs) and deep learning (DL), for the automatic reconstruction of Portuguese tile panels, a challenging real-world variant of the jigsaw puzzle problem (JPP) with important national heritage implications. Specifically, we introduce an enhanced GA-based puzzle solver, whose integration with a novel DL-based compatibility measure (DLCM) yields state-of-the-art performance, regarding the above application. Current compatibility measures consider typically (the chromatic information of) edge pixels (between adjacent tiles), and help achieve high accuracy for the synthetic JPP variant. However, such measures exhibit rather poor performance when applied to the Portuguese tile panels, which are susceptible to various real-world effects, e.g., monochromatic panels, non-squared tiles, edge degradation, etc. To overcome such difficulties, we have developed a novel DLCM to extract high-level texture/color statistics from the entire tile information. Integrating this measure with our enhanced GA-based puzzle solver, we have demonstrated, for the first time, how to deal most effectively with large-scale real-world problems, such as the Portuguese tile problem. Specifically, we have achieved 82% accuracy for the reconstruction of Portuguese tile panels with unknown piece rotation and puzzle dimension (compared to merely 3.5% average accuracy achieved by the best method known for solving this problem variant). The proposed method outperforms even human experts in several cases, correcting their mistakes in the manual tile assembly

Measures of Similarity between Qualitative Descriptions of Shape, Colour and Size Applied to Mosaic Assembling

A computational approach for obtaining a similarity measure between qualitative descriptions of shape, colour and size of objects within digital images is presented. According to the definition of the qualitative features, the similarity values determined are based on conceptual neighbourhood diagrams or interval distances. An approximate matching algorithm between object descriptions is defined and applied to tile mosaic assembling and results of previous approaches are improved.This work has been partially supported by Universitat Jaume I (Fons del Pla Estratégic de 2011/2012), by the Zentrale Forschungsförderung der Universität Bremen under the project name “Providing human-understandable qualitative and semantic descriptions”, and by the Spanish Ministry of Science and Innovation under project ARTEMISA (TIN2009-14378-C02-01)

Solving Jigsaw Puzzles By the Graph Connection Laplacian

We propose a novel mathematical framework to address the problem of automatically solving large jigsaw puzzles. This problem assumes a large image, which is cut into equal square pieces that are arbitrarily rotated and shuffled, and asks to recover the original image given the transformed pieces. The main contribution of this work is a method for recovering the rotations of the pieces when both shuffles and rotations are unknown. A major challenge of this procedure is estimating the graph connection Laplacian without the knowledge of shuffles. We guarantee some robustness of the latter estimate to measurement errors. A careful combination of our proposed method for estimating rotations with any existing method for estimating shuffles results in a practical solution for the jigsaw puzzle problem. Numerical experiments demonstrate the competitive accuracy of this solution, its robustness to corruption and its computational advantage for large puzzles