Wall Painting Reconstruction Using a Genetic Algorithm

Abstract

Global reconstruction of two-dimensional wall paintings (frescoes) from fragments is an important problem for many archaeological sites. The goal is to find the global position and rotation for each fragment so that all fragments jointly “reconstruct” the original surface (i.e., solve the puzzle). Manual fragment placement is difficult and time-consuming, especially when fragments are irregularly shaped and uncolored. Systems have been proposed to first acquire 3D surface scans of the fragments and then use computer algorithms to solve the reconstruction problem. These systems work well for small test cases and for puzzles with distinctive features, but fail for larger reconstructions of real wall paintings with eroded and missing fragments due to the complexity of the reconstruction search space. We address the search problem with an unsupervised genetic algorithm: we evolve a pool of partial reconstructions that grow through recombination and selection over the course of generations. We introduce a novel algorithm for combining partial reconstructions that is robust to noise and outliers, and we provide a new selection procedure that balances fitness and diversity in the population. In experiments with a benchmark dataset, our algorithm is able to achieve larger and more accurate global reconstructions than previous automatic algorithms.

RETRACTED ARTICLE: Innovative design of wall painting pattern based on microprocessor system and evolutionary computer technology

“…He introduced a novel algorithm to combine partial reconstructions that are robust to noise and outliers, and provided a new selection procedure that balances overall adaptability and diversity. Although his algorithm can achieve a larger and more accurate global reconstruction, the innovation of the research is not prominent enough [3]. Piovesan R studied mural materials from two Roman archaeological sites in Israel (the ancient province of Aidabia, later called Palestine in Syria).…”

Application of evolutionary and swarm optimization in computer vision: a literature survey

“…Wall painting reconstruction is similar to but more complex than jigsaw puzzles, it is not limited by a rectangular shape and can be eroded and lose some of its fragments. Sizikova and Funkhouser [114] proposed solving a wall painting reconstruction using a modified GA, modifying the selection in two steps, including fragment-and binary-based selection, and premodifying the crossover in two categories, a crossover by fragmentation and a crossover by matching. The GA framework starts with one or two fragments, grows to optimize the orientation and translation of the merges, and ends based on a set number of iterations or the completion of all fragments.…”

“…First, due to the variety of algorithms, the optimal choice of algorithm for a particular problem remains an open question. Also, the tuning of hyperparameters and the combination of appropriate operators require human experience [30,60,87,114]. Second, many methods include timeconsuming processes especially with expensive fitness function, which makes real-time applications difficult to implement [32,51,57,106].…”

Developing a Reassembling Algorithm for Broken Objects

“…A fragment reassembling method based on contour lines obtains the contour lines of the fragment by boundary extraction, and determines whether it should be reassembled according to the matching degree of contour lines [9] [10] . The methods of this category are mostly used in the matching of 2D images, or the cases with very thinner fragment fracture surface, such as fresco matching [11] [12] and ceramics matching [13] . They are not applicable to the fragments with inconspicuous outlines and blurred boundaries.…”