José María Gomis

Structural description of textile and tile pattern designs using image processing

Cataloguing pattern and tiling designs using their geometrical features is an old research topic, whose main goal is the synthesis of new designs. However, little effort have been made to approach the inverse problem, this is the analysis of a design using image processing techniques. A set of structural descriptors for automatically classifying designs of textile and tile fabric is proposed. Graphic descriptors as parallelogram fundamental, design cluster, design symmetry axes etc., are properly re-defined in a new framework that, using the theory of symmetry groups, tries to describe the structure of a pattern design. We describe the sequence of operations introduced for the analysis and extraction of these structural descriptors and the methodology used in each stage, devoting special attention to the techniques used in the image segmentation, object extraction, and clustering stages. Experimental results with textile patrimony images and tile museum images are also included.

Analysis tool for cataloguing textile and tile pattern designs

This paper presents an analysis tool, part of an integrated management system for pattern design in the textile and tile industries, that provides automatic cataloguing capabilities based on the application of the scientific theory of symmetry groups. To do this, a process of analysis is performed which starts from an initial digitized image of the decorative element, which in turn is subjected to a number of segmentation and labelling operators that allow to detect the objects present in the image. These objects are vectorized, compared, and their isometries obtained; subsequently they are grouped and the isometries of the groups of objects detected. Finally, a composition analysis is carried out that, on the basis of the repetitions and symmetry axes existing in the design, provides the fundamental parallelogram and the plane symmetry group. This paper summarizes the results obtained from processing 95 pattern designs using the analysis tool developed by the authors.

A computational model for pattern and tile designs classification using plane symmetry groups

This paper presents a computational model for pattern analysis and classification using symmetry group theory. The model was designed to be part of an integrated management system for pattern design cataloguing and retrieval in the textile and tile industries. While another reference model [6], uses intensive image processing operations, our model is oriented to the use of graphic entities. The model starts by detecting the objects present in the initial digitized image. These objects are then transformed into Bezier curves and grouped to form motifs. The objects and motifs are compared and their symmetries are computed. Motif repetition in the pattern provides the fundamental parallelogram, the deflexion axes and rotation centres that allow us to classify the pattern according its plane symmetry group. This paper summarizes the results obtained from processing 22 pattern designs from Islamic mosaics in the Alcazar of Seville.

A new method to analyse mosaics based on Symmetry Group theory applied to Islamic Geometric Patterns

New method to analyse mosaics based on the mathematical principles of Symmetry Groups.The method includes a higher level of knowledge based on objects.Extraction of objects and their main features of patterns with a Wallpaper Group (WG).Classification of objects according to their shape and obtaining their isometries.The extraction of the WG of the pattern using the relationships between objects. This article presents a new method for analysing mosaics based on the mathematical principles of Symmetry Groups. This method has been developed to get the understanding present in patterns by extracting the objects that form them, their lattice, and the Wallpaper Group. The main novelty of this method resides in the creation of a higher level of knowledge based on objects, which makes it possible to classify the objects, to extract their main features (Point Group, principal axes, etc.), and the relationships between them. In order to validate the method, several tests were carried out on a set of Islamic Geometric Patterns from different sources, for which the Wallpaper Group has been successfully obtained in 85% of the cases. This method can be applied to any kind of pattern that presents a Wallpaper Group. Possible applications of this computational method include pattern classification, cataloguing of ceramic coatings, creating databases of decorative patterns, creating pattern designs, pattern comparison between different cultures, tile cataloguing, and so on.

Intregated system and methodology for supporting textile and tile pattern design

This paper presents a methodology for graphic pattern design and redesign applicable to tile and textile patterns. The methodology is based on a Design Information System integrated with two computer tools. One for the structural and morphologic analysis of graphic designs that uses as reference framework the scientific theory of symmetry groups, and a second interactive tool for the structural edition of patterns that exploits all the capabilities provided by the manipulation of the minimum region and fundamental parallelogram of pattern designs. We present some application examples to generate new designs as modifications from designs acquired from historic sources. The methodology and tools are oriented to bridge the gap between the historical and artistic production of graphic design and the tile and textile industries.

Methodology for graphic redesign applied to textile and tile pattern design

One of the difficulties of using Artificial Neural Networks (ANNs) to estimate atmospheric temperature is the large number of potential input variables available. In this study, four different feature extraction methods were used to reduce the input vector to train four networks to estimate temperature at different atmospheric levels. The four techniques used were: genetic algorithms (GA), coefficient of determination (CoD), mutual information (MI) and simple neural analysis (SNA). The results demonstrate that of the four methods used for this data set, mutual information and simple neural analysis can generate networks that have a smaller input parameter set, while still maintaining a high degree of accuracy.