José Miguel Valiente

Performance evaluation of soft color texture descriptors for surface grading using experimental design and logistic regression

This paper presents a novel approach to the question of surface grading, the soft color texture descriptors method. This method is extracted from an extensive evaluation process of several factors based on the use of two well established statistical tools: experimental design and logistic regression. The utility of different combinations of factors is evaluated in regard to the problem of automatic classification of materials such as ceramic tiles that need to be grouped according to homogeneous visual appearance, that is, the surface grading application. The set of factors includes the number of neighbors in the k-NN classifier (several values of k parameter), color space representation schemes (CIE Lab, CIE Luv, RGB, and grayscale), and color texture features (mean, standard deviation, 2nd-5th histogram moments). A factorial experimental design is performed testing all combinations of the above factors on a large image database of ceramic tiles. Accuracy estimates are computed using logistic regression to determine the best combinations of factors. From the point of view of machine learning the overall process conforms a wrapper approach able to select significant design choices (k parameter in k-NN classifier and color space) and carry out a feature selection within the set of color texture features at the same time. Experiments were repeated with alternate color texture schemes from the literature: color histograms and centile-LBP. Comparisons of methods are presented describing both accuracy estimates and runtimes.

Fast surface grading using color statistics in the CIE lab space

In this paper we approach the problem of fast surface grading of flat pieces decorated with random patterns. The proposed method is based on the use of global statistics of color computed in the CIE Lab space. Two other fast methods based on color histograms [1] and Centile-LBP features [8] are introduced for comparison purposes. We used CIE Lab in order to provide accuracy and perceptual approach in color difference computation. Experiments with RGB were also carried out to study CIE Lab reliability. The ground truth was provided through an image database of ceramic tiles. Nevertheless, the approach is suitable to be extended to other random decorated surfaces like marble, granite, wood or textile stuff. The experiments make us to conclude that a simple collection of global statistics of color in the CIE Lab space is powerful enough to well discriminate surface grades. The average success surpasses 95% in most of the tests, improving literature methods and achieving factory compliance.

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.

Multivariate statistical projection methods to perform robust feature extraction and classification in surface grading

We present an innovative way to simultaneously perform feature extraction and classification for the quality-control issue of surface grading by applying two multivariate statistical projection methods: SIMCA and PLS-DA. These tools have been applied to compress the color texture data that describe the visual appearance of surfaces (soft color texture descriptors) and to directly perform classification using statistics and predictions from the projection models. Experiments have been carried out using an extensive ce- ramic images database (VxC TSG) comprised of 14 different mod- els, 42 surface classes, and 960 pieces. A factorial experimental design evaluated all the combinations of several factors affecting the accuracy rate. These factors include the tile model, color repre- sentation scheme (CIE Lab, CIE Luv, and RGB), and compression/ classification approach (SIMCA and PLS-DA). Moreover, a logistic regression model is fitted from the experiments to compute accu- racy estimates and study the effect of the factors on the accuracy rate. Results show that PLS-DA performs better than SIMCA, achieving a mean accuracy rate of 98.95%. These results outper- form those obtained in a previous work where the soft color texture descriptors in combination with the CIE Lab color space and the k-NN classifier achieved an accuracy rate of 97.36%.

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.

Surface grading using soft colour-texture descriptors

This paper presents a new approach to the question of surface grading based on soft colour-texture descriptors and well known classifiers. These descriptors come from global image statistics computed in perceptually uniform colour spaces (CIE Lab or CIE Luv). The method has been extracted and validated using a statistical procedure based on experimental design and logistic regression. The method is not a new theoretical contribution, but we have found and demonstrate that a simple set of global statistics softly describing colour and texture properties, together with well-known classifiers, are powerful enough to meet stringent factory requirements for real-time and performance. These requirements are on-line inspection capability and 95% surface grading accuracy. The approach is also compared with two other methods in the surface grading literature; colour histograms [1] and centile-LBP [8]. This paper is an extension and in-depth development of ideas reported in a previous work [11].

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.

Feature extraction and classifcation in surface grading application using multivariate statistical projection models

In this paper we present an innovative way to simultaneously perform feature extraction and classification for the quality control issue of surface grading by applying two well known multivariate statistical projection tools (SIMCA and PLS-DA). These tools have been applied to compress the color texture data describing the visual appearance of surfaces (soft color texture descriptors) and to directly perform classification using statistics and predictions computed from the extracted projection models. Experiments have been carried out using an extensive image database of ceramic tiles (VxC TSG). This image database is comprised of 14 different models, 42 surface classes and 960 pieces. A factorial experimental design has been carried out to evaluate all the combinations of several factors affecting the accuracy rate. Factors include tile model, color representation scheme (CIE Lab, CIE Luv and RGB) and compression/classification approach (SIMCA and PLS-DA). In addition, a logistic regression model is fitted from the experiments to compute accuracy estimates and study the factors effect. The results show that PLS-DA performs better than SIMCA, achieving a mean accuracy rate of 98.95%. These results outperform those obtained in a previous work where the soft color texture descriptors in combination with the CIE Lab color space and the k-NN classi.er achieved a 97.36% of accuracy.