Carlos Wilson Dantas de Almeida

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The Fuzzy Kohonen Clustering Network combines the idea of fuzzy membership values for learning rates. It is a kind of self-organizing fuzzy neural network that can show great superiority in processing the ambiguity and the uncertainty of data sets or images. Symbolic data analysis provides suitable tools for managing aggregated data described by intervals.(More)
This work presents a new multiscale, curvature-based shape representation technique for planar curves. One limitation of the well-known curvature scale space (CSS) method is that it uses only curvature zero-crossings to characterize shapes and thus there is no CSS descriptor for convex shapes. The proposed method, on the other hand, uses(More)
This article presents a hybrid approach for texture­ based image classification using the gray-level co-occurrence matrices (GLCM) and self-organizing map (SOM) methods. The GLCM is a matrix of how often different combinations of pixel brightness values (grey levels) occur in an image. The GLCM matrices extracted from an image database are processed to(More)
A novel hybrid system for shape-based image retrieval using the curvature scale space (CSS) and self-organizing map (SOM) methods is presented. The shape features of images are represented by CSS images extracted from, for example, a large database and they are processed using the PCA technique. These processed CSS images constitutes the training dataset(More)
The recording of interval data has become a common practice in real world applications and nowadays this kind of data is often used to describe objects. In this paper, we introduce a new fuzzy Kohonen clustering network for symbolic interval data (IFKCN). The network combine the idea of fuzzy membership values for learning rates and the algorithm is able to(More)
In a previous work (de Almeida), we presented an approach for shape-based image retrieval using the curvature scale space (CSS) and self-organizing map (SOM) methods. Here, we examine the robustness of the representation with images under variations of rotation. Moreover, the CSS images extracted from a database are processed and represented by median(More)
This article presents a new method for shape description suitable to be used as a solution to the retrieval problem in large image collections. The proposed approach, called Multiscale Symbolic Data Descriptor (MSDD) combines multiscale methods with Symbolic Data Analysis. The contour convexities and concavities at different scale levels are represented(More)
This article presents a hybrid approach for texture-based image classification using the gray-level co-occurrence matrices (GLCM) and a new Fuzzy Kohonen Clustering Network for Symbolic Interval Data (IFKCN). The GLCM matrices extracted from an image database are processed to create the training data set using IFKCN algorithm. The IFKCN organizes and(More)
In a previous work, we presented an approach for shape-based image retrieval using the curvature scale space (CSS) and self-organizing map (SOM) methods. Here, we examine the robustness of the representation with images under different scales. The shape features of images are represented by CSS images extracted from, for example, a large database and(More)