ENHANCING MULTISCALE FRACTAL DESCRIPTORS USING FUNCTIONAL DATA ANALYSIS

This work presents a novel approach in order to increase the recognition power of Multiscale Fractal Dimension (MFD) techniques, when applied to image classification. The proposal uses Functional Data Analysis (FDA) with the aim of enhancing the MFD technique precision achieving a more representative descriptors vector, capable of recognizing and characterizing more precisely objects in an image. FDA is applied to signatures extracted by using the Bouligand-Minkowsky MFD technique in the generation of a descriptors vector from them. For the evaluation of the obtained improvement, an experiment using two datasets of objects was carried out. A dataset was used of characters shapes (26 characters of the Latin alphabet) carrying different levels of controlled noise and a dataset of fish images contours. A comparison with the use of the well-known methods of Fourier and wavelets descriptors was performed with the aim of verifying the performance of FDA method. The descriptor vectors were submitted to Linear Discriminant Analysis (LDA) classification method and we compared the correctness rate in the classification process among the descriptors methods. The results demonstrate that FDA overcomes the literature methods (Fourier and wavelets) in the processing of information extracted from the MFD signature. In this way, the proposed method can be considered as an interesting choice for pattern recognition and image classification using fractal analysis.

A novel MAP-MRF approach for multispectral image contextual classification using combination of suboptimal iterative algorithms

In this paper we present a novel approach for multispectral image contextual classification by combining iterative combinatorial optimization algorithms. The pixel-wise decision rule is defined using a Bayesian approach to combine two MRF models: a Gaussian Markov Random Field (GMRF) for the observations (likelihood) and a Potts model for the a priori knowledge, to regularize the solution in the presence of noisy data. Hence, the classification problem is stated according to a Maximum a Posteriori (MAP) framework. In order to approximate the MAP solution we apply several combinatorial optimization methods using multiple simultaneous initializations, making the solution less sensitive to the initial conditions and reducing both computational cost and time in comparison to Simulated Annealing, often unfeasible in many real image processing applications. Markov Random Field model parameters are estimated by Maximum Pseudo-Likelihood (MPL) approach, avoiding manual adjustments in the choice of the regularization parameters. Asymptotic evaluations assess the accuracy of the proposed parameter estimation procedure. To test and evaluate the proposed classification method, we adopt metrics for quantitative performance assessment (Cohen`s Kappa coefficient), allowing a robust and accurate statistical analysis. The obtained results clearly show that combining sub-optimal contextual algorithms significantly improves the classification performance, indicating the effectiveness of the proposed methodology. (C) 2010 Elsevier B.V. All rights reserved.

Application of chemometric methods in the evaluation of chemical and spectroscopic data on organic matter from Oxisols in sewage sludge applications

Chemometric methods can contribute to soil research by permitting the extraction of more information from the data. The aim of this work was to use Principal Component Analysis to evaluate data obtained through chemical and spectroscopic methods on the changes in the humification process of soil organic matter from two tropical soils after sewage sludge application. In this case, humic acids extracted from Typic Eutrorthox and Typic Haplorthox soils with and without sewage sludge application for 7 consecutive years were studied. The results obtained for all of the samples and methods showed two clusters: samples extracted from the two soil types. These expected results indicated the textural difference between the two soils was more significant than the differences between treatments (control and sewage sludge application) or between depths. In this case, an individual chemometric treatment was made for each type of soil. It was noted that the characterization of the humic acids extracted from soils with and without sewage sludge application after 7 consecutive years using several methods supplies important results about changes in the humification degree of soil organic matter, These important result obtained by Principal Component Analysis justify further research using these methods to characterize the changes in the humic acids extracted from sewage sludge-amended soils. (C) 2009 Elsevier B.V. All rights reserved.