Tsallis Entropy In Bi-level And Multi-level Image Thresholding

International audienceThe maximum entropy principle has a relevant role in image processing, in particular for thresholding and image segmentation. Different entropic formulations are available to this purpose; one of them is based on the Tsallis non-extensive entropy. Here, we propose a discussion of its use for bi-and multi-level thresholding

Tsallis Entropy In Bi-level And Multi-level Image Thresholding

The maximum entropy principle has a relevant role in image processing, in particular for thresholding and image segmentation. Different entropic formulations are available to this purpose; one of them is based on the Tsallis non-extensive entropy. Here, we propose a discussion of its use for bi-and multi-level thresholding

Fault diagnosis of rolling bearing based on relevance vector machine and kernel principal component analysis

In order to improve the speed and accuracy of rolling bearing fault diagnosis on small samples, a method based on relevance vector machine (RVM) and Kernel Principle Component Analysis (KPCA) is proposed. Firstly, the wavelet packet energy of the vibration signal is extracted with the wavelet packet transform, which is used as fault feature vectors. Secondly, the dimension of feature vectors is reduced in order to weaken the correlation between the features. The important principal components are selected using KPCA as the new feature vectors under the criterion that the cumulative variance is greater than 95 %. Finally, the faults of rolling bearing are diagnosed through combining KPCA with RVM. Simulation experimental indicates the advantages of the presented method. Moreover, the proposed approach is applied to diagnoses rolling bearing fault. The results show that wavelet packet energy can express rolling bearing fault features accurately, KPCA can reduce the dimension of feature vectors effectively and the proposed method has better performance in the speed of fault diagnosis than the method based on support vector machine (SVM), which supplies a strategy of fault diagnosis for rolling bearing. In this paper, the performance of the proposed method is also compared with other diagnostic methods

Wavelet packet energy, Tsallis entropy and statistical parameterization for support vector-based and neural-based classification of mammographic regions

This work develops a support vector and neural-based classification of mammographic regions by applying statistical, wavelet packet energy and Tsallis entropy parameterization. From the first four wavelet packet decomposition levels, four different feature sets were evaluated using two-sample Kolmogorov–Smirnov test (KS-test) and, in one case, principal component analysis (PCA). Feature selection was performed applying a hybrid scheme integrating non-parametric KS-test, correlation analysis, a logistic regression (LR) model and sequential forward selection (SFS). The top selected features (depending on the selected wavelet decomposition level) produced the best classification performances in comparison to other well-known feature selection methods. The classification of the data was carried out using several support vector machine (SVM) schemes and multi-layer perceptron (MLP) neural networks. The new set of features improved significantly the classification performance of mammographic regions using conventional SVMs and MLPs

Wavelet packet energy, Tsallis entropy and statistical parameterization for support vector-based and neural-based classification of mammographic regions

Este trabajo desarrolla un vector de soporte y una clasificación basada en los nervios de las regiones mamográficas mediante la aplicación de parámetros estadísticos de energía de paquetes de ondas y entropía de Tsallis. De los primeros cuatro niveles de descomposición de paquetes de ondículas, se evaluaron cuatro conjuntos de características diferentes utilizando la prueba de Kolmogorov-Smirnov de dos muestras (prueba KS) y, en un caso, el análisis de componentes principales (PCA). La selección de características se realizó aplicando un esquema híbrido que integra una prueba KS no paramétrica, un análisis de correlación, un modelo de regresión logística (LR) y una selección directa secuencial (SFS). Las características principales seleccionadas (según el nivel de descomposición de ondículas seleccionado) produjeron los mejores rendimientos de clasificación en comparación con otros métodos de selección de características bien conocidos. La clasificación de los datos se llevó a cabo utilizando varios esquemas de máquina de vectores de soporte (SVM) y redes neuronales de perceptrón multicapa (MLP). El nuevo conjunto de características mejoró significativamente el rendimiento de clasificación de las regiones mamográficas utilizando SVM y MLP convencionalesThis work develops a support vector and neural-based classification of mammographic regions by applying statistical, wavelet packet energy and Tsallis entropy parameterization. From the first four wavelet packet decomposition levels, four different feature sets were evaluated using two-sample Kolmogorov–Smirnov test (KS-test) and, in one case, principal component analysis (PCA). Feature selection was performed applying a hybrid scheme integrating non-parametric KS-test, correlation analysis, a logistic regression (LR) model and sequential forward selection (SFS). The top selected features (depending on the selected wavelet decomposition level) produced the best classification performances in comparison to other well-known feature selection methods. The classification of the data was carried out using several support vector machine (SVM) schemes and multi-layer perceptron (MLP) neural networks. The new set of features improved significantly the classification performance of mammographic regions using conventional SVMs and MLPs

Digital Image Processing

This book presents several recent advances that are related or fall under the umbrella of 'digital image processing', with the purpose of providing an insight into the possibilities offered by digital image processing algorithms in various fields. The presented mathematical algorithms are accompanied by graphical representations and illustrative examples for an enhanced readability. The chapters are written in a manner that allows even a reader with basic experience and knowledge in the digital image processing field to properly understand the presented algorithms. Concurrently, the structure of the information in this book is such that fellow scientists will be able to use it to push the development of the presented subjects even further