This paper introduces the Inverted Hierarchical Neuro-Fuzzy BSP System (HNFB 1 ), a new neuro-fuzzy model that has been specifically created for record classification and rule extraction in databases. The HNFB 1 is based on the Hierarchical Neuro-Fuzzy Binary Space Partitioning Model (HNFB), which embodies a recursive partitioning of the input space, is able to automatically generate its own structure, and allows a greater number of inputs. The new HNFB 1 allows the extraction of knowledge in the form of interpretable fuzzy rules expressed by the following: If is and is , then input pattern belongs to class . For the process of rule extraction in the HNFB 1 model, two fuzzy evaluation measures were defined: 1) fuzzy accuracy and 2) fuzzy coverage. The HNFB 1 has been evaluated with different benchmark databases for the classification task: Iris Dataset, Wine Data, Pima Indians Diabetes Database, Bupa Liver Disorders, and Heart Disease. When compared with several other pattern classification models and algorithms, the HNFB 1 model has shown similar or better classification performance. Nevertheless, its performance in terms of processing time is remarkable. The HNFB 1 converged in less than one minute for all the databases described in the case study.

This paper explores the use of an hierarchical neurofuzzy model for image classification of macroscopic rock texture. The relevance of this study is to help Geologists in diagnosing and planning the oil reservoir exploitation. The same approach can be also applied to metals, in order to classify the different types of materials based on their grain texture. We present an image classification for macroscopic rocks, based on these texture descriptors and on a neuro-fuzzy approach.

We used a Hierarchical Neuro-Fuzzy Class Method based on binary space partitioning (NFHB-Class Method) for macroscopic rock texture classification. The relevance of this study is in helping Geologists in the diagnosis and planning of oil reservoir exploration. The proposed method is capable of generating its own decision structure, with automatic extraction of fuzzy rules. These rules are linguistically interpretable, thus explaining the obtained data structure. The presented image classification for macroscopic rocks is based on texture descriptors, such as spatial variation coefficient, Hurst coefficient, entropy, and cooccurrence matrix. Four rock classes have been evaluated by the NFHB-Class Method: gneiss (two subclasses), basalt (four subclasses), diabase (five subclasses), and rhyolite (five subclasses). These four rock classes are of great interest in the evaluation of oil boreholes, which is considered a complex task by geologists. We present a computer method to solve this problem. In order to evaluate system performance, we used 50 RGB images for each rock classes and subclasses, thus producing a total of 800 images. For all rock classes, the NFHB-Class Method achieved a percentage of correct hits over 73%. The proposed method converged for all tests presented in the case study.

ResumenEste trabajo presenta un sistema de detección de posición angular de buques, utilizando técnicas de extracción de características en imágenes digitales y redes neurales artificiales. Se utilizan imágenes de embarcaciones militares generadas gráficamente. Se realizaron diferentes pruebas usando redes neuronales artificiales aplicadas al conjunto de características geométricas. Los resultados de las pruebas comprueban la importante contribución de la utilización de algoritmos de reconocimiento en la determinación de posicionamiento angular de embarcaciones, independiente del alejamiento del observador. Los resultados favorecen aplicaciones futuras en el seguimiento de buques (tracking) utilizando imágenes infrarrojas. Palabras clave: tracking, redes neuronales, visión computacional, imágenes infrarrojas Target Angular Position Detection using Computer Vision Techniques and Artificial Neural Networks AbstractThis paper presents a system for detecting angular position of targets, using feature extraction techniques in digital imaging and artificial neural networks. Military ships images graphically generated by three-dimensional solid modeling software are used. Several tests using artificial neural networks applied to the set of geometric features were performed. The results show the important contribution of recognition algorithms in determining the ship angular position, regardless of their distance from the observer. The results encourage future applications for tracking targets using infrared images.