Modelos conexionistas auto-organizados y su aplicación en reconocimiento de patrones

El trabajo en curso tiene por objeto desarrollar técnicas conexionistas para reconocimiento de patrones. A partir del sistema ya desarrollado por el grupo (que consiste básicamente en un modelo híbrido no supervisado -de tipo autoorganizado- seguido de una instancia supervisada) se estudia la introducción de innovaciones que incrementen su potencia y su eficiencia. Las investigaciones en curso giran alrededor de dos frentes: i) preprocesamiento de la entrada: se consideran opciones a la técnica inicialmente empleada (máscaras de Kirsch), entre ellas el uso de transformadas wavelet y la extracción de componentes principales; ii) estructura del módulo intermedio (no supervisado): analizamos posibles sofisticaciones orientadas a obtener una clasificación más especializada de acuerdo con las características de la distribución de los datos de entrada. La calidad y eficiencia de la propuesta resultante deberán ser luego comparadas con las de los métodos ya existentes.Eje: Inteligencia artificialRed de Universidades con Carreras en Informática (RedUNCI

Modelos conexionistas auto-organizados y su aplicación en reconocimiento de patrones

El trabajo en curso tiene por objeto desarrollar técnicas conexionistas para reconocimiento de patrones. A partir del sistema ya desarrollado por el grupo (que consiste básicamente en un modelo híbrido no supervisado -de tipo autoorganizado- seguido de una instancia supervisada) se estudia la introducción de innovaciones que incrementen su potencia y su eficiencia. Las investigaciones en curso giran alrededor de dos frentes: i) preprocesamiento de la entrada: se consideran opciones a la técnica inicialmente empleada (máscaras de Kirsch), entre ellas el uso de transformadas wavelet y la extracción de componentes principales; ii) estructura del módulo intermedio (no supervisado): analizamos posibles sofisticaciones orientadas a obtener una clasificación más especializada de acuerdo con las características de la distribución de los datos de entrada. La calidad y eficiencia de la propuesta resultante deberán ser luego comparadas con las de los métodos ya existentes.Eje: Inteligencia artificialRed de Universidades con Carreras en Informática (RedUNCI

Reconocimiento de dígitos manuscritos usando la transformada wavelet continua en 2 dimensiones y redes neuronales

En este trabajo presentamos un método de preprocesamiento para el reconocimiento de dígitos manuscritos, basado en la aplicación de la transformada wavelet continua en dos dimensiones. Los datos preprocesados son utilizados como entrada de una red neuronal del tipo feed forward multicapa, la cual es entrenada con el algoritmo de backpropagation. Nuestros resultados preliminares son alentadoresIn this paper we present a preprocessing method for handwritten numerals recognition, based on a two dimensional continuous wavelet transform. We use the wavelet transformed digits to input into a multilayer feed forward neural network, trained with backpropagation algorithm. Our preliminary results are promising.Red de Universidades con Carreras en Informática (RedUNCI

Reconocimiento de dígitos manuscritos usando la transformada wavelet continua en 2 dimensiones y redes neuronales

En este trabajo presentamos un método de preprocesamiento para el reconocimiento de dígitos manuscritos, basado en la aplicación de la transformada wavelet continua en dos dimensiones. Los datos preprocesados son utilizados como entrada de una red neuronal del tipo feed forward multicapa, la cual es entrenada con el algoritmo de backpropagation. Nuestros resultados preliminares son alentadoresIn this paper we present a preprocessing method for handwritten numerals recognition, based on a two dimensional continuous wavelet transform. We use the wavelet transformed digits to input into a multilayer feed forward neural network, trained with backpropagation algorithm. Our preliminary results are promising.Red de Universidades con Carreras en Informática (RedUNCI

Handwritten Digit Recognition by Fourier-Packet Descriptors

Any statistical pattern recognition system includes a feature extraction component. For character patterns, several feature families have been tested, such as the Fourier-Wavelet Descriptors. We are proposing here a generalization of this family: the Fourier-Packet Descriptors. We have selected sets of these features and tested them on handwritten digits: the error rate was 1.55% with a polynomial classifier for a 70 features set and 1.97% with a discriminative learning quadratic discriminant function for a 40 features set

License Plate Recognition Using Wavelet Transform and Cluster-k-Nearest Neighbor Classifier

This project proposes an implementation of License Plate Recognition in improving the current system of control access for Universiti Teknologi PETRONAS (UTP). The Plate Recognition methods utilized in this project are Wavelet Feature Extraction and Cluster-k-Nearest Neighbours Classifier. All the programs are carried out in MATLAB. Generally, the project is carried out by creating image database and the database of vehicles and owners information

Advances in Image Processing, Analysis and Recognition Technology

For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches

Off-line Arabic Handwriting Recognition System Using Fast Wavelet Transform

In this research, off-line handwriting recognition system for Arabic alphabet is introduced. The system contains three main stages: preprocessing, segmentation and recognition stage. In the preprocessing stage, Radon transform was used in the design of algorithms for page, line and word skew correction as well as for word slant correction. In the segmentation stage, Hough transform approach was used for line extraction. For line to words and word to characters segmentation, a statistical method using mathematic representation of the lines and words binary image was used. Unlike most of current handwriting recognition system, our system simulates the human mechanism for image recognition, where images are encoded and saved in memory as groups according to their similarity to each other. Characters are decomposed into a coefficient vectors, using fast wavelet transform, then, vectors, that represent a character in different possible shapes, are saved as groups with one representative for each group. The recognition is achieved by comparing a vector of the character to be recognized with group representatives. Experiments showed that the proposed system is able to achieve the recognition task with 90.26% of accuracy. The system needs only 3.41 seconds a most to recognize a single character in a text of 15 lines where each line has 10 words on average

Strategies for neural networks in ballistocardiography with a view towards hardware implementation

A thesis submitted for the degree of Doctor of Philosophy at the University of LutonThe work described in this thesis is based on the results of a clinical trial conducted by the research team at the Medical Informatics Unit of the University of Cambridge, which show that the Ballistocardiogram (BCG) has prognostic value in detecting impaired left ventricular function before it becomes clinically overt as myocardial infarction leading to sudden death. The objective of this study is to develop and demonstrate a framework for realising an on-line BCG signal classification model in a portable device that would have the potential to find pathological signs as early as possible for home health care. Two new on-line automatic BeG classification models for time domain BeG classification are proposed. Both systems are based on a two stage process: input feature extraction followed by a neural classifier. One system uses a principal component analysis neural network, and the other a discrete wavelet transform, to reduce the input dimensionality. Results of the classification, dimensionality reduction, and comparison are presented. It is indicated that the combined wavelet transform and MLP system has a more reliable performance than the combined neural networks system, in situations where the data available to determine the network parameters is limited. Moreover, the wavelet transfonn requires no prior knowledge of the statistical distribution of data samples and the computation complexity and training time are reduced. Overall, a methodology for realising an automatic BeG classification system for a portable instrument is presented. A fully paralJel neural network design for a low cost platform using field programmable gate arrays (Xilinx's XC4000 series) is explored. This addresses the potential speed requirements in the biomedical signal processing field. It also demonstrates a flexible hardware design approach so that an instrument's parameters can be updated as data expands with time. To reduce the hardware design complexity and to increase the system performance, a hybrid learning algorithm using random optimisation and the backpropagation rule is developed to achieve an efficient weight update mechanism in low weight precision learning. The simulation results show that the hybrid learning algorithm is effective in solving the network paralysis problem and the convergence is much faster than by the standard backpropagation rule. The hidden and output layer nodes have been mapped on Xilinx FPGAs with automatic placement and routing tools. The static time analysis results suggests that the proposed network implementation could generate 2.7 billion connections per second performance