This paper describes a new approach to the characterization of texture properties at multiple scales using the wavelet transform. The analysis uses an overcomplete wavelet decomposition, which yields a description that is translation invariant. It is shown that this representation constitutes a tight frame of l(2) and that it has a fast iterative algorithm. A texture is characterized by a set o...

In this paper, a novel road extraction method using Stationary Wavelet Transform is proposed. To detect road features from color aerial satellite imagery, Mexican hat Wavelet filters are used by applying the Stationary Wavelet Transform in a multiresolution, multi-scale, sense and forming the products of Wavelet coefficients at a different scales to locate and identify road features at a few sc...

Acoustical parameters extracted from the recorded voice samples are actively pursued for accurate detection of vocal fold pathology. Most of the system for detection of vocal fold pathology uses high quality voice samples. This paper proposes a hybrid expert system approach to detect vocal fold pathology using the compressed/low quality voice samples which includes feature extraction using wave...

In this article, a method has been offered to classify normal and defective tiles using wavelet transform and artificial neural networks. The proposed algorithm calculates max and min medians as well as the standard deviation and average of detail images obtained from wavelet filters, then comes by feature vectors and attempts to classify the given tile using a Perceptron neural network with a ...

In this paper, an automatic system is presented for word recognition using real Turkish word signals. This paper especially deals with combination of the feature extraction and classification from real Turkish word signals. A Discrete Wavelet Neural Network (DWNN) model is used, which consists of two layers: discrete wavelet layer and multi-layer perceptron. The discrete wavelet layer is used f...

This paper presents a comparative study of implementation of feature extraction and classification algorithms based on discrete wavelet decompositions and Adaptive Network Based Fuzzy Inference System (ANFIS) for digital modulation recognition. Here, in first stage, 20 different feature extraction methods are generated by separately using Daubechies, Biorthogonal, Coiflets, Symlets wavelet fami...

This paper presents an approach which addresses both de-noising and contrast enhancement. In a multiscale wavelet analysis framework, we take advantage of both soft thresholding and hard thresholding wavelet shrinkage techniques to reduce noise. In addition, we carry out nonlinear processing to enhance contrast within structures and along boundaries. Feature restoration and enhancement are acco...

In this study, a Mexican Hat Wavelet scalogram neural genetic network approach is proposed for signal classification. The Wavelet Scalogram network uses a Levenberg-Marquardt multilayer feedforward neural network-genetic algorithm hybrid structure, and its input layer constitutes the feature extraction part, whereas the hidden layer and output layer constitute the signal classification part. Fr...

In this paper a new visual feature, binary wavelet transform based histogram (BWTH) is proposed for content based image retrieval. BWTH is facilitated with the color as well as texture properties. BWTH exhibits the advantages of binary wavelet transform and histogram. The performance of CBIR system with proposed feature is observed on Corel 1000 (DB1) and Corel 2450 (DB2) natural image database...

Within this paper a new feature extraction techniques is presented which uses wavelet analysis and fractal theory for image recognition. The proposed method reduces the dimensionality of a twodimensional pattern by way of a central projection approach, and thereafter, performs Daubechies' wavelet transformation on the derived one-dimensional pattern to generate a set of wavelet transformation s...