This paper describes the wavelet transformation of IR spectra with the Daubechies analyzing wavelet functions as a feature extracting method that successfully reduces the spectral data more than 20-fold with a significant improvement in the classification process.

Hyperspectral images contain rich and fine spectral information, an improvement of land use/cover classification accuracy is expected from the use of such images. However, the classification methods that have been successfully applied to multispectral data in the past are not as effective as to hyperspectral data. The major cause is that the size of training data set does not correspond to the ...

Along with other electrical components, the transmission line suffers from the unexpected failures due to various faults. Protecting of transmission lines is one of the important tasks to safeguard electric power systems. For safe operation of EHVAC transmission line systems, the protection to detect, classify, locate accurately and clear the fault as fast as possible to maintain stability in t...

Image fusion is defi ned as the process of combining two or more different images into a new single image retaining important features from each image with extended information content. There are two approaches to image fusion, namely spatial fusion and transform fusion and three levels namely pixel, feature and region level. In spatial fusion, the pixel values from the source images are direct...

Automatic human iris verification is an active research area with numerous applications in security purposes. Unfortunately, most of feature extraction methods in human iris verification systems are sensitive to noise, scale and rotation. This paper proposes an integrated hybrid model among Discrete Wavelet Transform, Wavelet Neural Network and Genetic Algorithms for optimizing the feature extr...

–A detailed study on feature extractors in spatial and transformed domain is carried out in this work. The survey in Spatial domain include most of the traditional detectors until recently the SIFT and its variants. In the transformed domain, the detectors developed using the Fourier transforms to wavelet transforms have been explored. The advantages and the limitations of each one of them is e...

In this study, a new supervised noise-artifact-robust heart arrhythmia fusion classification solution, is introduced. Proposed method consists of structurally diverse classifiers with a new QRS complex geometrical feature extraction technique. Toward this objective, first, the events of the electrocardiogram (ECG) signal are detected and delineated using a robust wavelet-based algorithm. Then, ...

Feature selection is a very important aspect in the field of machine learning. It entails the search of an optimal subset from a very large data set with high dimensional feature space. Apart from eliminating redundant features and reducing computational cost, a good selection of feature also leads to higher prediction and classification accuracy. In this paper, an efficient feature selection t...

Optical character recognition system research has been acquired howling success, but the reconstruction of layout needs fonts of the characters. In this paper, a novel font recognition algorithm is proposed, which is based on multi-scale wavelet analysis. We adopt wavelet analysis and the grid method to deal with the character image, and extract wavelet energy density feature, and apply the BP ...

in this paper, we proposed a data-hiding scheme based on run length matrix. in a previously proposed method, a technique based on texture classification was introduced where four statically features extracted from run length matrix; then best cover images are selected based on these features. using appropriate features for comparing images from undetectability viewpoint, guarantees, less detect...