2-Dimensional Wavelet Packet Spectrum for Texture Analysis
نویسندگان
چکیده
The paper derives a 2-Dimensional spectrum estimator from some recent results on the statistical properties of wavelet packet coefficients of random processes. It provides an analysis of the bias of this estimator with respect to the wavelet order. The paper also discusses the performance of this wavelet based estimator, in comparison with the conventional 2-D Fourier-based spectrum estimator on texture analysis and content based image retrieval. It highlights the effectiveness of the wavelet based spectrum estimation.
منابع مشابه
HYBRID ARTIFICIAL NEURAL NETWORKS BASED ON ACO-RPROP FOR GENERATING MULTIPLE SPECTRUM-COMPATIBLE ARTIFICIAL EARTHQUAKE RECORDS FOR SPECIFIED SITE GEOLOGY
The main objective of this paper is to use ant optimized neural networks to generate artificial earthquake records. In this regard, training accelerograms selected according to the site geology of recorder station and Wavelet Packet Transform (WPT) used to decompose these records. Then Artificial Neural Networks (ANN) optimized with Ant Colony Optimization and resilient Backpropagation algorith...
متن کاملTexture Analysis Using Multidimensional Histogram
Texture features have long been used in remote sensing applications for representing and retrieving regions similar to a query region. Various representations of texture have been proposed based on the power spectrum, grey-level cooccurrence matrices, wavelet features, Gabor features, etc. Analysis of several co-occurring pixel values may benefit texture description but is impeded by the expone...
متن کاملOptical and Sonar Image Classification: Wavelet Packet Transform vs Fourier Transform
To develop a noise-insensitive texture classification algorithm for both optical and underwater sidescan sonar images, we study the multichannel texture classification algorithm that uses the wavelet packet transform and Fourier transform. The approach uses a multilevel dominant eigenvector estimation algorithm and statistical distance measures to combine and select frequency channel features o...
متن کاملTexture Classification by Wavelet Packet Signatures
This correspondence introduces a new approach to characterize textures at multiple scales. The performance of wavelet packet spaces are measured in terms of sensitivity and selectivity for the classification of twenty-five natural textures. Both energy and entropy metrics were computed for each wavelet packet and incorporated into distinct scale space representations, where each wavelet packet ...
متن کاملAn Adaptive Approach for Texture Segmentation by Multi-channel Wavelet Frames
We introduce an adaptive approach for texture feature extraction based on multi-channel wavelet frames and two-dimensional envelope detection. Representations obtained from both standard wavelets and wavelet packets are evaluated for reliable texture segmentation. Algorithms for envelope detection based on edge detection and the Hilbert transform are presented. Analytic lters are selected for e...
متن کامل