Texture Analysis using wavelet Transform - Mishra, Vinay priy (ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 2020)
In this research application of wavelet based multiscale image analysis methods for texture analysis has been highlighted. These methods are based on multiresolution properties of the two-dimensional wavelet transform, which is used to extract the features needed to discriminate and differentiate various textures more accurately then existing methods, we also took into account the texture model, the noise distribution, and the inter-dependence of the texture features which further help in discriminating factor. Multiresolution approach is nothing but a modified wavelet transform called the tree-structured wavelet transform or wavelet packets for texture analysis and classification. This approach is motivated by the observation that a large class of natural textures can be modeled as quasi-periodic signals whose dominant frequencies are located in the middle frequency channels. With the transform, we are able to zoom into any desired frequency channels for further decomposition and thus we could extract more texture features as compared to other methods.