Multiresolution analysis

About: Multiresolution analysis is a research topic. Over the lifetime, 4032 publications have been published within this topic receiving 140743 citations. The topic is also known as: Multiresolution analysis, MRA.

MR-Brain Image Segmentation Using Gaussian Multiresolution Analysis and the EM Algorithm.

Abstract: We present a MR image segmentation algorithm based on the conventional Expectation Maximization (EM) algorithm and the multiresolution analysis of images. Although the EM algorithm was used in MRI brain segmentation, as well as, image segmentation in general, it fails to utilize the strong spatial correlation between neighboring pixels. The multiresolution-based image segmentation techniques, which have emerged as a powerful method for producing high-quality segmentation of images, are combined here with the EM algorithm to overcome its drawbacks and in the same time take its advantage of simplicity. Two data sets are used to test the performance of the EM and the proposed Gaussian Multiresolution EM, GMEM, algorithm. The results, which proved more accurate segmentation by the GMEM algorithm compared to that of the EM algorithm, are represented statistically and graphically to give deep

Haar Wavelet Quasilinearization Approach for Solving Nonlinear Boundary Value Problems

Abstract: Objective of our paper is to present the Haar wavelet based solutions of boundary value problems by Haar collocation method and utilizing Quasilinearization technique to resolve quadratic nonlinearity in y. More accurate solutions are obtained by wavelet decomposition in the form of a multiresolution analysis of the function which represents solution of boundary value problems. Through this analysis, solutions are found on the coarse grid points and refined towards higher accuracy by increasing the level of the Haar wavelets. A distinctive feature of the proposed method is its simplicity and applicability for a variety of boundary conditions. Numerical tests are performed to check the applicability and efficiency. C++ program is developed to find the wavelet solution.

Discrete, spatiotemporal, wavelet multiresolution analysis method for computing optical flow

Abstract: A wavelet-based system for computing localized velocity fields associated with time-sequential imagery is described. The approach combines the mathematical rigor of the multiresolution wavelet analysis with well-known spatiotemporal frequency flow computation principles. The foundation of the approach consists of a unique, nonhomogeneous multiresolution wavelet filter bank designed to extract moving objects in a 3-D image sequence based on their location, size, and speed. The filter bank is generated by an unconventional 3-D subband coding scheme that generates 20 orientation-tuned filters at each spatial and temporal resolution. The frequency responses of the wavelet filter bank are combined using a least-squares method to assign a velocity vector to each spatial location in an image sequence. Several examples are provided to demonstrate the flow computation abilities of the wavelet vector motion sensor.

A classification method of glass defect based on multiresolution and information fusion.

Abstract: The existence of defects is a key factor for quality degradation of float glass. This paper introduces an approach of glass defect identification based on multiresolution and information fusion analysis. With the help of 2D discrete wavelet transform, the subtracting defect image with a valid region is decomposed into approximated subimages and detailed subimages. The approximated subimages in three-level scales and the original defect image are chosen to proceed recognition with artificial neural network and fuzzy k-nearest neighbor. The decisive vectors from four classifiers are fused by an improved Dempster–Shafer (DS) evidence theory with head difference calibration-DS. Besides, a twice OTSU segmentation method as well as ten statistic features are interpreted for the preparation of defect recognition. The results of application indicate that the proposed algorithm can significantly increase the correct recognition rate of glass defects in five classes.