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.

Wavelet based automatic thresholding for image segmentation

Abstract: In this paper, a new systematic method to segment possible target areas based on wavelet transforms is presented. We develop an analytic model for the segmentation of targets, which uses a novel multiresolution analysis in concert with a Bayesian classifier to identify the possible target areas. A method is developed which adaptively chooses thresholds to segment targets from background, by using a multiscale analysis of the image probability density function (PDF). We present examples which demonstrate the efficiency of the technique on a variety of targets.

Deep Adaptive Wavelet Network

Abstract: Even though convolutional neural networks have become the method of choice in many fields of computer vision, they still lack interpretability and are usually designed manually in a cumbersome trial-and-error process. This paper aims at overcoming those limitations by proposing a deep neural network, which is designed in a systematic fashion and is interpretable, by integrating multiresolution analysis at the core of the deep neural network design. By using the lifting scheme, it is possible to generate a wavelet representation and design a network capable of learning wavelet coefficients in an end-to-end form. Compared to state-of-the-art architectures, the proposed model requires less hyper-parameter tuning and achieves competitive accuracy in image classification tasks

Palmprint identification using wavelet energy

Abstract: Palmprint identification is the means of recognizing an individual from the database using his/ her palmprint features. Palmprint is easy to capture, requires cheaper equipment and is more acceptable by the public. Moreover, palmprint is also rich in features. Wavelet transform is a multi-resolution analysis tool that can extract palm lines in different resolution levels. In low-resolution level, fine palm lines are extracted. The higher the resolution level, the coarser are the extracted palm lines. In this work, a digital camera is used to acquire the ten right hand image of 100 different individuals. The hand images are pre-processed to find the key points. By referring to the key point, the palmprint images are rotated and cropped. The palmprint images are enhanced and resized. The resized images are decomposed using different types of wavelets for six decomposition levels. Two different wavelet energy representations are tested. The feature vectors are compared to the database using Euclidean Distance or classified using feedforward backpropagation neural network. From the results, an accuracy of 99.07 percent can be obtained using Db5 wavelet energy feature type 2 and classified with neural network.

Phase Function Effects on Identification of Terahertz Spectral Signatures Using the Discrete Wavelet Transform

Abstract: We describe the application of the discrete wavelet transform (DWT) in extracting the characteristic absorption signatures of materials from terahertz reflection spectra. We compare the performance of different mother wavelets, including Daubechies, least asymmetric (LA), and Coiflet, based on their phase and gain functions and filter lengths. We show that the phase functions of the wavelet and scaling filters result in spectral shifts to the absorption lines in the wavelet domain. We provide a solution by calculating advancement coefficients necessary to achieve effective zero-phase-function DWT. We demonstrate the utility of this signal processing technique using $\alpha$ -lactose monohydrate/polyethylene samples with different levels of rough surface scattering. In all cases, the DWT-based algorithm successfully extracts resonant signatures at 0.53 and 1.38 THz, even when they are obscured by the rough surface scattering effects. The DWT analysis with accompanying phase corrections can be utilized as a robust technique for material identification in nondestructive evaluation using terahertz spectroscopy.