Results 61 to 70 of about 388 (166)
Scale Invariant and Noise Robust Interest Points With Shearlets
, 2017 Shearlets are a relatively new directional multi-scale framework for signal analysis, which have been shown effective to enhance signal discontinuities, such as edges and corners at multiple scales even in the presence of a large quantity of noise.
Miguel A. Duval-Poo +7 more
core +1 more source
Democracy of shearlet frames with applications
Journal of Approximation Theory, 2017 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire +1 more source
IET Image Processing, Volume 18, Issue 5, Page 1358-1372, 17 April 2024.An adaptive neuro‐fuzzy inference system is presented based on an optimization of genetic algorithm to classify normal and abnormal brain tumours. Abstract An adaptive neuro‐fuzzy inference system is presented which is optimized by a genetic algorithm to classify normal and abnormal brain tumours.
Marzieh Ghahramani, Nabiollah Shiri
wiley +1 more source
Deep learning‐based methods for detecting defects in cast iron parts and surfaces
IET Image Processing, Volume 18, Issue 1, Page 47-58, 10 January 2024.First, this article used multiple data augmentation methods to alleviate the problem of small sample size in casting datasets. Second, attention mechanism was introduced. Finally, a novel feature fusion layer structure was adopted to improve the original network model.
Pengyu Wang, Peng Jing
wiley +1 more source
mBCCf: Multilevel Breast Cancer Classification Framework Using Radiomic Features
International Journal of Intelligent Systems, Volume 2024, Issue 1, 2024.Breast cancer characterization remains a significant and challenging issue in contemporary medicine. Accurately distinguishing between malignant and benign breast lesions is crucial for effective diagnosis and treatment. The anatomical structure of malignant breast ultrasound images is more chaotic than that of benign images due to disease pathologies.
Lipismita Panigrahi +6 more
wiley +1 more source
Sparse Regularization Based on Orthogonal Tensor Dictionary Learning for Inverse Problems
Mathematical Problems in Engineering, Volume 2024, Issue 1, 2024.In seismic data processing, data recovery including reconstruction of the missing trace and removal of noise from the recorded data are the key steps in improving the signal‐to‐noise ratio (SNR). The reconstruction of seismic data and removal of noise becomes a sparse optimization problem that can be solved by using sparse regularization.
Diriba Gemechu, Francisco Rossomando
wiley +1 more source
, 2022 The discrete shearlet transformation accurately represents the discontinuities and edges occurring in magnetic resonance imaging, providing an excellent option of a sparsifying transform.
Protonotarios, N.E. +3 more
core
, 2014 Shearlets emerged in recent years among the most successful frameworks for the efficient representation of multidimensional data. Indeed, after it was recognized that traditional multiscale methods are not very efficient at capturing edges ...
Gitta Kutyniok, Demetrio Labate
core
Shearlets and sparse representation for microresistivity borehole image inpainting
, 2018 Microresistivity image logs from wireline tools commonly include nonmeasured gaps corresponding to the spaces between electrode-carrying pads in contact with the borehole wall.
Peter Elkington, Said Assous
core +1 more source
Analysis vs. synthesis sparsity for α-shearlets
, 2017 There are two notions of sparsity associated to a frame Ψ=(ψi)i∈I: Analysis sparsity of f means that the analysis coefficients (⟨f,ψi⟩)i are sparse, while synthesis sparsity means that f=∑iciψi with sparse coefficients (ci)i.
Pein, Anne, Voigtlaender, Felix
core