Results 31 to 40 of about 131 (116)
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
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Shearlets on Bounded Domains [PDF]
, 2011 Shearlet systems have so far been only considered as a means to analyze $L^2$-functions defined on $\R^2$, which exhibit curvilinear singularities. However, in applications such as image processing or numerical solvers of partial differential equations the function to be analyzed or efficiently encoded is typically defined on a non-rectangular shaped ...
Kutyniok, Gitta, Lim, Wang-Q
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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
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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
Different Faces of the Shearlet Group [PDF]
The Journal of Geometric Analysis, 2015 Recently, shearlet groups have received much attention in connection with shearlet transforms applied for orientation sensitive image analysis and restoration. The square integrable representations of the shearlet groups provide not only the basis for the shearlet transforms but also for a very natural definition of scales of smoothness spaces, called ...
Dahlke, Stephan +5 more
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Constructing Multiwavelet-based Shearlets and using Them for Automatic Segmentation of Noisy Brain Images Affected by COVID-19. [PDF]
J Med Signals Sens, 2023 Aghazadeh N, Moradi P, Noras P.
europepmc +1 more source
Discrete Shearlets as a Sparsifying Transform in Low-Rank Plus Sparse Decomposition for Undersampled (k, t)-Space MR Data. [PDF]
J Imaging, 2022 Protonotarios NE +3 more
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Shearlets and Microlocal Analysis [PDF]
, 2012 Although wavelets are optimal for describing pointwise smoothness properties of univariate functions, they fail to efficiently characterize the subtle geometric phenomena of multidimensional singularities in high-dimensional functions. Mathematically these phenomena can be captured by the notion of the wavefront set which describes point- and direction-
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Image Processing Using Shearlets [PDF]
, 2012 Since shearlets provide nearly optimally sparse representations for a large class of functions that are useful to model natural images, many image processing methods benefit from their use. In particular, the error rates of data estimation from noise are highly dependent on the sparsity properties of the representation, so that many successful ...
Glenn R. Easley, Demetrio Labate
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