Results 11 to 20 of about 258 (126)
Normalized group activations based feature extraction technique using heterogeneous data for Alzheimer’s disease classification [PDF]
PeerJ Computer ScienceSeveral deep learning networks are developed to identify the complex atrophic patterns of Alzheimer's disease (AD). Among various activation functions used in deep neural networks, the rectifier linear unit is the most used one.
Krishnakumar Vaithianathan +5 more
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CURVELET BASED U-NET FRAMEWORK FOR BUILDING FOOTPRINT IDENTIFICATION [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2023 This paper proposes a multiresolution based U-net composite architecture for segmentation of remotely sensed images for building footprint identification.
R. A. Ansari, W. Thomas
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Central and Periodic Multi-Scale Discrete Radon Transforms
Applied Sciences, 2021 The multi-scale discrete Radon transform (DRT) calculates, with linearithmic complexity, the summation of pixels, through a set of discrete lines, covering all possible slopes and intercepts in an image, exclusively with integer arithmetic operations. An
Óscar Gómez-Cárdenes +3 more
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Noise Filtering of Remotely Sensed Images using Iterative Thresholding of Wavelet and Curvelet Transforms [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2014 This article presents techniques for noise filtering of remotely sensed images based on Multi-resolution Analysis (MRA). Multiresolution techniques provide a coarse-to-fine and scale-invariant decomposition of images for image interpretation.
R. A. Ansari, B. K. Mohan
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Wavelets and curvelets transform for image denoising to damage identification of thin plate
Results in Engineering, 2023 As a common structural form, thin plates are widely used in civil engineering. Since the thin plate needs to face harsh work conditions, the damage inevitably to be accumulated, thus affecting the stability and safety of the application components ...
Deng Yulong +7 more
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Migration preconditioning with Curvelets [PDF]
SEG Technical Program Expanded Abstracts 2004, 2004 In this paper, the property of Curvelet transforms for preconditioning the migration and normal operators is investigated. These operators belong to the class of Fourier integral operators and pseudo-differential operators, respectively. The effect of this preconditioner is shown in term of improvement of sparsity, convergence rate, number of iteration
Moghaddam, Peyman P., Herrmann, Felix J.
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Accounting for observation errors in image data assimilation [PDF]
Tellus: Series A, Dynamic Meteorology and Oceanography, 2015 This paper deals with the assimilation of image-type data. Such kinds of data, such as satellite images, have good properties (dense coverage in space and time), but also one crucial problem for data assimilation: they are affected by spatially ...
Vincent Chabot +3 more
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The curvelet transform for image denoising [PDF]
Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205), 2002 Summary form only given, as follows. We present approximate digital implementations of two new mathematical transforms, namely, the ridgelet transform and the curvelet transform. Our implementations offer exact reconstruction, stability against perturbations, ease of implementation, and low computational complexity. We apply these digital transforms to
Jean-Luc Starck +2 more
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Deconvolution based on the curvelet transform [PDF]
Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), 2004 This paper describes a new deconvolution algorithm, based on both the wavelet transform and the curvelet transform. It extends previous results which were obtained for the denoising problem. Using these two different transformations in the same algorithm allows us to optimally detect in the same time isotropic features, well represented by the wavelet ...
Nguyen, Mai K. +2 more
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IEEE Signal Processing Magazine, 2010 Multiresolution methods are deeply related to image processing, biological and computer vision, and scientific computing. The curvelet transform is a multiscale directional transform that allows an almost optimal nonadaptive sparse representation of objects with edges.
Jianwei Ma, Gerlind Plonka
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