A dilated casual convolution with dilated factors d = 1,2,4 and filter kernel size k = 3.</p
<p>Convolution filtering is used to modify the characteristics of an image. The images of dimensions...
<p>The insertions are convolved with a kernel function with a width determined by the scale paramete...
<p>The values of Macro-Precision, Macro-Recall, Macro-F1 and Micro-F1 under different number of feat...
A dilated temporal convolution with factor d = 1, 2, 4 and filter size k = 3.</p
In all examples the kernel size is 3x3, but the rate differs. The rate defines by which factor the f...
Complexity comparison between the regular convolution kernel versus the depthwise separable convolut...
One of the most effective image processing techniques is the use of convolutional neural networks th...
Both operations use a 3x3 kernel and a stride of two. Traditional convolution determines the output ...
Group A is the conventional distribution, Group B is the distribution after arbitrary migration, Gro...
<p>Soft contrast and conspicuity comparison for (a) FBP with sharp kernel filtering, (b) FBP with so...
Computing discrete two-dimensional convolutions is an important problem in image processing. In mat...
<p>Quantitative results by different super-resolution algorithms (factor = 3).</p
The analysis of the discrete Mellin convolution is given. A generalization of rezults from [4,5] is ...
The left, middle, and right sides represent standard convolution, deformable convolution, and T-defo...
When designing Convolutional Neural Networks (CNNs), one must select the size of the convolutional k...
<p>Convolution filtering is used to modify the characteristics of an image. The images of dimensions...
<p>The insertions are convolved with a kernel function with a width determined by the scale paramete...
<p>The values of Macro-Precision, Macro-Recall, Macro-F1 and Micro-F1 under different number of feat...
A dilated temporal convolution with factor d = 1, 2, 4 and filter size k = 3.</p
In all examples the kernel size is 3x3, but the rate differs. The rate defines by which factor the f...
Complexity comparison between the regular convolution kernel versus the depthwise separable convolut...
One of the most effective image processing techniques is the use of convolutional neural networks th...
Both operations use a 3x3 kernel and a stride of two. Traditional convolution determines the output ...
Group A is the conventional distribution, Group B is the distribution after arbitrary migration, Gro...
<p>Soft contrast and conspicuity comparison for (a) FBP with sharp kernel filtering, (b) FBP with so...
Computing discrete two-dimensional convolutions is an important problem in image processing. In mat...
<p>Quantitative results by different super-resolution algorithms (factor = 3).</p
The analysis of the discrete Mellin convolution is given. A generalization of rezults from [4,5] is ...
The left, middle, and right sides represent standard convolution, deformable convolution, and T-defo...
When designing Convolutional Neural Networks (CNNs), one must select the size of the convolutional k...
<p>Convolution filtering is used to modify the characteristics of an image. The images of dimensions...
<p>The insertions are convolved with a kernel function with a width determined by the scale paramete...
<p>The values of Macro-Precision, Macro-Recall, Macro-F1 and Micro-F1 under different number of feat...
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