A convolution on the concatenation of the di- lated convolution filters is used to combine the information fetched from varying receptive fields. Residual learning has been shown to be very effective in disparity refinement process so we propose a cascade of such blocks to iteratively improve the ...
Experimental results confirm our statement that the dilation of filters have a positive impact for edge-detection algorithms from simple to rather complex algorithms. Keywords: dilated filters; edge-detection operator; edge detection; first-order edge detection; Canny algorithm; Laplace algorithm; Laplace...
The convolutional kernel’s filter size is 3 × 3 with padding of the same for all orientations. The total number of filters that are used in the layer is 64. The convolutional operation is calculated as usual by convolving each 3 × 3 patch with an increase in the number of rows per...
Such FVs will later be appropriate as inputs for data mining or ML algorithms. 3.3. FS Employing HSBSOA The motivation behind the shark smell optimization (SSO) algorithm is the shark’s capability and supremacy in capturing prey by employing a strong sense of smell (SoS) in a short time....
Inspired by VH-stage and dilated convolutions, we create a DVH block for linear feature segmentation. As mentioned in the inception model [40], the 1×𝑛1×n and 𝑛×1n×1 filters can make the model easier to train. Further, 1×𝑛1×n and 𝑛×1n×1 filters in dilated ...
Inspired by VH-stage and dilated convolutions, we create a DVH block for linear feature segmentation. As mentioned in the inception model [40], the 1×𝑛1×n and 𝑛×1n×1 filters can make the model easier to train. Further, 1×𝑛1×n and 𝑛×1n×1 filters in dilated ...
First, for training AUN, we replace the DDBs in segmentor with standard convolution layers with 64, 128, 256 and 512 filters as in [17], which is commonly used in relative fields. Then, we use residual blocks in ARN where convolution layers are connected with residual connection. For ADFN...
We have used 3 × 3 filters in all convolution blocks. The total number of filters in the first convolution block is 64, and the rest are 128, 256, 512 in order. The three parallel stacks (branches) are similar except they have different dilation rates j = 1, 2 and 3, respectively ...