Scientific computing,Image edge detection,Feature detection,Filtering algorithms,Feature extraction,Kernel,Image reconstructionEdge detection is a basic technique of fundamental feature detection in image proce
CNN structures extract image characteristics through convolution filters. Based on their size, filters select specific features from a scan for extraction. The size of filters was previously selected by network designers prior to the implementation of Inception modules. As opposed to filter concatenation,...
The combination of a dropout rate of 0.5 and the Adam optimizer establishes a strong foundation for training CNNs, resulting in enhanced generalization and expedited convergence. As the depth of the network develops, the number of filters often increases to enable the model to capture more complex...
The 1D CNN model is designed by stacking two convolutional layers each with 64 filters. The kernel size of the 1D CNN in this study is equal to 3 that indicates the length of the 1D convolution window with stride size of 1. A Max-pooling layer with the window size equal to 2 is ...
However, most disease recognition algorithms extract image features via multiple filters [8]. The extraction process is tedious and frequently selects for recognition objects with noticeable disease features and concentrated disease areas. As a result, this traditional recognition method cannot extract ...
The combination of a dropout rate of 0.5 and the Adam optimizer establishes a strong foundation for training CNNs, resulting in enhanced generalization and expedited convergence. As the depth of the network develops, the number of filters often increases to enable the model to capture more complex...
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...
Among these comparison methods, Mask R-CNN [20] and MS R-CNN [22] are both anchor-based two-stage instance segmentation algorithms. More specifically, MS R-CNN is an improved version of Mask R-CNN by adding a single MaskIoU for Mask score, which can obtain similar performance to the ...
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 ...