What is the network that can extract edge features of images?

The network that can extract the edge features of the picture is the convolution layer; the purpose of the convolution operation is to extract different features of the input. The first convolution layer may only be able to extract some low-level features such as edges, lines and Angular level, more layers of the network can iteratively extract more complex features from low-level features.

The operating environment of this article: Windows 7 system, DELL G3 computer

What is the network that can extract edge features of images?

The network that can extract the edge features of the image is the convolutional layer.

Each convolutional layer (Convolutional layer) in the convolutional neural network is composed of several convolution units, and the parameters of each convolution unit are optimized through the back propagation algorithm. The purpose of the convolution operation is to extract different features of the input. The first convolution layer may only be able to extract some low-level features such as edges, lines, and corners. More layers of networks can iteratively extract more complex features from low-level features. Characteristics.

Convolutional Neural Network

Convolutional Neural Network (CNN) is a feedforward neural network whose artificial neurons can respond to a part of the coverage Surrounding units within range, excellent performance for large image processing.

A convolutional neural network consists of one or more convolutional layers and a fully connected layer at the top (corresponding to a classic neural network), as well as associated weights and pooling layers. This structure enables convolutional neural networks to exploit the two-dimensional structure of the input data. Convolutional neural networks can give better results in image and speech recognition compared to other deep learning structures. This model can also be trained using the backpropagation algorithm. Compared to other deep, feedforward neural networks, convolutional neural networks require fewer parameters to consider, making them an attractive deep learning structure.

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