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This article mainly introduces "what is the network that can extract the edge features of the picture in Windows". In the daily operation, I believe that many people have doubts about what is the network that can extract the edge features of the picture in Windows. The editor consulted all kinds of data and sorted out a simple and easy-to-use method of operation. I hope it will be helpful for you to answer the doubt that "what is the network that can extract the edge features of the picture in Windows?" Next, please follow the editor to study!

The network that can extract the edge features of the image is the convolution layer; the purpose of the convolution operation is to extract different features of the input, and the first convolution layer may only 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.

Operating environment of this article: Windows7 system, DELL G3 computer

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

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

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

Convolution neural network

Convolution neural network (Convolutional Neural Network,CNN) is a kind of feedforward neural network, whose artificial neurons can respond to the surrounding units in a part of the coverage area, and has excellent performance for large-scale image processing.

The convolution neural network consists of one or more convolution layers and the top fully connected layer (corresponding to the classical neural network), as well as the association weight and pooling layer (pooling layer). This structure enables the convolution neural network to make use of the two-dimensional structure of the input data. Compared with other deep learning structures, convolution neural network can give better results in image and speech recognition. This model can also be trained by back propagation algorithm. Compared with other depth and feedforward neural networks, convolution neural networks need to consider fewer parameters, making it an attractive deep learning structure.

At this point, the study on "what is the network that can extract the edge features of the picture in Windows" is over. I hope to be able to solve your doubts. The collocation of theory and practice can better help you learn, go and try it! If you want to continue to learn more related knowledge, please continue to follow the website, the editor will continue to work hard to bring you more practical articles!

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