The invention provides a deep learning image compression method based on semantic analysis, and belongs to the technical field of image lossy compression. The deep learning image compression method comprises the following steps: 1, performing feature extraction on an input image by using a convolutional neural network (CNN), and identifying the semantic importance degree of each pixel; 2, partitioning an input image, and calculating a corresponding compression ratio of each image block according to the semantic importance degree of the image block through compressed bit allocation; and 3, compressing and recovering the image according to the calculated compression ratio by using a network structure based on the CNN and the recurrent neural network LSTM. According to the invention, the application of deep learning in the fields of image semantic analysis and image compression is combined; compressed bits of different areas of the image are distributed according to the semantic importance degree, so that the image compression effect based on semantic importance degree analysis is achieved, and the visual quality of the area, concerned by human eyes, in the image is improved, and theoccupied space of the whole image is smaller; and the deep learning image compression method has good application value in the aspect of Internet of Things image processing.

Deep learning image compression method based on semantic analysis

The invention discloses an epilepsy detection method based on adaptive weighted feature fusion of a deep network stack model. The method comprises the following steps of: 1, after an original electroencephalogram signal is preprocessed, conducting discrete Fourier transform on data of each channel of the electroencephalogram signal, and obtaining an average amplitude spectrum of the electroencephalogram signal; randomly dividing the obtained average amplitude spectrum features into several parts so as to be suitable for a deep network stack model of adaptive weighted feature fusion; 2, performing second feature extraction on the data set obtained in the step 1 by using convolutional neural networks of different structures; and 3, performing adaptive weighted feature fusion on the features extracted by different convolutional neural networks, and finally predicting the category of the sample by an error correction output coding model based on a support vector machine. According to the invention, through a stack integration method and an adaptive weighted feature fusion algorithm, the system can fuse features extracted by depth networks of different structures, and the epilepsy prediction effect is improved.

Epilepsy detection method based on adaptive weighted feature fusion of deep network stack model

The invention provides a lung tissue image segmentation method based on deep learning, and belongs to the technical field of medical image segmentation. The lung tissue image segmentation method comprises the steps that an X-ray chest radiograph image is input into a segmentation model, the segmentation model is obtained through training of multiple sets of training data, and each set of trainingdata in the multiple sets of training data comprises the X-ray chest radiograph image and a corresponding gold standard used for identifying lung tissue; and output information of the model is obtained, and the output information comprises a segmentation result of the lung tissue in the X-ray chest radiography image. According to the lung tissue image segmentation method, the segmentation of the lung tissue of the X-ray chest radiography is realized through an improved Deeplabv3+ deep learning method, and the problems of local convergence and false positive segmentation when the lung tissue issegmented by using a traditional method are solved; the lung tissue image segmentation method respectively obtains 95.3% of MIoU and 94.8% of MIoU on the public data set and the pneumoconiosis data set; and the false positive problem of the FCN network is solved, and the segmentation accuracy of ribs at the thoracic diaphragm angle and on the X-ray chest radiography in the SCAN network method isimproved.

Lung tissue image segmentation method based on deep learning

The invention discloses an image semantic segmentation method based on dense connection, and the method comprises the following steps: S101, determining a to-be-used data set and a corresponding labeled image, and enabling the labeled image to give a category label to each pixel; S102, designing an image semantic segmentation network based on dense connection, wherein the image semantic segmentation network comprises a dense connection unit, a channel fusion unit and a feature map fusion unit; S103, designing dense connection units, performing image transformation operation on the feature mapextracted by the connection units, then performing transposition operation, and then performing softmax operation to obtain a channel attention mapping map; designing a channel fusion unit, and enhancing the feature maps with the interdependence relationship by mining the interdependence relationship between the channel maps; and designing a feature map fusion unit, and performing addition operation fusion operation on an output mapping map of the channel fusion unit and a result of the densely connected feature extraction unit. According to the method, the characteristics between different layers and different channels can be effectively and repeatedly utilized, so that the segmentation efficiency is effectively improved, and the parameter quantity is reduced.

Image semantic segmentation method based on dense connection

The embodiment of the invention discloses an intelligent vehicle license plate recognition method and device suitable for a complex illumination environment. The method comprises the steps that a to-be-detected image is acquired; performing edge detection on the to-be-detected image by using a preset edge detection algorithm, and determining a to-be-detected gradient image corresponding to the to-be-detected image; utilizing the to-be-detected image, the to-be-detected gradient image and a pre-trained license plate detection model; determining whether the to-be-detected image contains a license plate; determining that the to-be-detected image contains the license plate; determining the position information of the license plate contained in the to-be-detected image; wherein the pre-trainedlicense plate detection model is a model obtained by training based on a sample original image marked with the position of a sample vehicle and a sample edge image corresponding to the sample originalimage, so that the detection rate of license plate detection in a complex illumination environment, such as an environment scene with large illumination change, is improved.

Intelligent vehicle license plate recognition method and device suitable for complex illumination environment

The invention discloses an image semantic segmentation method based on deep learning. The method comprises four parts of data set processing, deep semantic segmentation network construction, deep semantic segmentation network training and parameter learning, and semantic segmentation on a test image. The RGB image and the gray level image of the input image are used as the input of the network model, the edge information of the gray level image is fully utilized, and the richness degree of input characteristics is effectively increased; a convolutional neural network and a bidirectional threshold recursion unit are combined, and more context dependency relationships and global feature information are captured on the basis of learning image local features; coordinate information is added tothe feature map through the first coordinate channel module and the second coordinate channel module, the coordinate features of the model are enriched, the generalization ability of the model is improved, and a semantic segmentation result with high resolution and accurate boundary is generated.

An image semantic segmentation method based on deep learning

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An image semantic segmentation method based on deep learning