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US 12,536,825 B2Granted
Text matting method and apparatus based on neural network, device, and storage medium
AssigneeBoe Technology Group Co., Ltd.
InventorRan Duan
Life of this patent
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Abstract
The present disclosure provides a text matting method and apparatus based on a neural network, a device, and a storage medium. The text matting method based on a neural network includes: processing a first image with a feature extraction network to obtain feature maps, processing the feature maps with an intermediate processing network to obtain intermediate feature maps, processing the intermediate feature maps with a feature fusion network to obtain a second image, wherein the second image includes a text feature extracted from the first image.
Claims16
A text matting method based on a neural network, comprising:
- processing a first image with a feature extraction network to obtain feature maps, wherein, the feature extraction network comprises sequentially connected n extraction convolutional network blocks, and wherein a 1-st extraction convolutional network block processes the first image and outputs a 1-st feature map, and an i-th extraction convolutional network block processes an (i−1)-th feature map and outputs an i-th feature map;
- processing the feature maps with an intermediate processing network to obtain intermediate feature maps, wherein, the intermediate processing network comprises n spatial convolutional network blocks, a 1-st spatial convolutional network block processes the 1-st feature map and outputs a 1-st intermediate feature map, and an i-th spatial convolutional network block processes the i-th feature map and outputs an i-th intermediate feature map;
- and
- processing intermediate feature maps with a feature fusion network to obtain a second image, wherein, the feature fusion network comprises sequentially connected n fusion convolutional network blocks, a 1-st fusion convolutional network block processes an n-th intermediate feature map output by an n-th spatial convolutional network block to obtain a 1-st fusion map, an i-th fusion convolutional network block processes an (n−i+1)-th intermediate feature map output by an (n−i+1)-th spatial convolutional network block and an (i−1)-th fusion map output by an (i−1)-th fusion convolutional network block to obtain an i-th fusion map,
- wherein an n-th fusion map output by an n-th fusion convolutional network block serves as the second image, and the second image comprises a text feature extracted from the first image, and
- wherein n is an integer greater than 1, and i is an integer greater than 1 but less than or equal to n.
3 more elements
A text matting apparatus based on a neural network, comprising:
- a feature extracting unit, configured to process a first image with a feature extraction network to obtain feature maps, wherein the feature extraction network comprises sequentially connected n extraction convolutional network blocks, and wherein a 1-st extraction convolutional network block processes the first image and outputs a 1-st feature map, and an i-th extraction convolutional network block processes an (i−1)-th feature map and outputs an i-th feature map;
- an intermediate processing unit, configured to process the feature maps with an intermediate processing network to obtain intermediate feature maps, wherein the intermediate processing network comprises n spatial convolutional network blocks, a 1-st spatial convolutional network block processes the 1-st feature map and outputs a 1-st intermediate feature map, and an i-th spatial convolutional network block processes the i-th feature map and outputs an i-th intermediate feature map;
- and
- a feature fusing unit, configured to process the intermediate feature maps with a feature fusion network to obtain a second image, wherein, the feature fusion network comprises sequentially connected n fusion convolutional network blocks, a 1-st fusion convolutional network block processes an n-th intermediate feature map output by an n-th spatial convolutional network block to obtain a 1-st fusion map, an i-th fusion convolutional network block processes an (n−i+1)-th intermediate feature map output by an (n−i+1)-th spatial convolutional network block and an (i−1)-th fusion map output by an (i−1)-th fusion convolutional network block to obtain an i-th fusion map,
- wherein, an n-th fusion map output by an n-th fusion convolutional network block serves as the second image;
- and the second image comprises a text feature extracted from the first image, and
- wherein n is an integer greater than 1, and i is an integer greater than 1 but less than or equal to n.
4 more elements
An image processing device, comprising:
- a processor;
- and
- a memory with computer-readable codes stored thereon, wherein the computer-readable codes, upon executed by the processor, cause the processor to:
- process a first image with a feature extraction network to obtain feature maps, wherein, the feature extraction network comprises sequentially connected n extraction convolutional network blocks, and wherein a 1-st extraction convolutional network block processes the first image and outputs a 1-st feature map, and an i-th extraction convolutional network block processes an (i−1)-th feature map and outputs an i-th feature map;
- process the feature maps with an intermediate processing network to obtain intermediate feature maps, wherein, the intermediate processing network comprises n spatial convolutional network blocks, a 1-st spatial convolutional network block processes the 1-st feature map and outputs a 1-st intermediate feature map, and an i-th spatial convolutional network block processes the i-th feature map and outputs an i-th intermediate feature map;
- and
- process intermediate feature maps with a feature fusion network to obtain a second image, wherein, the feature fusion network comprises sequentially connected n fusion convolutional network blocks, a 1-st fusion convolutional network block processes an n-th intermediate feature map output by an n-th spatial convolutional network block to obtain a 1-st fusion map, an i-th fusion convolutional network block processes an (n−i+1)-th intermediate feature map output by an (n−i+1)-th spatial convolutional network block and an (i−1)-th fusion map output by an (i−1)-th fusion convolutional network block to obtain an i-th fusion map,
- wherein an n-th fusion map output by an n-th fusion convolutional network block serves as the second image, and the second image comprises a text feature extracted from the first image, and
- wherein n is an integer greater than 1, and i is an integer greater than 1 but less than or equal to n.
6 more elements
Dependencies are inferred from the claim text.