TARGET RECOGNITION METHOD AND APPARATUS FOR A DEFORMED IMAGE
First Claim
1. An object recognition method for a deformed image, comprising:
- inputting an image into a preset localization network to obtain a plurality of localization parameters for the image, wherein the preset localization network comprises a preset number of convolutional layers, and wherein the plurality of localization parameters are obtained by regressing image features in a feature map that is generated from a convolution operation on the image;
performing a spatial transformation on the image based on the plurality of localization parameters to obtain a corrected image; and
inputting the corrected image into a preset recognition network to obtain an object classification result for the image.
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Abstract
An object recognition method and apparatus for a deformed image are provided. The method includes: inputting an image into a preset localization network to obtain a plurality of localization parameters for the image, wherein the preset localization network comprises a preset number of convolutional layers, and wherein the plurality of localization parameters are obtained by regressing image features in a feature map that is generated from a convolution operation on the image; performing a spatial transformation on the image based on the plurality of localization parameters to obtain a corrected image; and inputting the corrected image into a preset recognition network to obtain an object classification result for the image. In the process of the neural network based object recognition, the embodiment of the present application first transforms the deformed image that has deformation, and then performs the object recognition on the transformed image.
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Citations
20 Claims
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1. An object recognition method for a deformed image, comprising:
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inputting an image into a preset localization network to obtain a plurality of localization parameters for the image, wherein the preset localization network comprises a preset number of convolutional layers, and wherein the plurality of localization parameters are obtained by regressing image features in a feature map that is generated from a convolution operation on the image; performing a spatial transformation on the image based on the plurality of localization parameters to obtain a corrected image; and inputting the corrected image into a preset recognition network to obtain an object classification result for the image. - View Dependent Claims (2, 3, 4, 5)
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6-10. -10. (canceled)
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11. An electronic device, which comprises a processor and a memory,
the memory is configured to store a computer program; - and
the processor is configured to execute the computer program stored in the memory to carry out operations comprising; inputting an image into a preset localization network to obtain a plurality of localization parameters for the image, wherein the preset localization network comprises a preset number of convolutional layers, and wherein the plurality of localization parameters are obtained by regressing image features in a feature map that is generated from a convolution operation on the image; performing a spatial transformation on the image based on the plurality of localization parameters to obtain a corrected image; and inputting the corrected image into a preset recognition network to obtain an object classification result for the image. - View Dependent Claims (12, 13, 14, 15)
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16. A non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, cause the processor to carry out operations comprising:
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inputting an image into a preset localization network to obtain a plurality of localization parameters for the image, wherein the preset localization network comprises a preset number of convolutional layers, and wherein the plurality of localization parameters are obtained by regressing image features in a feature map that is generated from a convolution operation on the image; performing a spatial transformation on the image based on the plurality of localization parameters to obtain a corrected image; and inputting the corrected image into a preset recognition network to obtain an object classification result for the image. - View Dependent Claims (17, 18, 19, 20)
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Specification