Dense feature scale detection for image matching
First Claim
Patent Images
1. A method comprising:
- identifying, using one or more processors of a machine, an image;
generating a plurality of scaled images from the image;
generating image feature datasets for the plurality of scaled images; and
generating a dense feature dataset by combining the image feature datasets with attention values of an attention map, the attention values being one or more numerical values that modify values of the dense feature dataset based at least in part on the scale of the plurality of scaled images;
tracking an object depicted in the images of an image sequence using the dense feature dataset;
generating a modified image sequence by applying an image effect to the object depicted in the image sequence, the image effect applied to the depiction of the object tracked in the images of the image sequence; and
publishing, on a network site, the modified image sequence as an electronic message.
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Abstract
Dense feature scale detection can be implemented using multiple convolutional neural networks trained on scale data to more accurately and efficiently match pixels between images. An input image can be used to generate multiple scaled images. The multiple scaled images are input into a feature net, which outputs feature data for the multiple scaled images. An attention net is used to generate an attention map from the input image. The attention map assigns emphasis as a soft distribution to different scales based on texture analysis. The feature data and the attention data can be combined through a multiplication process and then summed to generate dense features for comparison.
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Citations
20 Claims
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1. A method comprising:
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identifying, using one or more processors of a machine, an image; generating a plurality of scaled images from the image; generating image feature datasets for the plurality of scaled images; and generating a dense feature dataset by combining the image feature datasets with attention values of an attention map, the attention values being one or more numerical values that modify values of the dense feature dataset based at least in part on the scale of the plurality of scaled images; tracking an object depicted in the images of an image sequence using the dense feature dataset; generating a modified image sequence by applying an image effect to the object depicted in the image sequence, the image effect applied to the depiction of the object tracked in the images of the image sequence; and publishing, on a network site, the modified image sequence as an electronic message. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. A system comprising:
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one or more processors of a machine; and a memory storing instructions that, when executed by the one or more processors, cause the machine to perform operations comprising; identifying an image; generating a plurality of scaled images from the image; generating image feature datasets for the plurality of scaled images; and generating a dense feature dataset by combining the image feature datasets with attention values of an attention map, the attention values being one or more numerical values that modify values of the dense feature dataset based at least in part on the scale of the plurality of scaled images; tracking an object depicted in the images of an image sequence using the dense feature dataset; generating a modified image sequence by applying an image effect to the object depicted in the image sequence, the image effect applied to the depiction of the object tracked in the images of the image sequence; and publishing, on a network site, the modified image sequence as an electronic message. - View Dependent Claims (13, 14, 15, 16, 17, 18)
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19. A non-transitory machine-readable storage device embodying instructions that, when executed by a machine, cause the machine to perform operations comprising:
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identifying an image; generating a plurality of scaled images from the image; generating image feature datasets for the plurality of scaled images; and generating a dense feature dataset by combining the image feature datasets with attention values of an attention map, the attention values being one or more numerical values that modify values of the feature dataset based at least in part on the scale of the plurality of scaled images; and tracking an object depicted in the images of an image sequence using the dense feature dataset; generating a modified image sequence by applying an image effect to the object depicted in the image sequence, the image effect applied to the depiction of the object tracked in the images of the image sequence; and publishing, on a network site, the modified image sequence as an electronic message. - View Dependent Claims (20)
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Specification