Method of image processing using a neural network
A method of generating landmark locations for an image crop comprises: processing the crop through an encoder-decoder to provide a plurality of N output maps of comparable spatial resolution to the crop, each output map corresponding to a respective landmark of an object appearing in the image crop; processing an output map from the encoder through a plurality of feed forward layers to provide a feature vector comprising N elements, each element including an (x,y) location for a respective landmark. Any landmarks locations from the feature vector having an x or a y location outside a range for a respective row or column of the crop are selected for a final set of landmark locations; with remaining landmark locations tending to be selected from the N (x,y) landmark locations from the plurality of N output maps.
1. A method comprising:
identifying an object within an image;
generating a crop comprising at least a portion of said object;
processing said crop by one or more convolutional layers to provide an output map of lower spatial resolution than said crop;
processing said output map by one or more de-convolutional layers to provide N output maps of comparable spatial resolution to said crop, each N output map of said N output maps corresponding to a respective landmark of said object;
obtaining N landmark locations from said N output maps output by the one or more de-convolutional layers;
processing said output map by one or more layers different from the one or more de-convolutional layers to provide a feature vector comprising (x,y) locations for multiple landmarks;
selecting a first set of landmark locations from the multiple landmarks of said feature vector, at least some of the first set of landmark locations being outside a boundary of said crop; and
selecting a second set of landmark locations from said N landmark locations associated with the N output maps.
2. The method according to claim 1 , wherein the second set of landmark locations represents locations comprising distortion relative to the first set of landmark locations.
3. The method according to claim 1 , wherein the first set of landmark locations selected from said feature vector do not comprise distortion relative to said crop.
4. The method according to claim 1 , further comprising processing said output map by one or more feed forward layers to provide a classification of at least one of: pitch, yaw, or roll of said object within said crop.
5. The method according to claim 1 , wherein said one or more convolutional layers and said one or more de-convolutional layers are associated with a single stage encoder-decoder.
6. The method according to claim 1 , wherein said output map from a first convolutional layer is aggregated with an output map of said N output maps from a first de-convolutional layer to provide an input map for a second convolutional layer.
7. The method according to claim 1 , wherein said object comprises a face.
8. The method according to claim 1 , wherein said crop comprises a range of 64×64 pixels.
9. The method according to claim 1 , wherein processing said crop or processing said output map comprises execution by at least one of: a general-purpose processor; a multi-core processor; a dedicated neural network processing engine; or a multi-core neural network processing engine.
10. The method of claim 1 , wherein the output map comprises a plurality of channels.
11. The method of claim 1 , further comprising feeding said N output maps and said feature vector through a set of neural network layers to provide said first set of landmark locations or said second set of landmark locations.
12. A system comprising:
one or more processors; and
one or more non-transitory computer-readable media storing instructions executable by the one or more processors, wherein the instructions, when executed, cause the system to perform operations comprising:
identifying an object within an image;
generating a crop comprising at least a portion of said object;
processing said crop by one or more convolutional layers to provide an output map of lower spatial resolution than said crop;
processing said output map by one or more de-convolutional layers to provide N output maps having substantially similar resolution to said crop, at least one N output map of said N output maps corresponding to a landmark of said object;
obtaining N landmark locations from said N output maps output by the one or more de-convolutional layers;
processing said output map by one or more layers different from the one or more de-convolutional layers to provide a feature vector comprising (x, y) locations for multiple landmarks;
selecting a first set of landmark locations from the multiple landmarks of said feature vector, at least some of the first set of landmark locations being outside a boundary of said crop; and
selecting a second set of the landmark locations from said N landmark locations associated with the N output maps.
13. The system of claim 12 , the operations further comprising:
training a neural network based at least in part on the first set of landmark locations and the second set of landmark locations.
14. The system of claim 12 , the operations further comprising:
generating, by a neural network, an additional crop associated with an additional image based at least in part on the first set of landmark locations and the second set of landmark locations.
15. The system of claim 12 , wherein the second set of landmark locations represents locations comprising distortion relative to the first set of landmark locations.
16. The system of claim 12 , wherein the first set of landmark locations selected from said feature vector do not comprise distortion relative to said crop.
17. The system of claim 12 , wherein said object comprises a face.
18. The system of claim 12 , wherein said one or more convolutional layers and said one or more de-convolutional layers are associated with a single stage encoder-decoder.
19. The method of claim 1 , further comprising:
training a neural network based at least in part on the first set of landmark locations and the second set of landmark locations.
20. The method of claim 1 , further comprising:
generating, by a neural network, an additional crop associated with an additional image based at least in part on the first set of landmark locations and the second set of landmark locations.