Using pattern recognition to reduce noise in a 3D map
First Claim
1. A device comprising:
- at least one computer medium that is not a transitory signal and that comprises instructions executable by at least one processor to;
receive a digital depth map;
execute image recognition on the depth map;
based at least in part on the image recognition, identify a first object at least in part by identifying at least a segment of a continuous sequence of pixels in an image that have characteristics comparable to each other in the sequence but that are different by from characteristics of pixels nearby the sequence;
based at least in part on the image recognition, identify a second object;
identify a first noise reduction level associated with the first object;
identify a second noise reduction level associated with the second object, the first noise reduction level being different from the second level noise reduction level; and
apply the first and second noise reduction levels to respective entireties of the first and second objects.
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Accused Products
Abstract
To reduce the random noise in a depth map that is rendered using colors to convey the various depths, pattern recognition may be used to selectively apply noise reduction or to modulate the strength of the noise reduction. In this way, the potential adverse effect on the detail/sharpness of the image can be ameliorated. For example, in an image of a person, the skin does not have any sharp edges so noise reduction can be applied to such an image with little adverse consequence, whereas noise reduction applied to the image of a person'"'"'s eye can cause loss of the detail of the iris, eyelashes, etc. Using pattern recognition on objects in the image, the appropriate level of noise reduction can be applied across an image while minimizing blurring/loss of detail.
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Citations
16 Claims
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1. A device comprising:
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at least one computer medium that is not a transitory signal and that comprises instructions executable by at least one processor to; receive a digital depth map; execute image recognition on the depth map; based at least in part on the image recognition, identify a first object at least in part by identifying at least a segment of a continuous sequence of pixels in an image that have characteristics comparable to each other in the sequence but that are different by from characteristics of pixels nearby the sequence; based at least in part on the image recognition, identify a second object; identify a first noise reduction level associated with the first object; identify a second noise reduction level associated with the second object, the first noise reduction level being different from the second level noise reduction level; and apply the first and second noise reduction levels to respective entireties of the first and second objects. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. An assembly comprising:
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plural devices; each device comprising at least one range finder configured to output signals useful for generating images of other devices on the surface; at least one processor configured with instructions for selectively applying noise reduction to at least a first one of the images based on pattern recognition executed on the first image, wherein the instructions are executable to; receive a digital depth map generated by signals from at least one of the range finders; execute image recognition on the depth map; based at least in part on the image recognition, identify a first object at least in part by identifying at least a segment of a continuous sequence of pixels in an image that have characteristics similar to each other in the sequence but that are different by from characteristics of pixels nearby the sequence; based at least in part on the image recognition, identify a second object; identify a first noise reduction level associated with the first object; identify a second noise reduction level associated with the second object, the first noise reduction level being different from the second level noise reduction level; and apply the first and second noise reduction levels to entireties of the respective first and second objects. - View Dependent Claims (10, 11, 12, 13, 14, 15, 16)
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Specification