Active view planning by deep learning
First Claim
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1. A computer-implemented method comprising:
- receiving, by a computing device, a first image;
performing, by the computing device, recognition on the first image using a deep neural network;
determining, by the computing device, a probability of recognition for an object based on performing the recognition on the first image, the probability of recognition for the object identifying an extent of certainty about the image including the object and being captured at a first viewpoint;
determining, by the computing device, whether the probability of recognition for the object satisfies a predetermined threshold;
responsive to determining that the probability of recognition for the object does not satisfy the predetermined threshold, determining, by the computing device, a first expected gain in the probability of recognition when a first action is taken and a second expected gain in the probability of recognition when a second action is taken, the first action and the second action belonging to a set of actions describing receiving a second image for increasing the probability of recognition; and
identifying a next action from the first action and the second action based on an increase in expected gains.
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Abstract
A system and method that identifies an object and a viewpoint from an image with a probability that satisfies a predefined criterion is described. An active view planning application receives a first image, performs recognition on the first image to determine an object, a viewpoint and a probability of recognition, determines a first expected gain in the probability of recognition when a first action is taken and a second expected gain in the probability of recognition when a second action is taken, and identifies a next action from the first action and the second action based on an increase in expected gains.
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Citations
20 Claims
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1. A computer-implemented method comprising:
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receiving, by a computing device, a first image; performing, by the computing device, recognition on the first image using a deep neural network; determining, by the computing device, a probability of recognition for an object based on performing the recognition on the first image, the probability of recognition for the object identifying an extent of certainty about the image including the object and being captured at a first viewpoint; determining, by the computing device, whether the probability of recognition for the object satisfies a predetermined threshold; responsive to determining that the probability of recognition for the object does not satisfy the predetermined threshold, determining, by the computing device, a first expected gain in the probability of recognition when a first action is taken and a second expected gain in the probability of recognition when a second action is taken, the first action and the second action belonging to a set of actions describing receiving a second image for increasing the probability of recognition; and identifying a next action from the first action and the second action based on an increase in expected gains. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A system comprising:
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one or more processors; and a memory, the memory storing instructions, which when executed cause the one or more processors to; receive a first image; perform recognition on the first image using a deep neural network; determine a probability of recognition for an object based on performing the recognition on the first image, the probability of recognition for the object identifying an extent of certainty about the image including the object and being captured at a first viewpoint; determine whether the probability of recognition for the object satisfies a predetermined threshold; responsive to determining that the probability of recognition for the object does not satisfy the predetermined threshold, determine a first expected gain in the probability of recognition when a first action is taken and a second expected gain in the probability of recognition when a second action is taken, the first action and the second action belonging to a set of actions describing receiving a second image for increasing the probability of recognition; and identify a next action from the first action and the second action based on an increase in expected gains. - View Dependent Claims (10, 11, 12, 13, 14, 15, 16)
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17. A computer program product comprising a non-transitory computer readable medium storing a computer readable program, wherein the computer readable program when executed causes a computer to:
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receive a first image; perform recognition on the first image using a deep neural network; determine a probability of recognition for an object based on performing the recognition on the first image, the probability of recognition for the object identifying an extent of certainty about the image including the object and being captured at a first viewpoint; determine whether the probability of recognition for the object satisfies a predetermined threshold; responsive to determining that the probability of recognition for the object does not satisfy the predetermined threshold, determine a first expected gain in the probability of recognition when a first action is taken and a second expected gain in the probability of recognition when a second action is taken, the first action and the second action belonging to a set of actions describing receiving a second image for increasing the probability of recognition; and identify a next action from the first action and the second action based on an increase in expected gains. - View Dependent Claims (18, 19, 20)
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Specification