Detecting and localizing multiple objects in images using probabilistic inference
First Claim
1. A computer-implemented method comprising:
- under control of one or more processors configured with executable instructions;
receiving an image including an unknown number of objects to be detected;
obtaining a plurality of voting elements from the image, the plurality of voting elements placing votes on one or more hypotheses to determine one or more locations of one or more objects in the image;
deriving a probabilistic model based at least on the plurality of voting elements; and
ascertaining locations of a plurality of objects in the image based at least in part on the probabilistic model, the probabilistic model including a penalty factor to discourage hallucinated object detection by penalizing a number of hypotheses used to explain the unknown number of objects in the image, wherein the penalty factor increases as the number of hypotheses used to explain the unknown number of objects in the image increases.
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Accused Products
Abstract
An object detection system is disclosed herein. The object detection system allows detection of one or more objects of interest using a probabilistic model. The probabilistic model may include voting elements usable to determine which hypotheses for locations of objects are probabilistically valid. The object detection system may apply an optimization algorithm such as a simple greedy algorithm to find hypotheses that optimize or maximize a posterior probability or log-posterior of the probabilistic model or a hypothesis receiving a maximal probabilistic vote from the voting elements in a respective iteration of the algorithm. Locations of detected objects may then be ascertained based on the found hypotheses.
11 Citations
20 Claims
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1. A computer-implemented method comprising:
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under control of one or more processors configured with executable instructions; receiving an image including an unknown number of objects to be detected; obtaining a plurality of voting elements from the image, the plurality of voting elements placing votes on one or more hypotheses to determine one or more locations of one or more objects in the image; deriving a probabilistic model based at least on the plurality of voting elements; and ascertaining locations of a plurality of objects in the image based at least in part on the probabilistic model, the probabilistic model including a penalty factor to discourage hallucinated object detection by penalizing a number of hypotheses used to explain the unknown number of objects in the image, wherein the penalty factor increases as the number of hypotheses used to explain the unknown number of objects in the image increases. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. One or more computer storage media storing executable instructions that, when executed by one or more processors, cause the one or more processors to perform acts comprising:
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receiving an image including an unknown number of objects to be detected; obtaining a plurality of voting elements from the image, the plurality of voting elements placing votes on one or more hypotheses to determine one or more locations of one or more objects in the image; deriving a probabilistic model based at least on the plurality of voting elements; and ascertaining locations of a plurality of objects in the image based at least in part on the probabilistic model, the probabilistic model including a penalty factor to discourage hallucinated object detection by penalizing a number of hypotheses used to explain the unknown number of objects in the image, wherein the penalty factor increases as the number of hypotheses used to explain the unknown number of objects in the image increases. - View Dependent Claims (13, 14, 15, 16)
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17. A system comprising:
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one or more processors; memory storing executable instructions that, when executed by the one or more processors, cause the one or more processors to perform acts comprising; receiving an image including an unknown number of objects to be detected; obtaining a plurality of voting elements from the image, the plurality of voting elements placing votes on one or more hypotheses to determine one or more locations of one or more objects in the image; deriving a probabilistic model based at least on the plurality of voting elements; and ascertaining locations of a plurality of objects in the image based at least in part on the probabilistic model, the probabilistic model including a penalty factor to discourage hallucinated object detection by penalizing a number of hypotheses used to explain the unknown number of objects in the image, wherein the penalty factor increases as the number of hypotheses used to explain the unknown number of objects in the image increases. - View Dependent Claims (18, 19, 20)
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Specification