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 and a greedy algorithm maximizing a joint probability of a plurality of hypotheses for the locations of the plurality of objects.
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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.
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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 and a greedy algorithm maximizing a joint probability of a plurality of hypotheses for the locations of the plurality of objects. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
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13. 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; applying a Hough forest to the image to obtain a plurality of voting elements, the plurality of voting elements usable to place probabilistic votes on one or more hypotheses for locations of one or more objects; deriving a probabilistic model based at least on the plurality of voting elements and the one or more hypotheses for the locations of the one or more objects, the probabilistic model combining the probabilistic votes from the plurality of voting elements on the one or more hypotheses for the locations of the one or more objects; iteratively ascertaining a hypothesis for a location of an object in the image based on the probabilistic model in response to determining that probabilistic votes on the hypothesis for the location of the object is maximum compared to other hypotheses for locations of other objects in a respective iteration. - View Dependent Claims (14, 15, 16, 17, 18, 19)
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20. One or more computer-readable media storing computer-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 one or more voting elements from the image, the one or more voting elements casting probabilistic votes on a hypothesis for a location of an object to determine whether the hypothesis is probabilistically true; deriving a probabilistic model based at least on the one or more voting elements and one or more hypotheses for locations of one or more objects; applying a greedy algorithm to iteratively assign a voting element of the one or more voting elements to a hypothesis of the one or more hypotheses in response to determining that assigning the voting element to the hypothesis increases a log-posterior probability associated with the probabilistic model; and ascertaining a location of an object in the image in response to determining that votes for a hypothesis for the ascertained location of the object are maximum among the one or more hypotheses in a respective iteration of the greedy algorithm.
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Specification