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US 9,754,186 B2Granted
Visual feature extraction methods and systems
AssigneeLeidos Innovations Technology, Inc.
InventorsWilliam John Rose, Corey Brendan Hart
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Abstract
Systems and methods for image processing are disclosed. For example, a system for processing images includes voxel-ranking circuitry that ranks individual voxels using voxel data derived from physiological responses resulting from exposure to a plurality of corresponding baseline stimulus images, and voxel selection circuitry that selects a portion of the set of ranked voxels based on their rank.
Claims19
A system for processing images, comprising:
- voxel-ranking circuitry that ranks individual voxels using voxel data derived from physiological responses resulting from exposure to a plurality of corresponding baseline stimulus images;
- voxel selection circuitry that selects a portion of the set of ranked voxels based on their rank;
- and
- filter circuitry that creates two or more filters by determining which voxels will activate maximally in response to a feature presented in one or more stimulus images, then isolating each filter, the isolation of each filter based on the following equation:
- φi=∫ri(t)Sdt=∫r i(t)Σiφi*ri(t)dt,
- where φi represents an average filter response and expresses that a signal S is constructed from a weighted linear sum of neural firing rates, ri(t) represents variance-normalized average activity in a voxel at time step t, and S represents the stimulus image.
3 more elements
A method for processing images, comprising:
- ranking individual voxels using voxel data derived from physiological responses resulting from exposure to a plurality of corresponding baseline stimulus images;
- selecting a portion of ranked voxels based on their rank, and
- creating two or more filters by determining which voxels will activate maximally in response to a feature presented in one or more stimulus images, then isolating each filter, the isolation of each filter based on the following equation:
- φi=∫ri(t)Sdt=∫ri(t)Σiφi*ri(t)dt,
- where φi represents an average filter response and expresses that a signal S is constructed from a weighted linear sum of neural firing rates, ri(t) represents variance-normalized average activity in a voxel at time step t, and S represents the stimulus image.
2 more elements
Dependencies are inferred from the claim text.