Cognitive method for visual classification of very similar planar objects
First Claim
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1. An apparatus for cognitive visual recognition, the apparatus comprising:
- a memory device comprising a list of candidate templates;
an optical recording device configured to capture a digital query image and store the digital query image on the memory device; and
a processor coupled to the memory device and configured to;
retrieve the digital query image from the memory device,retrieve the list of candidate templates from the memory device,align each candidate template in the list of candidate templates with the digital query image,select a set of sample points of each candidate template in the list of candidate templates,measure a mutual saliency of the set of sample points,select a set of highest mutual saliency points from the set of sample points based on the mutual saliency,measure the similarity of each candidate template in the list of candidate templates to the query image at the set of highest mutual saliency points, andgenerate a list of highest similarity candidates based on the measured similarity.
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Abstract
A cognitive system and method for visual classification of similar planar objects is disclosed. The method uses a query image and a list of candidate templates as the input, and produces the most probable candidate for the query image. The system uses the mutual saliency among a sample of points in the query templates, and selects those points with the highest saliency. The corresponding candidate templates to the points with the highest saliency are then compared to the query image, and those with the highest similarity are kept. The system has applications to industrial and commercial settings where processes require object recognition from image data.
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Citations
20 Claims
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1. An apparatus for cognitive visual recognition, the apparatus comprising:
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a memory device comprising a list of candidate templates; an optical recording device configured to capture a digital query image and store the digital query image on the memory device; and a processor coupled to the memory device and configured to; retrieve the digital query image from the memory device, retrieve the list of candidate templates from the memory device, align each candidate template in the list of candidate templates with the digital query image, select a set of sample points of each candidate template in the list of candidate templates, measure a mutual saliency of the set of sample points, select a set of highest mutual saliency points from the set of sample points based on the mutual saliency, measure the similarity of each candidate template in the list of candidate templates to the query image at the set of highest mutual saliency points, and generate a list of highest similarity candidates based on the measured similarity. - View Dependent Claims (2, 3, 4, 5)
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6. An apparatus for cognitive visual recognition, the apparatus comprising:
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a memory storage device comprising a list of candidate templates; and a digital imager configured to; capture a digital image, and store the digital image on the memory storage device; and a processor configured to; receive the digital image, receive the list of candidate templates, align each candidate template in the list of candidate templates with the digital image, select a set of sample points of each candidate template in the list of candidate templates and measure their mutual saliency, select a set of highest mutual saliency points from the set of sample points, measure the similarity of each candidate template in the list of candidate templates to the image at the set of highest mutual saliency points, and remove at least a lowest similarity candidate from the list of candidate templates. - View Dependent Claims (7, 8, 9, 10)
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11. A method for cognitive visual recognition comprising:
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a. receiving a digital query image from a digital imaging device; b. receiving a list of candidate templates; c. aligning the list of candidate templates with the digital query image; d. selecting a set of sample points in the list of candidate templates and measuring their mutual saliency; e. selecting a set of highest mutual saliency points from the set of sample points; f. measuring a similarity of each candidate template in the list of candidate template to the digital query image at the set of highest mutual saliency points; and g. generating a list of highest similarity candidates based on the measured similarity. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18, 19, 20)
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Specification