METHOD AND APPARATUS FOR IMAGE PROCESSING
First Claim
1. A method for image processing comprising:
- training a convolutional network including multiple layers of filters to produce at least one image in succeeding layers with at least the same resolution as an original image.
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Abstract
Identifying objects in images is a difficult problem, particularly in cases an original image is noisy or has areas narrow in color or grayscale gradient. A technique employing a convolutional network has been identified to identify objects in such images in an automated and rapid manner. One example embodiment trains a convolutional network including multiple layers of filters. The filters are trained by learning and are arranged in successive layers and produce images having at least a same resolution as an original image. The filters are trained as a function of the original image or a desired image labeling; the image labels of objects identified in the original image are reported and may be used for segmentation. The technique can be applied to images of neural circuitry or electron microscopy, for example. The same technique can also be applied to correction of photographs or videos.
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Citations
31 Claims
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1. A method for image processing comprising:
training a convolutional network including multiple layers of filters to produce at least one image in succeeding layers with at least the same resolution as an original image. - View Dependent Claims (2, 3, 4, 5, 6)
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7. An image processing system comprising:
a training module to train a convolutional network including multiple layers of filters to produce at least one image in succeeding layers with at least the same resolution as an original image. - View Dependent Claims (8, 9, 10, 11, 12)
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13. A method for image processing comprising:
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training a convolutional network including multiple layers of filters trained by learning and arranged in successive layers and producing images having at least a same resolution as an original image; training the filters as a function of the original image or a desired image labeling; and reporting image labels of objects identified in the original image. - View Dependent Claims (14, 15, 16, 17, 18, 19, 20, 21)
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22. An image processing system comprising:
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a first training module to train a convolutional network including multiple layers of filters trained by learning and arranged in successive layers and producing images having at least a same resolution as an original image; a second training module to train the filters as a function of the original image or a desired image labeling; and a reporting module to report image labels of objects identified in the original image. - View Dependent Claims (23, 24, 25, 26, 27, 28, 29, 30)
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31-94. -94. (canceled)
Specification