Method and apparatus for image processing
First Claim
1. A method for image processing comprising:
- training a convolutional network defined by nodes and edges, between nearest neighbor nodes, that form a lattice of multiple layers of filters arranged in successive layers of filters to produce at least one image in the successive layers of filters with at least a same resolution as an original image, wherein images at nodes of a given layer of the successive layers compose a given image of the given layer as a function of images at nodes of a previous layer convolved with corresponding filters of the previous layer, the previous layer immediately preceding the given layer in the convolutional network, the training including partitioning an affinity graph, representing affinities between affinity nodes of an output of the convolutional network, by cutting affinity edges with weak affinity to create clusters of affinity nodes that correspond to different image segments, the training further including cutting edges between nodes on boundaries of the edges.
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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
33 Claims
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1. A method for image processing comprising:
training a convolutional network defined by nodes and edges, between nearest neighbor nodes, that form a lattice of multiple layers of filters arranged in successive layers of filters to produce at least one image in the successive layers of filters with at least a same resolution as an original image, wherein images at nodes of a given layer of the successive layers compose a given image of the given layer as a function of images at nodes of a previous layer convolved with corresponding filters of the previous layer, the previous layer immediately preceding the given layer in the convolutional network, the training including partitioning an affinity graph, representing affinities between affinity nodes of an output of the convolutional network, by cutting affinity edges with weak affinity to create clusters of affinity nodes that correspond to different image segments, the training further including cutting edges between nodes on boundaries of the edges. - View Dependent Claims (2, 3, 4, 5, 6, 33)
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7. An image processing system comprising:
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a memory; and a processor, the processor being configured to execute; a training module, stored in the memory, to train a convolutional network defined by nodes and edges, between nearest neighbor nodes, that form a lattice of multiple layers of filters arranged in successive layers of filters to produce at least one image in the successive layers of filters with at least a same resolution as an original image, wherein images at nodes of a given layer of the successive layers compose a given image of the given layer as a function of images at nodes of a previous layer convolved with corresponding filters of the previous layer, the previous layer immediately preceding the given layer in the convolutional network, the training module including a partitioning module configured to partition an affinity graph, representing affinities between affinity nodes of an output of the convolutional network, by cutting affinity edges with weak affinity to create clusters of affinity nodes that correspond to different image segments and by cutting edges between nodes on boundaries of the edges. - 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 defined by nodes and edges, between nearest neighbor nodes, that form a lattice of multiple layers of filters trained by learning and arranged in successive layers of filters to produce images in the successive layers of filters with at least a same resolution as an original image, wherein images at nodes of a given layer of the successive layers compose a given image of the given layer as a function of images at nodes of a previous layer convolved with corresponding filters of the previous layer, the previous layer immediately preceding the given layer in the convolutional network, the training including partitioning an affinity graph, representing affinities between affinity nodes of an output of the convolutional network, by cutting affinity edges with weak affinity to create clusters of affinity nodes that correspond to different image segments, the training further including cutting edges between nodes on boundaries of the edges; 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:
a processor, the processor being configured to execute; a first training module to train a convolutional network defined by nodes and edges, between nearest neighbor nodes, that form a lattice of multiple layers of filters trained by learning and arranged in successive layers of filters to produce at least one image in the successive layers of filters with at least a same resolution as an original image, wherein images at nodes of a given layer of the successive layers compose a given image of the given layer as a function of images at nodes of a previous layer convolved with corresponding filters of the previous layer, the previous layer immediately preceding the given layer in the convolutional network, the training including partitioning an affinity graph, representing affinities between affinity nodes of an output of the convolutional network, by cutting affinity edges with weak affinity to create clusters of affinity nodes that correspond to different image segments, the training further including cutting edges between nodes on boundaries of the edges; 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. A non-transitory computer-readable medium with computer instructions stored thereon, the computer instructions when executed by a processor cause an apparatus to:
train a convolutional network including defined by nodes and edges, between nearest neighbor nodes, that form a lattice of layers of filters arranged in successive layers of filters to produce at least one image in the successive layers of filters with at least a same resolution as an original image, wherein images at nodes of a given layer of the successive layers compose a given image of the given layer as a function of images at nodes of a previous layer convolved with corresponding filters of the previous layer, the previous layer immediately preceding the given layer in the convolutional network, the training including partitioning an affinity graph, representing affinities between affinity nodes of an output of the convolutional network, by cutting affinity edges with weak affinity to create clusters of affinity nodes that correspond to different image segments, the training further including cutting edges between nodes on boundaries of the edges.
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32. A non-transitory computer-readable medium with computer instructions stored thereon, the computer instructions when executed by a processor cause an apparatus to:
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train a convolutional network defined by nodes and edges, between nearest neighbor nodes, that form a lattice of multiple layers of filters trained by learning and arranged in successive layers of filters to produce at least one image in the successive layers of filters with at least a same resolution as an original image, wherein images at nodes of a given layer of the successive layers compose a given image of the given layer as a function of images at nodes of a previous layer convolved with corresponding filters of the previous layer, the previous layer immediately preceding the given layer in the convolutional network, the training including partitioning an affinity graph, representing affinities between affinity nodes of an output of the convolutional network, by cutting affinity edges with weak affinity to create clusters of affinity nodes that correspond to different image segments, the training further including cutting edges between nodes on boundaries of the edges; train the filters as a function of the original image or a desired image labeling; and report image labels of objects identified in the original image.
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Specification