Feature extraction using pixel-level and object-level analysis
First Claim
1. In a graphical information system (GIS), a method for processing a digital image depicting one or more physical objects to locate a feature in the digital image, the method comprising the following:
- by a processor, executing a pixel-level cue algorithm to identify an interesting area of a raster image depicting the one or more physical objects;
determining a pixel-level probability that the interesting area of the raster image identified is the feature using a result from the pixel-level cue algorithm;
comparing the pixel-level probability to a pixel-level cue threshold; and
if the pixel-level probability satisfies the pixel-level cue threshold;
converting at least a portion of the raster image to a vector layer by geometric modeling using points, lines, curves, and polygons to generate a representation of digital vector objects represented by the points, lines, curves and polygons;
executing an object-level cue algorithm on the vector layer to identify an interesting area of the vector layer;
determining an object-level probability that the interesting area of the vector layer is the feature using a result of the pixel-level cue algorithm; and
comparing the object-level probability to an object-level threshold.
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Abstract
Image processing for extracting features in images. Pixel-level cue algorithms can be performed on raster images. The raster images can be converted to a vector layer. Object-level cue algorithms can be performed on the vector layer. The feature can be detected using a result of the pixel-level cues and using a result of the object-level cue algorithms performed. A computer-readable medium can include a first data field containing data representing pixel-level cues functioning to describe a pixel-level cue of the feature. The computer-readable medium can also include a second data field containing data representing object-level cues functioning to describe the object-level cues of the feature. Relation-level cue algorithms can be performed on the vector layers. The features can be detected using a result of any combination of the pixel-level cue algorithms, object-level cue algorithms, and/or relation-level cue algorithms.
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Citations
14 Claims
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1. In a graphical information system (GIS), a method for processing a digital image depicting one or more physical objects to locate a feature in the digital image, the method comprising the following:
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by a processor, executing a pixel-level cue algorithm to identify an interesting area of a raster image depicting the one or more physical objects; determining a pixel-level probability that the interesting area of the raster image identified is the feature using a result from the pixel-level cue algorithm; comparing the pixel-level probability to a pixel-level cue threshold; and if the pixel-level probability satisfies the pixel-level cue threshold; converting at least a portion of the raster image to a vector layer by geometric modeling using points, lines, curves, and polygons to generate a representation of digital vector objects represented by the points, lines, curves and polygons; executing an object-level cue algorithm on the vector layer to identify an interesting area of the vector layer; determining an object-level probability that the interesting area of the vector layer is the feature using a result of the pixel-level cue algorithm; and comparing the object-level probability to an object-level threshold. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)
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14. In a graphical information system (GIS), a method for processing a digital image depicting one or more physical objects to locate a feature in the digital image, the method comprising the following:
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by a processor, performing a pixel-level cue algorithm on a digital raster image, the raster image depicting the one or more physical objects by a set of pixels; based on the results of the pixel-level cue algorithm, generating a pixel probability layer in which each pixel'"'"'s value represents a probability that it is the feature of interest; converting the pixel probability layer into a raster object layer which contains pixels that are grouped as raster objects; for each raster object, calculating a mean probability of all of the probability pixels corresponding to each raster object; converting at least a portion of the raster object layer to a digital vector layer by geometric modeling, wherein geometric modeling uses geometric primitive including points, lines, curves and polygons to generate a representation of digital vector objects represented by the points, lines, curves and polygons, and wherein each digital vector layer object is associated with the mean probability calculated from the raster object from which the vector object was converted; performing an object-level cue algorithm on the digital vector layer representation of the one or more digital vector layer objects; and identifying a feature using a result of the pixel-level cue algorithm and a result of the object-level cue algorithm performed.
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Specification