Curve retraining decision system
AssigneeSchlumberger Technology Corporation
InventorsRitik Shrivastava, Omkar Anil Gune, Purnaprajna Raghavendra Mangsuli
Life of this patent
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Abstract
A method implements a curve retraining decision system. The method includes executing a frequency model using an initial image and an extracted curve image to generate a frequency model score. The method further includes executing a spatial model using the initial image and the extracted curve image to generate a spatial model score. The method further includes generating a curve retraining score for a curve segmentation model of a raster digitization engine. The method further includes retraining the curve segmentation model using the curve retraining score.
Claims20
A method comprising:
- executing a frequency model using an initial image and an extracted curve image to generate a frequency model score;
- executing a spatial model using the initial image and the extracted curve image to generate a spatial model score;
- generating a curve retraining score for a curve segmentation model of a raster digitization engine;
- and
- retraining the curve segmentation model using the curve retraining score.
The method of claim 61, wherein executing the frequency model comprises:
- executing a frequency transform module using the initial image and the extracted curve image to generate an initial frequency spectrum and a curve frequency spectrum.
The method of claim 61, wherein executing the frequency model comprises:
- executing a combination model using an initial frequency spectrum and a curve frequency spectrum to combine the curve frequency spectrum with the initial frequency spectrum and form a combined frequency spectrum.
The method of claim 61, wherein executing the frequency model comprises:
- executing a low pass filter using a combined frequency spectrum to remove frequencies above a frequency threshold from the combined frequency spectrum.
The method of claim 61, wherein executing the frequency model comprises:
- determining the frequency model score from a low frequency part of a combined frequency spectrum;
- setting the frequency model score to a first value when the low frequency part satisfies a low value threshold;
- and
- setting the frequency model score to a second value when the low frequency part does not satisfy the low value threshold.
The method of claim 61, wherein executing the spatial model comprises:
- executing a grid removal module using the initial image to generate a gridless image by:
- determining an average number of grid pixels for each coordinate axis, and
- adjusting pixels in a line along an axis having a number of grid pixels greater than a grid pixel threshold for the axis.
The method of claim 61, wherein executing the spatial model comprises:
- calculating an area of a gridless image intersected by the extracted curve image to generate an intersection value.
The method of claim 61, wherein executing the spatial model comprises:
- comparing an intersection value to an intersection threshold to determine the spatial model score.
The method of claim 61, wherein generating the curve retraining score comprises:
- combining a set of frequency model scores, comprising the frequency model score, and a set of spatial model scores, comprising the spatial model score, for a data set to generate the curve retraining score.
The method of claim 61, wherein retraining the curve segmentation model comprises:
- retraining the curve segmentation model when the curve retraining score satisfies a curve retraining threshold, wherein the curve retraining threshold is 0.9 and the curve segmentation model is retrained when the curve retraining score is below the curve retraining threshold.
A system comprising:
- at least one processor;
- and
- an application that, when executing on the at least one processor, performs operations comprising:
- executing a frequency model using an initial image and an extracted curve image to generate a frequency model score,
- executing a spatial model using the initial image and the extracted curve image to generate a spatial model score,
- generating a curve retraining score for a curve segmentation model of a raster digitization engine, and
- retraining the curve segmentation model using the curve retraining score.
The system of claim 71, wherein executing the frequency model comprises:
- executing a frequency transform module using the initial image and the extracted curve image to generate an initial frequency spectrum and a curve frequency spectrum.
The system of claim 71, wherein executing the frequency model comprises:
- executing a combination model using an initial frequency spectrum and a curve frequency spectrum to combine the curve frequency spectrum with the initial frequency spectrum and form a combined frequency spectrum.
The system of claim 71, wherein executing the frequency model comprises:
- executing a low pass filter using a combined frequency spectrum to remove frequencies above a frequency threshold from the combined frequency spectrum.
The system of claim 71, wherein executing the frequency model comprises:
- determining the frequency model score from a low frequency part of a combined frequency spectrum;
- setting the frequency model score to a first value when the low frequency part satisfies a low value threshold;
- and
- setting the frequency model score to a second value when the low frequency part does not satisfy the low value threshold.
The system of claim 71, wherein executing the spatial model comprises:
- executing a grid removal module using the initial image to generate a gridless image by:
- determining an average number of grid pixels for each coordinate axis, and
- adjusting pixels in a line along an axis having a number of grid pixels greater than a grid pixel threshold for the axis.
The system of claim 71, wherein executing the spatial model comprises:
- calculating an area of a gridless image intersected by the extracted curve image to generate an intersection value.
The system of claim 71, wherein executing the spatial model comprises:
- comparing an intersection value to an intersection threshold to determine the spatial model score.
The system of claim 71, wherein generating the curve retraining score comprises:
- combining a set of frequency model scores, comprising the frequency model score, and a set of spatial model scores, comprising the spatial model score, for a data set to generate the curve retraining score.
A non-transitory computer readable medium comprising instructions executable by at least one processor to perform operations comprising:
- executing a frequency model using an initial image and an extracted curve image to generate a frequency model score;
- executing a spatial model using the initial image and the extracted curve image to generate a spatial model score;
- generating a curve retraining score for a curve segmentation model of a raster digitization engine;
- and
- retraining the curve segmentation model using the curve retraining score.
Dependencies are inferred from the claim text.