FRAME FIELD GENERATION FOR ENHANCED IMAGE FEATURE EXTRACTION

1 . A method for generating shapes corresponding to features in an image, comprising:

receiving image data, a 2-dimensional array of coordinates corresponding to edges of one or more features in the image data, and a list of path descriptions indicating how points in the 2-D array of coordinates are connected to form a preliminary skeleton;

encoding extracted image line data from the image data as complex coefficients;

interpolating the complex coefficients across the entire image in the image data using the extracted image line data and preliminary skeleton line data corresponding to the preliminary skeleton thereby generating a frame field; and

feeding the frame field to an optimization processor.

2 . The method of claim 1 , further comprising:

determining a probability loss by calculating an absolute difference between the path probability and 0.5;

calculating a length loss for edge length, wherein the length loss represents a difference between the lengths of an edge and its mean;

determining a frame field (FF) loss by using the frame field function to calculate the FF loss;

calculating a turn loss by evaluating angles between coincident edges;

calculating the distance loss of a current position of the coordinates of the 2-D array of coordinates relative to original positions of the 2-D array of coordinates; and

determining one or more best-fit paths that minimize a combination of the probability loss, the length loss, the frame field loss, the turn loss, and the distance loss, thereby generating one or more optimized paths; and

simplifying the one or more optimized paths by reducing the number of points or vertices.

3 . The method of claim 1 , further comprising:

converting each preliminary skeleton into vectors, thereby creating a set of closed shapes that represent one or more building footprints or polylines that represent road centerlines.

4 . The method of claim 1 , wherein feeding the frame field to the optimization processor causes the optimization processor to minimize an Edge Energy function.

5 . The method of claim 3 , further comprising:

filtering the closed shapes based on a mean segmentation value to include a set of closed shapes that meet a predetermined threshold.

6 . The method of claim 3 , further comprising:

generating a set of vectors with associated confidence values.

7 . The method of claim 3 , further comprising:

converting the preliminary skeleton into one or more polygons;

applying a filter to the one or more polygons based on mean segmentation value representing a level of confidence or likelihood that a given polygon represents a building;

generating vectorized building footprints or road centerlines corresponding to the one or more polygons or polylines, respectively;

associating confidence values, such as the mean and standard deviation of the segmentation values, with each vector; and

storing in a data store vectors with the corresponding confidence values.

8 . A system for generating shapes corresponding to features in an image, comprising:

a memory storage and a processing unit coupled to the memory storage, wherein the processing unit is operative to:

receive image data, a 2-dimensional array of coordinates corresponding to edges of one or more features in the image data, and a list of path descriptions indicating how points in the 2-D array of coordinates are connected to form a preliminary skeleton;

encode extracted image line data from the image data as complex coefficients;

interpolate the complex coefficients across the entire image in the image data using the extracted image line data and preliminary skeleton line data corresponding to the preliminary skeleton thereby generating a frame field; and

feed the frame field to an optimization processor.

9 . The system of claim 8 , the processing unit being further operative to:

determine a probability loss by calculating an absolute difference between the path probability and 0.5;

calculate a length loss for edge length, wherein the length loss represents a difference between the lengths of an edge and its mean;

determine a frame field (FF) loss by using the frame field function to calculate the FF loss;

calculate a turn loss by evaluating angles between coincident edges;

calculate the distance loss of a current position of the coordinates of the 2-D array of coordinates relative to original positions of the 2-D array of coordinates; and

determine one or more best-fit paths that minimize a combination of the probability loss, the length loss, the frame field loss, the turn loss, and the distance loss, thereby generating one or more optimized paths; and

simplify the one or more optimized paths by reducing the number of points or vertices.

10 . The system of claim 8 , the processing unit being further operative to:

convert each preliminary skeleton into polygons, thereby creating a set of closed shapes that represent one or more building footprints or polylines that represent road centerlines.

11 . The system of claim 1 , the optimization processor is configured to minimize an Edge Energy function.

12 . The system of claim 10 , the processing unit being further operative to:

filter the closed shapes based on a mean segmentation value to include a set of closed shapes that meet a predetermined threshold.

13 . The system of claim 10 , the processing unit being further operative to:

generate a set of vectors with associated confidence values.

14 . The system of claim 10 , the processing unit being further operative to:

convert the preliminary skeleton into one or more polygons;

apply a filter to the one or more polygons based on mean segmentation value representing a level of confidence or likelihood that a given polygon represents a building;

generate vectorized building footprints or road centerlines corresponding to the one or more polygons or polylines, respectively;

associate confidence values, such as the mean and standard deviation of the segmentation values, with each vector; and

store in a data store vectors with the corresponding confidence values.

15 . A non-transitory computer-readable medium having stored thereon one or more sequences of instructions for causing one or more processors to perform:

receiving image data, a 2-dimensional array of coordinates corresponding to edges of one or more features in the image data, and a list of path descriptions indicating how points in the 2-D array of coordinates are connected to form a preliminary skeleton;

encoding extracted image line data from the image data as complex coefficients;

interpolating the complex coefficients across the entire image in the image data using the extracted image line data and preliminary skeleton line data corresponding to the preliminary skeleton thereby generating a frame field; and

feeding the frame field to an optimization processor.

16 . The non-transitory computer-readable medium of claim 15 , further having stored thereon a sequence of instructions for causing the one or more processors to perform:

determining a probability loss by calculating an absolute difference between the path probability and 0.5;

calculating a length loss for edge length, wherein the length loss represents a difference between the lengths of an edge and its mean;

determining a frame field (FF) loss by using the frame field function to calculate the FF loss;

calculating a turn loss by evaluating angles between coincident edges;

calculating the distance loss of a current position of the coordinates of the 2-D array of coordinates relative to original positions of the 2-D array of coordinates;

determining one or more best-fit paths that minimize a combination of the probability loss, the length loss, the frame field loss, the turn loss, and the distance loss, thereby generating one or more optimized paths; and

simplifying the one or more optimized paths by reducing the number of points or vertices.

17 . The non-transitory computer-readable medium of claim 15 , further having stored thereon a sequence of instructions for causing the one or more processors to perform:

converting each preliminary skeleton into vectors, thereby creating a set of closed shapes that represent one or more building footprints or polylines that represent road centerlines.

18 . The non-transitory computer-readable medium of claim 15 , further having stored thereon a sequence of instructions for causing the one or more processors to perform, wherein feeding the frame field to the optimization processor causes the optimization processor to minimize an Edge Energy function.

19 . The non-transitory computer-readable medium of claim 17 , further having stored thereon a sequence of instructions for causing the one or more processors to perform:

filtering the closed shapes based on a mean segmentation value to include a set of closed shapes that meet a predetermined threshold.

20 . The non-transitory computer-readable medium of claim 17 , further having stored thereon a sequence of instructions for causing the one or more processors to perform:

converting the preliminary skeleton into one or more vectors;

applying a filter to the one or more vectors based on mean segmentation value representing a level of confidence or likelihood that a given vector represents a feature (building or road);

generating vectorized building footprints or road centerlines corresponding to the one or more polygons or polylines, respectively;

associating confidence values, such as the mean and standard deviation of the segmentation values, with each vector; and

storing in a data store vectors with the corresponding confidence values.