Eagle View Technologies, Inc.

Christopher Albert Ciarcia

Chris Pershing

A statistical point pattern matching technique is used to match corresponding points selected from two or more views of a roof of a building. The technique entails statistically selecting points from each of orthogonal and oblique aerial views of a roof, generating radial point patterns for each aerial view, calculating the origin of each point pattern, representing the shape of the point pattern as a radial function, and Fourier-transforming the radial function to produce a feature space plot. A feature profile correlation function can then be computed to relate the point match sets. From the correlation results, a vote occupancy table can be generated to help evaluate the variance of the point match sets, indicating, with high probability, which sets of points are most likely to match one another.

Statistical point pattern matching technique

A statistical point pattern matching technique is used to match corresponding points selected from two or more views of a roof of a building. The technique statistically selecting points from each of orthogonal and oblique aerial views of a roof. generating radial point patterns for each aerial view, calculating the origin of each point pattern, representing the shape of the point pattern as a radial function, and Fourier-transforming the radial function to produce a feature space plot. A feature profile correlation function can then be computed to relate the point match sets. From the correlation results, a vote occupancy table can be generated to help evaluate the variance of the point match sets, indicating, with high probability, which sets of points are most likely to match one another.

Systems and methods for relating images to each other by determining transform functions between the images and the three-dimensional world coordinate system of the object depicted in the image, without using image acquisition metadata are disclosed. Points of interest are independently selected on each image. An initial transform function is applied to transform the points in the plane of one image to the plane of the other image. A Gaussian Mixture Model is then iteratively applied to the points to determine a best match, which then provides adjustments to the argument values of the transform function which is again applied to the points of interest on each image. This process repeats until the argument values converge to provide the resulting transform function. The resulting function can then be used to identify objects in the images in three dimensional space.

Systems and methods for relating images to each other by determining transforms without using image acquisition metadata

Automatic roof identification systems and methods are described. Example embodiments include a roof estimation system configured to automatically detect a roof in a target image of a building having a roof. In one embodiment, automatically detecting a roof in a target image includes training one or more artificial intelligence systems to identify likely roof sections of an image. The artificial intelligence systems are trained on historical image data or an operator-specified region of interest within the target image. Then, a likely outline of the roof in the target image can be determined based on the trained artificial intelligence systems. The likely roof outline can be used to generate a roof estimate report. This abstract is provided to comply with rules requiring an abstract, and it is submitted with the intention that it will not be used to interpret or limit the scope or meaning of the claims.

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Monroe, WA, United States of America

Christopher Albert Ciarcia