Computer vision systems and methods for identifying anomalies in building models
AssigneeInsurance Services Office, Inc.
InventorsJose Luis Esteban, Eladio Rego
Life of this patent
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Abstract
Computer vision systems and methods for detecting anomalous building models are provided. The systems and methods can detect anomalies in building models using one or more of an independent univariate Gaussian algorithm, a multivariate Gaussian algorithm, a combination of a multivariate Gaussian algorithm for continuous features and a frequency histogram algorithm for discrete features, and/or a bin frequency model. The system automatically processes computerized models to determine anomalies, and indicates whether the models are accurate and whether correction is required.
Claims12
A method for identifying an anomaly in a computerized building model, comprising the steps of:
- receiving at a computer system the computerized building model, said computerized model representing a building;
- selecting at least one feature of the building for analysis;
- processing the at least one feature to assign a Gaussian distribution having at least one parameter to the selected at least one building feature;
- processing the at least one parameter to determine a probability density of a feature value using a sample vector of the computerized building model;
- processing the computerized building model to improve accuracy of the computerized building model using the probability density;
- further comprising calculating a mean μ and a variance σ2 from a training sample using:
- μ
- j
- =
- 1
- m
-
- ∑
- i
- =
- 1
- m
- x
- j
- [
- i
- ]
- σ
- j
- 2
- =
- 1
- m
-
- ∑
- i
- =
- 1
- m
- (
- x
- j
- [
- i
- ]
- -
- μ
- j
- )
- 2
- where m=a number of samples;
- and
- determining the computerized building model is anomalous when a multiplied probability density p({circumflex over (x)}) of each of the probability density of a mean μ feature value and the probability density of the probability density of a variance σ2 feature value is less than a probability threshold.
A system for identifying an anomaly in a building model, the system comprising:
- a memory;
- and
- a processor in communication with the memory, the processor:
- receiving at a computer system the building model;
- selecting at least one feature of the building for analysis;
- processing the at least one feature to assign a Gaussian distribution having at least one parameter to the selected at least one building feature;
- processing the at least one parameter to determine a probability density of a feature value using a sample vector of the building model;
- processing the building model to improve accuracy of the building model using the probability density;
- calculating a mean μ and a variance σ2 from a training sample using:
- μ
- j
- =
- 1
- m
-
- ∑
- i
- =
- 1
- m
- x
- j
- [
- i
- ]
- σ
- j
- 2
- =
- 1
- m
-
- ∑
- i
- =
- 1
- m
- (
- x
- j
- [
- i
- ]
- -
- μ
- j
- )
- 2
- where m=a number of samples;
- and
- determining the building model is anomalous when a multiplied probability density p({circumflex over (x)}) of each of the probability density of a mean μ feature value the probability density of a variance σ2 feature value is less than a probability threshold.
Dependencies are inferred from the claim text.