1. A method for measuring risk associated with a behavioural activity, the method comprising:
a) determining a first risk component associated with one or more persons involved in performing the activity;
b) determining a second risk component associated with sensitivity of one or more assets comprising data associated with the risk;
c) determining a third risk component associated with an endpoint which receives said one or more assets due to the activity;
d) determining a fourth risk component associated with a type of the activity; and
e) measuring the risk as a function of at least one of the first risk component, the second risk component, the third risk component, and the fourth risk component.
2. The method of claim 1, further comprising automatically tuning parameters of the function.
3. The method of claim 2, wherein said parameters include one or more weighting factors each multiplying a respective one of the first risk component, the second risk component, the third risk component, and the fourth risk component.
4. The method of claim 1, further comprising manually tuning parameters of the function.
5. A method for measuring risk associated with a behavioural activity, the method comprising:
a) obtaining one or more probabilities, each probability associated with a respective potential undesired event and each probability being a conditional probability given the observed activity;
b) optionally adjusting each of said probabilities by multiplication with a respective probability weighting factor;
c) for each potential undesired event, obtaining one or more entity costs, each entity cost representative of a contribution to said risk associated with a given type of entity associated with the activity;
d) for each potential undesired event, determining a resultant cost as a function of said entity costs; and
e) measuring the risk as an expectation over the one or more resultant costs distributed over the associated probabilities of potential undesired events.
6. The method according to claim 5, wherein a single nonzero event risk value is determined and a single corresponding conditional probability of said event given the behaviour is obtained.
7. The method according to claim 5, wherein each of the probability weighting factors are bounded between zero and one, inclusive.
8. The method according to claim 5, wherein the probability weighting factor is associated with one or both of the behaviour and the event corresponding to the conditional probability being adjusted by multiplication with said probability weighting factor.
9. The method according to claim 5, wherein the function of entity costs associated with said event is a weighted or unweighted average of entity costs associated with said event.
10. The method according to claim 9, wherein the average of said entity costs is a weighted average, and wherein each weighting factor associated with the weighted average is bounded between zero and one, inclusive.
11. The method according to claim 5, wherein said probability weighting factor is defined automatically, defined via user input, or a combination thereof.
12. The method according to claim 5, wherein the given type of entity corresponds to a set of persons interacting with data to potentially be leaked, an asset comprising said data to potentially be leaked, and an endpoint to which said data to potentially be leaked is transferred.
13. The method according to claim 5, wherein determining at least one of the entity costs comprises:
a) obtaining a set of entities of the given type of entity, each of said set of entities associated with the activity;
b) obtaining a set of sub-costs, each sub-cost associated with a member of the set of entities;
c) determining a weighted sum of the set of sub-costs.
14. The method according to claim 13, wherein each sub-cost is weighted by a weighting factor equal to 2\u2212i, where i corresponds to the ordinal position of said sub-cost relative to the set of sub-costs when the set of sub-costs is sorted in order of descending value.
15. A method for measuring risk associated with data files within a population, the method comprising:
a) initializing risk scores of the data files based on a rule set;
b) adjusting the risk scores in response to ongoing interaction with the data files;
c) identifying commonalities across data files; and
d) at least partially propagating risk scores between data files based on said identified commonalities.
16. The method of claim 15, wherein adjusting the risk scores in response to ongoing interaction with the data files is based on operator input indicative of risk, events associated with interaction with said data files, or a combination thereof.
17. The method of claim 16, wherein propagating risk scores between data files comprises propagating adjustments to the risk score from a first data file to a second data file bearing a similarity to the first data file.
18. The method of claim 15, wherein propagating risk scores between data files is performed in response to data flow between data files.
19. The method of claim 15, wherein initialized risk scores are based on one or more of: file type, file location, file author, file owner, file user, filename patterns, document metadata, and keywords located in the file.
20. The method of claim 15, further comprising adjusting the rule set based on adjustments to the risk scores as performed in at least some of (b) to (d).
21. A method for measuring risk associated with persons within a population, the method comprising:
a) initializing risk scores of said persons based on a rule set;
b) adjusting the risk scores in response to ongoing monitoring of events associated with activities of said persons;
c) identifying commonalities across said persons within the population; and
d) at least partially propagating risk scores between said persons based on said identified commonalities.
22. The method of claim 21, wherein adjusting the risk scores in response to ongoing interaction with the data files is based on operator input indicative of risk, events associated with interaction with said persons, or a combination thereof.
23. The method of claim 21, wherein propagating risk scores between persons is performed in response to interactions between said persons.
24. The method of claim 23, wherein propagating risk scores between persons comprises:
a) adjusting the risk score of an identified person based on operator input indicative of risk, events associated with activities of said person, or a combination thereof; and
b) at least partially propagating the risk score associated with a first person to a second person in response to interaction between the first person and the second person.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1. A computerized system for displaying, geolocating, and making measurements, comprising:
a computer system executing image display and analysis software reading:
an oblique image having corresponding location data indicative of a position and orientation of an image capturing device used to capture the oblique image, the oblique image depicting an object of interest; and
at least one data table storing ground plane data indicative of a plurality of first facets that closely approximates at least a portion of the terrain depicted within said oblique image, said at least one data table also comprising a TGP vertical plane data indicative of a second facet representing a mathematical model of the object of interest depicted within the oblique image,
wherein the image display and analysis software executed by the computer system displays at least a portion of the oblique image depicting the object of interest, receives one or more selected points within the oblique image on the object of interest and calculates a measurement of the object of interest using pixel location of the one or more selected points within the oblique image, the location data and the TGP vertical plane data.
2. The computerized system of claim 1, wherein the image display and analysis software is configured to utilize a single ray projection technique to calculate the measurement of the object of interest using the geo-location data and the TGP vertical plane data.
3. The computerized system of claim 1, wherein the image display and analysis software is configured to calculate a desired measurement within the oblique image utilizing the geo-location data and the ground plane data indicative of the plurality of first facets.
4. The computerized system of claim 1, wherein the TGP vertical plane data includes real-world three-dimensional location values representative of at least two points on the object of interest depicted in the oblique image and positioned at a distance farthest from a centerline of the object of interest.
5. The computerized system of claim 4, wherein the TGP vertical plane data includes at one real-world three-dimensional location value representative of a three-dimensional location where the object of interest over lies the Earth and having an elevation value indicative of an elevation of the terrain underneath the object of interest.
6. The computerized system of claim 1, wherein the object of interest is a utility tower.
7. The computerized system of claim 1, wherein the image display and analysis software executed by the computer system displays at least a portion of the oblique image depicting the object of interest by providing a webpage that when rendered by a processor executing a Web browser displays at least a portion of the oblique image depicting the object of interest.
8. A method for taking measurements within a displayed oblique image, comprising:
receiving one or more signal indicative of first selection and pixel location of a first pixel within the displayed image of a first point on an object of interest depicted within the displayed oblique image;
retrieving from a data file, location data indicative of a position and orientation of an image capturing device used to capture the displayed image, and a TGP vertical plane approximating a center of mass of the object of interest; and
determining a real-world location of the first point utilizing the pixel location within the oblique image, the location data and the TGP vertical plane data.
9. An automated method of creating three dimensional lidar data, comprising:
capturing images of a geographic area with one or more image capturing devices as well as location and orientation data for each of the images corresponding to the location and orientation of the one or more image capturing devices capturing the images, the images depicting an object of interest;
capturing three-dimensional lidar data of the geographic area with one or more lidar system such that the three-dimensional data includes the object of interest;
storing the three-dimensional lidar data on a non-transitory computer readable medium;
analyzing the images with a computer system to determine the three dimensional location of points on the object of interest; and
updating the three-dimensional lidar data with the three dimensional location of points on the object of interest determined by analyzing the images.
10. The automated method of claim 9, wherein the one or more image capturing devices and the one or more lidar system are mounted to an airplane, and wherein capturing images and three-dimensional lidar data are defined further as flying over the geographic area.
11. The automated method of claim 9, wherein the object of interest includes a utility tower, and wherein the step of analyzing the images is defined further as utilizing GIS data of a utility network to assist in locating the object of interest within one or more images.
12. The automated method of claim 9, wherein the object of interest includes a utility tower, and wherein the step of analyzing the images is defined further as scanning the images with an edge detection algorithm to locate utility wires depicted within the images, prior to determining three dimensional location of points on the utility tower.
13. A method for analyzing a utility network comprising:
capturing images of a geographic area encompassing at least a portion of the utility network with one or more image capturing devices, the images including utility wires and utility towers having crossbars as well as location and orientation data for each of the images corresponding to the location and orientation of the one or more image capturing devices capturing the image; and
analyzing at least one of the images with a computer system running a utility network detection algorithm with a gabor filter to identify pixel locations within the at least one image of cross-bars depicted within the images.
14. The method of claim 13, wherein the gabor filter is a first gabor filter having a first longitudinal axis, the pixel locations are first pixel locations, and wherein analyzing further comprises analyzing the at least one of the images with the computer system running the utility network detection algorithm with a second gabor filter having a second longitudinal axis substantially aligned with the utility wires depicted in the at least one image to identify second pixel locations within the at least one of the images of the utility wires depicted within the at least one of the images, wherein the first longitudinal axis extends within a range between 85 to 95 degrees relative to the second longitudinal axis.
15. The method of claim 14, further comprising the step of converting the first and second pixel locations to real-world three dimensional coordinates using the first and second pixel locations and the location and orientation data corresponding to the location and orientation of the one or more image capturing devices capturing the images and storing the real-world three dimensional coordinates within a three-dimensional model of the utility network.
16. The method of claim 15, wherein the three-dimensional model of the utility network is a Method 1 structure model.