Not Applicable.
Not Applicable.
Not Applicable.
Technology advancements within the three-dimensional modeling industry are providing avenues for physical simulation of real-life and hypothetical situations on computer systems. These models can provide valuable information for strategic and tactical planning. For example, three-dimensional models of city streets can provide first responders information regarding current city developments including entryway locations, building recognition, and the like. This information is valuable in reducing response time during emergency conditions. Further, emergency personal can train for emergency situations through simulated scenarios provided by or with the three-dimensional models.
Currently within the art, oblique images obtained from aerial photographs are used to provide close-to-accurate representations of each building's surface within the three-dimensional model. However, generally, it is difficult to obtain these oblique images with unobstructed views of the building's surface. For instance, most buildings in downtown regions of a major metropolitan city are close in proximity to one another. It becomes burdensome and time consuming to capture aerial images of each building without an obstruction, such as a neighboring building. Further, finding a single image without obstructions from the thousands of aerial images obtained, would be extremely time consuming and cost-prohibitive and may require three-dimensional modeling of all obstructing structures.
Some three-dimensional models edit the obstructed portion of the oblique image by approximating the building's surface using other portions of the same building. Although this method does provide a representation of the building within the three-dimensional model, the representation goes on the assumption that all portions of the building are created equal. However, this assumption is problematic as an obstructed area may have uniquely placed doorways and/or windows that may be ignored by the approximation.
Alternatively, a user can manually manipulate two or more oblique images to form a single image having an unobstructed view of the façade. This type of manual manipulation is slow and tedious, and requires experience and expertise in the modeling field.
In one embodiment, the present invention is related to a method of automatically generating a three-dimensional model of a structure. The three-dimensional model of the structure preferably includes real façade textures obtained from geo-referenced oblique images. The geographical position of the real façade texture is provided using wire-frame data. A base oblique image is selected from geo-referenced oblique images using the geographical positions obtained from the wire-frame data. The base oblique image is selected from the geo-referenced oblique images based on analysis of the image raster content of the real façade texture, and the real façade texture of the base oblique image is then applied to the three-dimensional model.
In one version, the real façade texture is analyzed to locate at least one occlusion. Occlusions can include obstructions in the view of the real façade texture such as an overlapping building, image distortions within the base oblique images, and/or the like. Occlusions may be located using pattern recognition, contrast, and/or the like. Unoccluded image characteristics of the real façade texture are provided by at least one geo-referenced oblique image. The unoccluded image characteristics of the occlusion are applied to the real façade texture to form a mosaic image.
In another version, the real façade texture is analyzed and corrected for misalignment. Misalignment may be corrected by shrinking and/or stretching the real façade texture, extracting portions of the real façade texture, and/or the like. For example, the outer boundaries of the real façade texture may be extracted using discontinuities in depth, discontinuities in surface orientation, variations in scene illumination, and/or the like.
In another version, the wire-frame data of the three-dimensional model is analyzed to locate the geographical position of a roof of the structure. Images containing the roof are provided and a base image is selected and applied to the three-dimensional model. The images may be nadir images or oblique images. Selection of the base image is based on the image raster content, for example, a base image may be preferred in which the image raster content contains a greater proportion of pixels associated with the roof as compared with other base images.
In another embodiment, the present invention is related to a method of automatically generating a three-dimensional model having structures with real façade textures. The real façade textures are obtained from geo-referenced aerial oblique images. Wire-frame data is analyzed to locate geographical positions of the real façade textures of the structures within a geographical area. An oblique image showing the real façade texture is selected. Where present, at least one incorrect area within at least a portion of the real façade texture may be identified. The incorrect area within the portion of the real façade texture is automatically corrected.
So the above-recited features and advantages of the present invention may be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to the embodiments thereof that are illustrated in the appended drawings. It is to be noted, however, that the appended drawings illustrate only typical embodiments of this invention and are therefore not to be considered limiting in scope, for the invention may admit to other equally-effective embodiments.
Embodiments of the invention are shown in the above-identified Figures and described in detail below. In describing the embodiments, like or identical reference numerals are used to identify common or similar elements. The Figures are not necessarily to scale and certain features and certain views of the Figures may be shown exaggerated in scale or in schematic in the interest of clarity and conciseness.
Referring now to the drawings and in particular to
Generally, the system 10 identifies a geographical position 22 of at least one real façade texture 16 of the structure 14. The system 10 locates geo-referenced images 18 containing the real façade texture 16 and selects a base image 19 (See
The system 10 identifies the geographical position 22 of the real façade textures 16 within the geo-referenced images 18. In one embodiment, the system 10 uses wire-frame data 20 of the structure 14 to provide the geographical position 22 through the identification of boundaries (17a, 17b, 17c, 17d . . . ) of the real façade texture 16, as best illustrated in
The system 10 may also use terrain data 24 to provide the geographical position 22 of the real façade texture 16. For example, the system may identify boundaries (17a, 17b, 17c, 17d . . . ) of the real façade texture 16 as illustrated in
The geo-referenced image 18 providing the real façade texture 16, as illustrated in
The geo-referenced images 18 are preferably nadir images and/or oblique images. Nadir images, as described herein, provide vertical or orthogonal views of the geographic area. Nadir images may be an image captured from an overhead position, a position above the structure 14, or at a right angle or an angle near to a right angle to the structure 14. For example, the image may be taken from an overhead position as related to the structure 14 at an eighty-seven degree angle or similar angle close to a ninety-degree angle. A nadir image may be rectified to fit an associated wire-frame, DTM, DEM, and/or the like. Rectifying a nadir image may entail mapping the pixels to the coordinates of the wire-frame, DTM, DEM, and/or the like.
As nadir images typically do not express the details and characteristics of structures and objects, in the currently preferred embodiment, oblique images will typically, but not exclusively, be used for the purposes of the present invention. Oblique images, as described herein, are images taken at an angle other than that of the nadir perspective or images derived from the nadir perspective. Oblique images provide a perspective line of sight that generally reveals information not visible from an orthogonal or orthophoto view. For example, an oblique image may have an angle to the structure 14 in the range of about zero degrees to about eighty-nine degrees.
Referring to
The third step 54 is to locate the geographical position 22 of at least one real façade texture 16 of the structure 14. Wire-frame data 20 and/or terrain data 24 may be used to locate the geographical position 22 coordinating to the structure 14 as previously discussed herein.
The fourth step 56 is to select a base image 19 containing the real façade texture 16 from the geo-referenced images 18. The base image 19 is preferably selected as one that accurately represents the real façade texture 16 and may be automatically selected by the system 10 or selected or determined by the user from a limited selection of geo-referenced images 18 provided by the system 10.
The base image 19 containing the real façade texture 16 is selected from the geo-referenced images 18 of the structure 14 based upon a factorial analysis of the image raster content of each geo-referenced image 18 of the structure 14. The factorial analysis may include a weighted determination based on the resolution of the image, colour depth of the image, proportional size of the real façade texture, contrast, time of day the image was captured, time of year the image was captured and/or the like. Foliage may also play a role in the factorial analysis. For example, the base image 19 may be selected based on the contrast in lighting conditions. Contrast may be measured as a histogram and provide information regarding shadows within the image. Such information is relevant in constructing replications of large geographical areas, as the three-dimensional model 12 would generally include real façade textures 16 representing the same approximate time of day and/or time of year.
The fifth step is to apply or relate the real façade texture 16 of the base image 19 to the three-dimensional model 12. The three-dimensional model 12 may comprise base factors such as wire-frame data 20 and/or terrain data 24. For example, the real façade texture 16 of the base image 19 may be applied to the wire-frame of the structure 14. The system 10 may automatically rotate, stretch, or shrink the real façade texture 16 to align with edges of the wire-frame data 20. Additionally, the system 10 may provide for user evaluation and/or manipulation of the real façade texture 16.
Alternatively, wire-frame data 20 and/or terrain data 24 may provide a coordinate system for the three-dimensional model 12 such that the real façade texture 16 is geographically positioned within the three-dimensional model according to the corresponding coordinates of the wire-frame data 20 and/or terrain data 24. Additionally, the real façade texture 16 may be applied to form the entire three-dimensional model 12 or, alternatively, at least a portion of the three-dimensional model 12. Multiple base images 19 with multiple real façade textures 16 may form the three-dimensional model 12.
In another embodiment, the real façade texture may be further processed to correct occlusions 70, as illustrated in
To correct for the occlusion 70 within the real façade texture 16, the base image 19 is analyzed to locate a geographical position 72 of the occlusion 70. For example, the geographical position 72 may be identified using boundaries (74a, 74b, 74c, 74d . . . ). Although the boundaries 74a-74d are illustrated as a rectangle, it will be appreciated by one skilled in the art, the boundaries 74a-74d may comprise a circle, square, triangle, or any fanciful shape. Additionally, an edge-detection algorithm may be used to identify the geographical position of the occlusion 70.
Once the geographical position 72 of the occlusion 70 is located, the geo-referenced images 18 of the structure 14 are analyzed to locate at least one image 76 having an unoccluded image characteristic 78. As illustrated in
The unoccluded image characteristic is applied to the real façade texture 16 forming a mosaic image 80. For example, pixels of the unoccluded image characteristic 78 may replace pixels of the occlusion 70 of the real façade texture 16. The mosaic image 80 may include several unoccluded image characteristics 78 in addition to the real façade texture 16 of the base image 19.
Storage of the mosaic image 80 may be problematic if the file size of the mosaic image 80 is extremely large. Thus, it may be beneficial to use an algorithm for storing the mosaic image 80. For example, a rectangular area of the mosaic image 80 can be extracted and all information outside of the rectangular space cropped and removed. However, the geo-referencing information of the area would still be maintained within the mosaic image 80.
Occlusions 70 are not limited to blocked areas on the real façade texture 16 but can also include misalignment of the real façade texture 16 when applied to the three-dimensional model 12 and/or undesirable background images within the real façade texture 16. Occlusions 70, misalignments, and background images may be identified using pattern recognition, contrast, and/or the like. For example, pattern recognition technically can be used to analyze each pixel or a group of pixels to determine whether the pixel belongs to the real façade texture 16 or is part of the occlusion 70. The three-dimensional geometry of the surrounding buildings may also be a key factor.
Additionally, the real façade texture 16 may be optimized for depth complexity. Methods for optimizing depth complexity involve the system 10 first determining whether overlapping elements within the real façade texture 16 are occlusions 70 or desirable features of the real façade texture 16. If the system 10 determines the overlapping elements are desirable features of the real façade texture 16, the system 10 applies the real façade texture 16 of the overlapping structures to the three-dimensional model. For example, the system 10 may detect a small roof structure that is overlapping a large roof structure. The system 10 will assume that the small roof structure is an element on top of the large roof structure and considered desirably within the real façade texture 16. The system will apply the real façade texture 16 of the overlapping structures to the three-dimensional model 12. Depth complexity analyses the geometry of overlapping 3D structures, such as a small room on a large roof. Usually, walls are represented by vertical rectangles, from the top of the roof to the terrain. In this case, walls of the small room are created only to the large roof and not to the terrain.
The formation of the three-dimensional model 12 with real façade textures 16 may be a continuous or intermittent process. Preferably, the formation of the three-dimensional model 12 is automatically performed by the system 10 to facilitate rapid modeling of an area. Using the methods as described herein, the system 10 determines the geographic location of an area of interest and retrieves the appropriate wire-frame data 20 and/or terrain data 24 associated with the area. The system 10 automatically locates and identifies structures 14 within the area based upon the wire-frame data 20 and/or terrain data 24 provided. Geo-referenced images 18 of the area are located by the system 10 using the wire-frame data 20 and/or terrain data 24. The base oblique image 19 of the structure 14 is automatically selected by the system 10 based on the image raster content of each of the geo-referenced images 18. If the base oblique image 19 contains more than one structure 14, the system 10 may locate each real façade texture 16 independently or together. Once the real façade texture 16 is located, the system 10 may fix any occlusions if necessary and apply the real façade texture to the three-dimensional model 12.
Generally, the system 10 is a computer system that is able to embody and/or execute the logic of the processes described herein. The logic embodied may be executed on any appropriate hardware such as, for example, a dedicated system or systems, personal computer system, distributed processing computer system, and/or the like.
Referring now to
The network 116 can be almost any type of network, although Internet and Internet 2 networks are preferred because of the wide support of their underlying technologies. One embodiment of the network 116 exists in an Internet environment, which means a TCP/IP-based network. It is conceivable that in the near future, the preferred or other embodiments, may wish to use more advanced networking topologies.
The servers 118 can be networked with a LAN 130. The gateway 120 is an entity responsible for providing access between the LAN 130 and the network 116. The gateway 120 can also be used as a security means to protect the LAN 130 from attack from external networks such as the network 116.
The LAN 130 network can be based on TCP/IP network such as the Internet, or it can based on another underlying network transport technology. The preferred embodiment uses an Ethernet network with TCP/IP because of the availability and acceptance of underlying technologies, but other embodiments may use other types of networks such as Fibre Channel, SCSI, Gigabit Ethernet, etc.
As discussed above, in one embodiment, the host system 112 includes the servers 118. The configuration of the server hardware will depend upon the requirements and needs of the particular embodiment of the system 10. Typical embodiments, including the preferred embodiment, will include multiple servers 118 with load balancing to increase stability and availability. It is envisioned that the servers 118 will include database servers and application/web servers. The database servers are preferably separated from the application/web servers to improve availability and to provide the database servers with improved hardware and storage.
The user devices 114 can be any number and type of device. Generally, user devices 114 involve a user 32, using a computer 34 with a display 36, keyboard 38, and mouse 40. It is contemplated user devices 114 may include a touch screen element on the display in addition to or in lieu of the keyboard 38 and/or mouse 40.
Typically, the user device 114 uses a type of software called a “browser” as indicated by reference numeral 42 to render HTML/XHTML content that is generated when requesting resources from a source, such as the host system 112. In the preferred embodiment, the system 10 is designed to be compatible with major Web Browser vendors (Microsoft Internet Explorer, Netscape Navigator, and Opera). Other embodiments may wish to focus on one particular browser depending upon the common user base using the system 10. It should be noted, user devices 114 may interact with the system 10 through any suitable functional software, such as a program specifically designed for the individual embodiments of the system 10.
The user devices 114 can also be implemented as a portable device such as a laptop computer 150 (or handheld computer); a cellular telephone 152 with a micro or embedded Web Browser; a Portable Digital Assistant 154 (PDA) capable of wireless network access; a pen-based or tablet computer 156, and/or the like. In another embodiment, the user device 114 may be a cable box 60 or other similar device for viewing through a display 62 or television. Current embodiments of the system 10 can also be modified to use any of these or similar future developed devices.
The system 10 is designed to provide flexibility in its deployment. Depending upon the requirements of the particular embodiment, the engine may be designed to work in almost any environment such as a desktop application, a web application, a series of web services designed to communicate with an external application, and/or the like.
The hardware and system software are designed with two key concerns: flexibility and scalability. Although some specifics for software and hardware components are described herein, it will be understood that a wide array of different components may be substituted. For example, different database vendors may be used, SML-based document stores may be used, and/or the like.
When the system 10 is used to execute the logic of the processes described herein, such computer(s) and/or execution may be conducted at a same geographical location or multiple geographic locations. Furthermore, the execution of the logic may be conducted continuously or at multiple discrete times.
The system 10 includes one or more computer readable medium storing instructions for displaying a pixel representation of one or more of the images described herein including the geo-referenced images 18, the base oblique images 19, wire frame data 20, terrain data 24, the three-dimensional model 12, and the like. The computer readable medium may be part of the host system 112, the user devices 114, or combination thereof. Additionally, the system 10 uses one or more databases or servers 118 to store the images in an organized format. For example, the geo-referenced images 18 of the structure 14 may be sorted and stored by the direction showing the particular real façade texture 16.
The system 10 may also include instructions for (1) displaying pixel representations of the images as described above; (2) selecting the geographical location of interest; (3) altering and/or editing images; and (4) other similar tasks. The instructions typically run on a combination of the user devices 114 and the host system 112.
Although the foregoing invention has been described in some detail by way of illustration and example for purposes of clarity of understanding, it will be obvious to those skilled in the art that certain changes and modifications may be practiced without departing from the spirit and scope thereof, as described in this specification and as defined in the appended claims below.
The present patent application is a continuation of U.S. Ser. No. 15/374,358 filed Dec. 9, 2016, which is a continuation of U.S. Ser. No. 15/056,598 filed Feb. 29, 2016, issued Dec. 13, 2016 as U.S. Pat. No. 9,520,000, which is a continuation of U.S. Ser. No. 14/633,285 filed Feb. 27, 2015, issued Mar. 1, 2016, as U.S. Pat. No. 9,275,496, which is a continuation of U.S. Ser. No. 14/152,638, filed Jan. 10, 2014, issued Mar. 3, 2015, as U.S. Pat. No. 8,970,615, which is a continuation of U.S. Ser. No. 13/903,683, filed May 28, 2013, issued Feb. 11, 2014, as U.S. Pat. No. 8,648,872, which is a continuation of U.S. Ser. No. 11/998,974, filed Dec. 3, 2007, issued Sep. 10, 2013, as U.S. Pat. No. 8,531,472, the entire contents of all of which are hereby incorporated herein by reference in their entirety.
Number | Name | Date | Kind |
---|---|---|---|
2273876 | Lutz et al. | Feb 1942 | A |
3153784 | Petrides et al. | Oct 1964 | A |
3594556 | Edwards | Jul 1971 | A |
3614410 | Bailey | Oct 1971 | A |
3621326 | Hobrough | Nov 1971 | A |
3661061 | Tokarz | May 1972 | A |
3716669 | Watanabe et al. | Feb 1973 | A |
3725563 | Woycechowsky | Apr 1973 | A |
3864513 | Halajian et al. | Feb 1975 | A |
3866602 | Furihata | Feb 1975 | A |
3877799 | O'Donnell | Apr 1975 | A |
4015080 | Moore-Searson | Mar 1977 | A |
4044879 | Stahl | Aug 1977 | A |
4184711 | Wakimoto | Jan 1980 | A |
4240108 | Levy | Dec 1980 | A |
4281354 | Conte | Jul 1981 | A |
4344683 | Stemme | Aug 1982 | A |
4360876 | Girault et al. | Nov 1982 | A |
4382678 | Thompson et al. | May 1983 | A |
4387056 | Stowe | Jun 1983 | A |
4396942 | Gates | Aug 1983 | A |
4463380 | Hooks | Jul 1984 | A |
4489322 | Zulch et al. | Dec 1984 | A |
4490742 | Wurtzinger | Dec 1984 | A |
4491399 | Bell | Jan 1985 | A |
4495500 | Vickers | Jan 1985 | A |
4527055 | Harkless et al. | Jul 1985 | A |
4543603 | Laures | Sep 1985 | A |
4586138 | Mullenhoff et al. | Apr 1986 | A |
4635136 | Ciampa et al. | Jan 1987 | A |
4653136 | Denison | Mar 1987 | A |
4653316 | Fukuhara | Mar 1987 | A |
4673988 | Jansson et al. | Jun 1987 | A |
4686474 | Olsen et al. | Aug 1987 | A |
4688092 | Kamel et al. | Aug 1987 | A |
4689748 | Hofmann | Aug 1987 | A |
4707698 | Constant et al. | Nov 1987 | A |
4758850 | Archdale et al. | Jul 1988 | A |
4805033 | Nishikawa | Feb 1989 | A |
4807024 | Mclaurin et al. | Feb 1989 | A |
4814711 | Olsen et al. | Mar 1989 | A |
4814896 | Heitzman et al. | Mar 1989 | A |
4843463 | Michetti | Jun 1989 | A |
4899296 | Khattak | Feb 1990 | A |
4906198 | Cosimano et al. | Mar 1990 | A |
4953227 | Katsuma et al. | Aug 1990 | A |
4956872 | Kimura | Sep 1990 | A |
5034812 | Rawlings | Jul 1991 | A |
5086314 | Aoki et al. | Feb 1992 | A |
5121222 | Endoh et al. | Jun 1992 | A |
5138444 | Hiramatsu | Aug 1992 | A |
5155597 | Lareau et al. | Oct 1992 | A |
5164825 | Kobayashi et al. | Nov 1992 | A |
5166789 | Myrick | Nov 1992 | A |
5191174 | Chang et al. | Mar 1993 | A |
5200793 | Ulich et al. | Apr 1993 | A |
5210586 | Grage et al. | May 1993 | A |
5231435 | Blakely | Jul 1993 | A |
5247356 | Ciampa | Sep 1993 | A |
5251037 | Busenberg | Oct 1993 | A |
5265173 | Griffin et al. | Nov 1993 | A |
5267042 | Tsuchiya et al. | Nov 1993 | A |
5270756 | Busenberg | Dec 1993 | A |
5296884 | Honda et al. | Mar 1994 | A |
5335072 | Tanaka et al. | Aug 1994 | A |
5342999 | Frei et al. | Aug 1994 | A |
5345086 | Bertram | Sep 1994 | A |
5353055 | Hiramatsu | Oct 1994 | A |
5369443 | Woodham | Nov 1994 | A |
5402170 | Parulski et al. | Mar 1995 | A |
5414462 | Veatch | May 1995 | A |
5467271 | Abel et al. | Nov 1995 | A |
5481479 | Wight et al. | Jan 1996 | A |
5486948 | Imai et al. | Jan 1996 | A |
5506644 | Suzuki et al. | Apr 1996 | A |
5508736 | Cooper | Apr 1996 | A |
5555018 | von Braun | Sep 1996 | A |
5604534 | Hedges et al. | Feb 1997 | A |
5617224 | Ichikawa et al. | Apr 1997 | A |
5633946 | Lachinski et al. | May 1997 | A |
5668593 | Lareau et al. | Sep 1997 | A |
5677515 | Selk et al. | Oct 1997 | A |
5798786 | Lareau et al. | Aug 1998 | A |
5835133 | Moreton et al. | Nov 1998 | A |
5841574 | Willey | Nov 1998 | A |
5844602 | Lareau et al. | Dec 1998 | A |
5852753 | Lo et al. | Dec 1998 | A |
5894323 | Kain et al. | Apr 1999 | A |
5899945 | Baylocq et al. | May 1999 | A |
5963664 | Kumar et al. | Oct 1999 | A |
6037945 | Loveland | Mar 2000 | A |
6088055 | Lareau et al. | Jul 2000 | A |
6094215 | Sundahl et al. | Jul 2000 | A |
6097854 | Szeliski et al. | Aug 2000 | A |
6108032 | Hoagland | Aug 2000 | A |
6130705 | Lareau et al. | Oct 2000 | A |
6157747 | Szeliski et al. | Dec 2000 | A |
6167300 | Cherepenin et al. | Dec 2000 | A |
6222583 | Matsumura et al. | Apr 2001 | B1 |
6236886 | Cherepenin et al. | May 2001 | B1 |
6256057 | Mathews et al. | Jul 2001 | B1 |
6373522 | Mathews et al. | Apr 2002 | B2 |
6421610 | Carroll et al. | Jul 2002 | B1 |
6434280 | Peleg et al. | Aug 2002 | B1 |
6597818 | Kumar et al. | Jul 2003 | B2 |
6639596 | Shum et al. | Oct 2003 | B1 |
6711475 | Murphy | Mar 2004 | B2 |
6731329 | Feist et al. | May 2004 | B1 |
6747686 | Bennett | Jun 2004 | B1 |
6810383 | Loveland | Oct 2004 | B1 |
6816819 | Loveland | Nov 2004 | B1 |
6826539 | Loveland | Nov 2004 | B2 |
6829584 | Loveland | Dec 2004 | B2 |
6834128 | Altunbasak et al. | Dec 2004 | B1 |
6876763 | Sorek et al. | Apr 2005 | B2 |
7009638 | Gruber et al. | Mar 2006 | B2 |
7018050 | Ulichney et al. | Mar 2006 | B2 |
7046401 | Dufaux et al. | May 2006 | B2 |
7061650 | Walmsley et al. | Jun 2006 | B2 |
7065260 | Zhang et al. | Jun 2006 | B2 |
7123382 | Walmsley et al. | Oct 2006 | B2 |
7127348 | Smitherman et al. | Oct 2006 | B2 |
7133551 | Chen | Nov 2006 | B2 |
7142984 | Rahmes et al. | Nov 2006 | B2 |
7184072 | Loewen et al. | Feb 2007 | B1 |
7233691 | Setterholm | Jun 2007 | B2 |
7262790 | Bakewell | Aug 2007 | B2 |
7348895 | Lagassey | Mar 2008 | B2 |
7509241 | Guo | Mar 2009 | B2 |
7728833 | Verma | Jun 2010 | B2 |
7832267 | Woro | Nov 2010 | B2 |
7844499 | Yahiro | Nov 2010 | B2 |
8078396 | Meadow | Dec 2011 | B2 |
8705843 | Lieckfeldt | Apr 2014 | B2 |
20020041328 | LeCompte et al. | Apr 2002 | A1 |
20020041717 | Murata et al. | Apr 2002 | A1 |
20020114536 | Xiong et al. | Aug 2002 | A1 |
20030014224 | Guo et al. | Jan 2003 | A1 |
20030043824 | Remboski et al. | Mar 2003 | A1 |
20030088362 | Melero et al. | May 2003 | A1 |
20030164962 | Nims et al. | Sep 2003 | A1 |
20030214585 | Bakewell | Nov 2003 | A1 |
20040105090 | Schultz et al. | Jun 2004 | A1 |
20040167709 | Smitherman et al. | Aug 2004 | A1 |
20050073241 | Yamauchi et al. | Apr 2005 | A1 |
20050088251 | Matsumoto | Apr 2005 | A1 |
20050169521 | Hel-Or | Aug 2005 | A1 |
20060028550 | Palmer et al. | Feb 2006 | A1 |
20060092043 | Lagassey | May 2006 | A1 |
20060238383 | Kimchi et al. | Oct 2006 | A1 |
20060250515 | Koseki et al. | Nov 2006 | A1 |
20070024612 | Balfour | Feb 2007 | A1 |
20070046448 | Smitherman | Mar 2007 | A1 |
20070237420 | Steedly et al. | Oct 2007 | A1 |
20080120031 | Rosenfeld et al. | May 2008 | A1 |
20080123994 | Schultz et al. | May 2008 | A1 |
20080158256 | Russell et al. | Jul 2008 | A1 |
20090110267 | Zakhor et al. | Apr 2009 | A1 |
20090177458 | Hochart et al. | Jul 2009 | A1 |
20090208095 | Zebedin | Aug 2009 | A1 |
20090304227 | Kennedy et al. | Dec 2009 | A1 |
20100296693 | Thornberry et al. | Nov 2010 | A1 |
20110033110 | Shimamura et al. | Feb 2011 | A1 |
20130246204 | Thornberry et al. | Sep 2013 | A1 |
Number | Date | Country |
---|---|---|
331204 | Jul 2006 | AT |
0316110 | Sep 2005 | BR |
2402234 | Sep 2000 | CA |
2505566 | May 2004 | CA |
1735897 | Feb 2006 | CN |
60017384 | Mar 2006 | DE |
60306301 | Nov 2006 | DE |
1418402 | Oct 2006 | DK |
1010966 | Feb 1999 | EP |
1180967 | Feb 2002 | EP |
1418402 | May 2004 | EP |
PA05004987 | Feb 2006 | EP |
1696204 | Aug 2006 | EP |
2266704 | Mar 2007 | ES |
2003317089 | Nov 2003 | JP |
WO9918732 | Apr 1999 | WO |
WO2000053090 | Sep 2000 | WO |
WO2004044692 | May 2004 | WO |
WO2005088251 | Sep 2005 | WO |
WO2008028040 | Mar 2008 | WO |
Entry |
---|
Ackermann, Prospects of Kinematic GPS Aerial Triangulation, ITC Journal, 1992. |
Ciampa, John A., “Pictometry Digital Video Mapping”, SPIE, vol. 2598, pp. 140-148, 1995. |
Ciampa, J. A., Oversee, Presented at Reconstruction After Urban earthquakes, Buffalo, NY, 1989. |
Dunford et al., Remote Sensing for Rural Development Planning in Africa, The Journal for the International Institute for Aerial Survey and Earth Sciences, 2:99-108, 1983. |
Gagnon, P.A., Agnard, J. P., Nolette, C., & Boulianne, M., “A Micro-Computer based General Photogrammetric System”, Photogrammetric Engineering and Remote Sensing, vol. 56, No. 5., pp. 623-625, 1990. |
Konecny, G., “Issues of Digital Mapping”, Leibniz University Hannover, Germany, GIS Ostrava 2008, Ostrava 27.—Jan. 30, 2008, pp. 1-8. |
Konecny, G., “Analytical Aerial Triangulation with Convergent Photography”, Department of Surveying Engineering, University of New Brunswick, pp. 37-57, 1966. |
Konecny, G., “Interior Orientation and Convergent Photography”, Photogrammetric Engineering, pp. 625-634, 1965. |
Graham, Lee A., “Airborne Video for Near-Real-Time Vegetation Mapping”, Journal of Forestry, 8:28-32, 1993. |
Graham, Horita TRG-50 SMPTE Time-Code Reader, Generator, Window Inserter, 1990. |
Hess, L.L, et al., “Geocoded Digital Videography for Validation of Land Cover Mapping in the Amazon Basin”, International Journal of Remote Sensing, vol. 23, No. 7, pp. 1527-1555, 2002. |
Hinthorne, J., et al., “Image Processing in The Grass GIS”, Geoscience and Remote Sensing Symposium, 4:2227-2229, 1991. |
Imhof, Ralph K., “Mapping from Oblique Photographs”, Manual of Photogrammetry, Chapter 18, 1966. |
Jensen, John R., Introductory Digital Image Processing: A Remote Sensing Perspective, Prentice-Hall, 1986; 399 pages. |
Lapine, Lewis A., “Practical Photogrammetric Control by Kinematic GPS”, GPS World, 1(3):44-49, 1990. |
Lapine, Lewis A., Airborne Kinematic GPS Positioning for Photogrammetry—The Determination of the Camera Exposure Station, Silver Spring, MD, 11 pages, at least as early as 2000. |
Linden et al., Airborne Video Automated Processing, US Forest Service Internal report, Fort Collins, CO, 1993. |
Myhre, Dick, “Airborne Video System Users Guide”, USDA Forest Service, Forest Pest Management Applications Group, published by Management Assistance Corporation of America, 6 pages, 1992. |
Myhre et al., “An Airborne Video System Developed Within Forest Pest Management—Status and Activities”, 10 pages, 1992. |
Myhre et al., “Airborne Videography—A Potential Tool for Resource Managers”—Proceedings: Resource Technology 90, 2nd International Symposium on Advanced Technology in Natural Resource Management, 5 pages, 1990. |
Myhre et al., Aerial Photography for Forest Pest Management, Proceedings of Second Forest Service Remote Sensing Applications Conference, Slidell, Louisiana, 153-162, 1988. |
Myhre et al., “Airborne Video Technology”, Forest Pest Management/Methods Application Group, Fort Collins, CO, pp. 1-6, at least as early as Jul. 30, 2006. |
Norton-Griffiths et al., 1982. “Sample surveys from light aircraft combining visual observations and very large scale color photography”. University of Arizona Remote Sensing Newsletter 82-2:1-4. |
Norton-Griffiths et al., “Aerial Point Sampling for Land Use Surveys”, Journal of Biogeography, 15:149-156, 1988. |
Novak, Rectification of Digital Imagery, Photogrammetric Engineering and Remote Sensing, 339-344, 1992. |
Slaymaker, Dana M., “Point Sampling Surveys with GPS-logged Aerial Videography”, Gap Bulletin No. 5, University of Idaho, http://www.gap.uidaho.edu/Bulletins/5/PSSwGPS.html, 1996. |
Slaymaker, et al., “Madagascar Protected Areas Mapped with GPS-logged Aerial Video and 35mm Air Photos”, Earth Observation magazine, vol. 9, No. 1, http://www.eomonline.com/Common/Archives/2000jan/00jan_tableofcontents.html, pp. 1-4, 2000. |
Slaymaker, et al., “Cost-effective Determination of Biomass from Aerial Images”, Lecture Notes in Computer Science, 1737:67-76, http://portal.acm.org/citation.cfm?id=648004.743267&coll=GUIDE&dl=, 1999. |
Slaymaker, et al., “A System for Real-time Generation of Geo-referenced Terrain Models”, 4232A-08, SPIE Enabling Technologies for Law Enforcement Boston, MA, ftp://vis-ftp.cs.umass.edu/Papers/schultz/spie2000.pdf, 2000. |
Slaymaker, et al., “ Integrating Small Format Aerial Photography, Videography, and a Laser Profiler for Environmental Monitoring”, In ISPRS WG III/1 Workshop on Integrated Sensor Calibration and Orientation, Portland, Maine, 1999. |
Slaymaker, et al., “Calculating Forest Biomass With Small Format Aerial Photography, Videography and a Profiling Laser”, In Proceedings of the 17th Biennial Workshop on Color Photography and Videography in Resource Assessment, Reno, NV, 1999. |
Slaymaker et al., Mapping Deciduous Forests in Southern New England using Aerial Videography and Hyperclustered Multi-Temporal Landsat TM Imagery, Department of Forestry and Wildlife Management, University of Massachusetts, 1996. |
Star et al., “Geographic Information Systems an Introduction”, Prentice-Hall, 1990. |
Tomasi et al., “Shape and Motion from Image Streams: a Factorization Method”—Full Report on the Orthographic Case, pp. 9795-9802, 1992. |
Warren, Fire Mapping with the Fire Mousetrap, Aviation and Fire Management, Advanced Electronics System Development Group, USDA Forest Service, 1986. |
Welch, R., “Desktop Mapping with Personal Computers”, Photogrammetric Engineering and Remote Sensing, 1651-1662, 1989. |
Westervelt, James, “Introduction to GRASS 4”, pp. 1-25, 1991. |
“RGB Spectrum Videographics Report, vol. 4, No. 1, McDonnell Douglas Integrates RGB Spectrum Systems in Helicopter Simulators”, pp. 1-6, 1995. |
RGB “Computer Wall”, RGB Spectrum, 4 pages, 1995. |
“The First Scan Converter with Digital Video Output”, Introducing . . . The RGB/Videolink 1700D-1, RGB Spectrum, 2 pages, 1995. |
ERDAS Field Guide, Version 7.4, A Manual for a commercial image processing system, 1990. |
“Image Measurement and Aerial Photography”, Magazine for all branches of Photogrammetry and its fringe areas, Organ of the German Photogrammetry Association, Berlin-Wilmersdorf, No. 1, 1958. |
“Airvideo Analysis”, MicroImages, Inc., Lincoln, NE, 1 page, Dec. 1992. |
Zhu, Zhigang, Hanson, Allen R., “Mosaic-Based 3D Scene Representation and Rendering”, Image Processing, 2005, ICIP 2005, IEEE International Conference on 1(2005). |
Mostafa, et al., “Direct Positioning and Orientation Systems How do they Work? What is the Attainable Accuracy?”, Proceeding, American Society of Photogrammetry and Remote Sensing Annual Meeting, St. Louis, MO, Apr. 24-27, 2001. |
“POS AV” georeferenced by APPLANIX aided inertial technology, http://www.applanix.com/products/posav_index.php. |
Mostafa, et al., “Ground Accuracy from Directly Georeferenced Imagery”, Published in GIM International vol. 14 N. Dec. 12, 2000. |
Mostafa, et al., “Airborne Direct Georeferencing of Frame Imagery: An Error Budget”, The 3rd International Symposium on Mobile Mapping Technology, Cairo, Egypt, Jan. 3-5, 2001. |
Mostafa, M.R. and Hutton, J., “Airborne Kinematic Positioning and Attitude Determination Without Base Stations”, Proceedings, International Symposium on Kinematic Systems in Geodesy, Geomatics, and Navigation (KIS 2001) Banff, Alberta, Canada, Jun. 4-8, 2001. |
Mostafa, et al., “Airborne DGPS Without Dedicated Base Stations for Mapping Applications”, Proceedings of ION-GPS 2001, Salt Lake City, Utah, USA, Sep. 11-14. |
Mostafa, “ISAT Direct Exterior Orientation QA/QC Strategy Using POS Data”, Proceedings of OEEPE Workshop: Integrated Sensor Orientation, Hanover, Germany, Sep. 17-18, 2001. |
Mostafa, “Camera/IMU Boresight Calibration: New Advances and Performance Analysis”, Proceedings of the ASPRS Annual Meeting, Washington, D.C., Apr. 21-26, 2002. |
Hiatt, “Sensor Integration Aids Mapping at Ground Zero”, Photogrammetric Engineering and Remote Sensing, Sep. 2002, p. 877-878. |
Mostafa, “Precision Aircraft GPS Positioning Using CORS”, Photogrammetric Engineering and Remote Sensing, Nov. 2002, p. 1125-1126. |
Mostafa, et al., System Performance Analysis of INS/DGPS Integrated System for Mobile Mapping System (MMS), Department of Geomatics Engineering, University of Calgary, Commission VI, WG VI/4, Mar. 2004. |
Artes F., & Hutton, J., “GPS and Inertial Navigation Delivering”, Sep. 2005, GEOconnexion International Magazine, p. 52-53, Sep. 2005. |
“POS AV” APPLANIX, Product Outline, airborne@applanix.com, 3 pages, Mar. 28, 2007. |
POSTrack, “Factsheet”, APPLANIX, Ontario, Canada, www.applanix.com, Mar. 2007. |
POS AV “Digital Frame Camera Applications”, 3001 Inc., Brochure, 2007. |
POS AV “Digital Scanner Applications”, Earthdata Brochure, Mar. 2007. |
POS AV “Film Camera Applications” AeroMap Brochure, Mar. 2007. |
POS AV “LIDAR Applications” MD Atlantic Brochure, Mar. 2007. |
POS AV “OEM System Specifications”, 2005. |
POS AV “Synthetic Aperture Radar Applications”, Overview, Orbisat Brochure, Mar. 2007. |
“POSTrack V5 Specifications” 2005. |
“Remote Sensing for Resource Inventory Planning and Monitoring”, Proceeding of the Second Forest Service Remote Sensing Applications Conference—Slidell, Louisiana and NSTL, Mississippi, Apr. 11-15, 1988. |
“Protecting Natural Resources with Remote Sensing”, Proceeding of the Third Forest Service Remote Sensing Applications Conference—Apr. 9-13, 1990. |
Heipke, et al, “Test Goals and Test Set Up for the OEEPE Test—Integrated Sensor Orientation”, 1999. |
Kumar, et al., “Registration of Video to Georeferenced Imagery”, Sarnoff Corporation, CN5300, Princeton, NJ, 1998. |
McConnel, Proceedings Aerial Pest Detection and Monitoring Workshop—1994.pdf, USDA Forest Service Forest Pest Management, Northern Region, Intermountain region, Forest Insects and Diseases, Pacific Northwest Region. |
“Standards for Digital Orthophotos”, National Mapping Program Technical Instructions, US Department of the Interior, Dec. 1996. |
Tao, “Mobile Mapping Technology for Road Network Data Acquisition”, Journal of Geospatial Engineering, vol. 2, No. 2, pp. 1-13, 2000. |
“Mobile Mapping Systems Lesson 4”, Lesson 4 SURE 382 Geographic Information Systems II, pp. 1-29, Jul. 2, 2006. |
Konecny, G., “Mechanische Radialtriangulation mit Konvergentaufnahmen”, Bildmessung und Luftbildwesen, 1958, Nr. 1. |
Myhre, “ASPRS/ACSM/RT 92” Technical papers, Washington, D.C., vol. 5 Resource Technology 92, Aug. 3-8, 1992. |
Rattigan, “Towns get new view from above,” The Boston Globe, Sep. 5, 2002. |
Mostafa, et al., “Digital image georeferencing from a multiple camera system by GPS/INS,” ISP RS Journal of Photogrammetry & Remote Sensing, 56(1): I -12, Jun. 2001. |
Dillow, “Grin, or bare it, for aerial shot,” Orange County Register (California), Feb. 25, 2001. |
Anonymous, “Live automatic coordinates for aerial images,” Advanced Imaging, 12(6):51, Jun. 1997. |
Anonymous, “Pictometry and US Geological Survey announce—Cooperative Research and Development Agreement,” Press Release published Oct. 20, 1999. |
Miller, “Digital software gives small Arlington the Big Picture,” Government Computer NewsState & Local, 7(12), Dec. 2001. |
Garrett, “Pictometry: Aerial photography on steroids,” Law Enforcement Technology 29(7):114-116, Jul. 2002. |
Weaver, “County gets an eyeful,” The Post-Standard (Syracuse, NY), May 18, 2002. |
Reed, “Firm gets latitude to map O.C. in 3D,” Orange County Register (California), Sep. 27, 2000. |
Reyes, “Orange County freezes ambitious aerial photography project,” Los Angeles Times, Oct. 16, 2000. |
Aerowest Pricelist of Geodata as of Oct. 21, 2005 and translations to English 3 pages. |
www.archive.org Web site showing archive of German AeroDach Web Site http://www.aerodach.de from Jun. 13, 2004 (retrieved Sep. 20, 2012) and translations to English 4 pages. |
AeroDach®Online Roof Evaluation Standard Delivery Format and 3D Data File: Document Version 01.00.2002 with publication in 2002, 13 pages. |
Noronha et al., “Detection and Modeling of Building from Multiple Aerial Images,” Institute for Robotics and Intelligent Systems, University of Southern California, Nov. 27, 2001, 32 pages. |
Applicad Reports dated Nov. 25, 1999-Mar. 9, 2005, 50 pages. |
Applicad Online Product Bulletin archive from Jan. 7, 2003, 4 pages. |
Applicad Sorcerer Guide, Version 3, Sep. 8, 1999, 142 pages. |
Xactimate Claims Estimating Software archive from Feb. 12, 2010, 8 pages. |
Bignone et al, Automatic Extraction of Generic House Roofs from High Resolution Aerial Imagery, Communication Technology Laboratory, Swiss Federal Institute of Technology ETH, CH-8092 Zurich, Switzerland, 12 pages, 1996. |
Geospan 2007 Job proposal. |
Greening et al., Commercial Applications of GPS-Assisted Photogrammetry, Presented at GIS/LIS Annual Conference and Exposition, Phoenix, AZ, Oct. 1994. |
APPLANIX CORP, Robust, Precise Position and Orientation Solutions, POS/AV & POS/DG Installation & Operation Manual, Redefining the way you survey, May 19, 1999, Ontario, Canada. |
APPLANIX CORP, Robust, Precise Position and Orientation Solutions, POS/AV V4 Ethernet & Disk Logging ICD, Redefining the way you survey, Revision 3, Apr. 18, 2001, Ontario, Canada. |
Fuji, Kensaku; Arikawa, Tomohiko; “Urban Object Reconstruction Using Airborne Laser Elevation Image and Aerial Image”, IEEE Transactions on GeoScience and Remote Sensing, vol. 40, No. 10, IEEE, Oct. 2002, Abstract. |
USPTO, Office Action dated Aug. 20, 2015 regarding U.S. Appl. No. 13/689,006. |
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