System and method for storing points of polygons related to an image

• US 10,311,098 B2
• Filed: 07/11/2018
• Issued: 06/04/2019
• Est. Priority Date: 03/16/2010
• Status: Active Grant

• Current Assignee: Cubic Digital Intelligence, Inc. (Cubic Corporation)
• Original Assignee: Pixia Corp. (Cubic Corporation)
• Inventors: Thakkar, Rahul C.
• Primary Examiner(s): Patel, Jayesh A
• Application Number:
  US16/032,401
• Publication Number:
  US 20180322120A1
• Time in Patent Office: 328 Days
• Field of Search: None
• CPC Class Codes: G06F 16/2246 Trees, e.g. B+trees
• CPC Class Codes: G06F 16/282 Hierarchical databases, e.g. IMS, LDAP data stores or Lotus Notes
• CPC Class Codes: G06F 16/51 Indexing; Data structures therefor; Storage structures
• CPC Class Codes: G06F 16/5838 using colour
• CPC Class Codes: G06F 16/5854 using shape and object relationship
• CPC Class Codes: G06T 2207/20021 Dividing image into blocks, subimages or windows
• CPC Class Codes: G06T 2207/20112 Image segmentation details
• CPC Class Codes: G06T 7/00 Image analysis
• CPC Class Codes: G06T 7/0002 Inspection of images, e.g. flaw detection
• CPC Class Codes: G06T 7/11 Region-based segmentation
• CPC Class Codes: G06T 7/13 Edge detection
• CPC Class Codes: G06T 7/50 Depth or shape recovery
• CPC Class Codes: G06T 7/60 Analysis of geometric attributes
• CPC Class Codes: G06T 7/90 Determination of colour characteristics
• CPC Class Codes: H04N 19/182 the unit being a pixel
• CPC Class Codes: H04N 19/184 the unit being bits, e.g. of the compressed video stream
• CPC Class Codes: H04N 19/186 the unit being a colour or a chrominance component
• CPC Class Codes: H04N 19/40 using video transcoding, i.e. partial or full decoding of a coded input stream followed by re-encoding of the decoded output stream

Abstract

A system and method for storing data may include: unevenly dividing the image into polygonal image sections having pixels with band(s) and a bit depth per band that is too high for displaying on a display screen, dividing the image into quads such that each quad has a boundary that contains a portion of one of the polygonal image sections, obtaining statistical data computed for each of the bands of each of the pixels within each of the polygonal image sections, storing, with respect to a reduced resolution dataset (R-Set) of the image, coordinates of bounds of a polygonal image section that intersects with a quad, organizing the image, the quads, and the polygonal image sections in a tree structure, and storing the statistical data, the pixel coordinates, and the tree structure.

Claims

1. The following listing of claims replaces all prior listings:

A method for storing data related to an image, the method being implemented by a computer system that includes one or more processors configured to execute computer program instructions, the method comprising;

unevenly dividing the image into a plurality of polygonal image sections each comprising a plurality of pixels, wherein each of the pixels has one or more bands and a bit depth per band that is too high for displaying on a display screen;

dividing the image into a plurality of quads such that each quad has a boundary that contains at least a portion of one of the plurality of polygonal image sections;

obtaining statistical data associated with the image, the statistical data being computed for each of the bands of each of the pixels within each of the polygonal image sections;

storing, with respect to a reduced resolution dataset (R-Set) of the image, pixel coordinates of bounds of at least one polygonal image section that intersects with a quad;

organizing the image, the plurality of quads, and the plurality of polygonal image sections in a tree structure; and

storing the statistical data, the pixel coordinates, and the tree structure.

2. The method according to claim 1, wherein the uneven division into the plurality of polygonal image sections results in at least two polygonal image sections of the plurality of polygonal image sections that have different polygonal shapes, area sizes, or numbers of sides, and

wherein each of the plurality of quads is rectangular.

3. The method according to claim 1, further comprising:

storing a plurality of reduced resolution datasets (R-Sets), each of the R-Sets being a progressively smaller version of the image,

wherein data describing each of the polygonal image sections is stored with the statistical data for pixels within the each section.

4. The method according to claim 1, further comprising:

determining in which quad of the plurality of quads and, for each quad, in which of the plurality of polygonal image sections each pixel is located by traversing the tree structure from a root node corresponding to the image to leaf nodes corresponding to the polygonal image sections,

wherein the tree structure is traversed sequentially.

5. The method according to claim 1, further comprising:

determining in which quad of the plurality of quads and, for each quad, in which of the plurality of polygonal image sections each pixel is located by traversing the tree structure from a root node corresponding to the image to leaf nodes corresponding to the polygonal image sections,

wherein the tree structure is traversed using parallel processing.

6. The method according to claim 1, wherein the statistical data of each band of each pixel within each of the polygonal image sections of the image comprises a histogram, and

wherein if a pixel is within multiple, overlapping polygonal image sections then the most recently computed histogram of the overlapping polygonal image section is used in computing the statistical data.

7. The method according to claim 1, further comprising:

converting each pixel having the bit depth per band that is too high for displaying on the display screen in an area of interest of the image into a corresponding pixel that has a lower bit depth per band displayable on the display screen using the statistical data of each of the polygonal image sections in which the respective pixel is located to obtain an image that corresponds to the area of interest and that has pixels with the lower bit depth per band.

8. The method according to claim 1, wherein the statistical data for pixels within each of the polygonal image sections is used to generate a plurality of look up tables, each look up table in the plurality of look up tables being associated with the corresponding polygonal image section of the plurality of polygonal image sections.

9. The method according to claim 1, wherein the statistical data for pixels within each of the polygonal image sections are computed using one or more of standard deviation, minimum/maximum values, mean, median, mode, skew, and kurtosis for each band of each pixel in the image.

10. The method according to claim 2, wherein each of the at least two polygonal image sections has substantially homogenous features that differ from one another.

11. A computer system for storing data related to an image, the image being associated with statistical data, the computer system comprising one or more processors configured to:

unevenly divide the image into a plurality of polygonal image sections each comprising a plurality of pixels, wherein each of the pixels has one or more bands and a bit depth per band that is too high for displaying on a display screen;

divide the image into a plurality of quads such that each quad has a boundary that contains at least a portion of one of the plurality of polygonal image sections;

obtain statistical data associated with the image, the statistical data being computed for each of the bands of each of the pixels within each of the polygonal image sections;

store, with respect to a reduced resolution dataset (R-Set) of the image, pixel coordinates of bounds of at least one polygonal image section that intersects with a quad;

organize the image, the plurality of quads, and the plurality of polygonal image sections in a tree structure; and

store the statistical data, the pixel coordinates, and the tree structure.

12. The computer system according to claim 11, wherein the uneven division into the plurality of polygonal image sections comprises at least two polygonal image sections of the plurality of polygonal image sections that have different polygonal shapes or area sizes or that have a different number of sides, and

wherein each of the plurality of quads is rectangular.

13. The computer system according to claim 11, wherein the one or more processors are further configured to store a plurality of reduced resolution datasets (R-Sets), each of the R-Sets being a progressively smaller version of the image, and

wherein data describing each of the polygonal image sections is stored with the statistical data for pixels within the each section.

14. The computer system according to claim 11, wherein the one or more processors are further configured to:

determine in which quad of the plurality of quads and, for each quad, in which of the plurality of polygonal image sections each pixel is located by traversing the tree structure from a root node corresponding to the image to leaf nodes corresponding to the polygonal image sections,

wherein the tree structure is traversed sequentially.

15. The computer system according to claim 11, wherein the one or more processors are further configured to:

determine in which quad of the plurality of quads and, for each quad, in which of the plurality of polygonal image sections each pixel is located by traversing the tree structure from a root node corresponding to the image to leaf nodes corresponding to the polygonal image sections,

wherein the tree structure is traversed using parallel processing.

16. The computer system according to claim 11, wherein the statistical data of each band of each pixel within each of the polygonal image sections of the image comprises a histogram,

wherein if a pixel is within multiple, overlapping polygonal image sections then the most recently computed histogram of the overlapping polygonal image section is used in computing the statistical data.

17. The computer system according to claim 11, wherein the one or more processors are further configured to convert each pixel having the bit depth per band that is too high for displaying on the display screen in an area of interest of the image into a corresponding pixel that has a lower bit depth per band displayable on the display screen using the statistical data of each of the polygonal image sections in which the respective pixel is located to obtain an image that corresponds to the area of interest and that has pixels with the lower bit depth per band.

18. The computer system according to claim 11, wherein the statistical data for pixels within each of the polygonal image sections is used to generate a plurality of look up tables, each look up table in the plurality of look up tables being associated with the corresponding polygonal image section of the plurality of polygonal image sections.

19. The computer system according to claim 11, wherein the statistical data for pixels within each of the polygonal image sections are computed using one or more of standard deviation, minimum/maximum values, mean, median, mode, skew, and kurtosis for each band of each pixel in the image.

20. The computer system according to claim 12, wherein each of the at least two polygonal image sections has substantially homogenous features that differ from one another.