METHOD AND APPARATUS FOR PROVIDING CLASSIFICATION OF QUALITY CHARACTERISTICS OF IMAGES

• US 20160379094A1
• Filed: 06/26/2015
• Published: 12/29/2016
• Est. Priority Date: 06/26/2015
• Status: Active Grant

• Current Assignee: HERE Global B.V. (Nokia Corporation)
• Original Assignee: HERE Global B.V. (Nokia Corporation)
• Inventors: MITTAL, Anish, MARKS, William
• Granted Patent: US 10,289,940 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06T 2207/30168 Image quality inspection
• CPC Class Codes: G06V 10/22 by selection of a specific region containing or referencing a pattern; Locating or processing of specific regions to guide the detection or recognition
• CPC Class Codes: G06V 10/50 by performing operations within image blocks; by using histograms, e.g. histogram of oriented gradients [HoG]; by summing image-intensity values; Projection analysis
• CPC Class Codes: G06V 10/60 relating to illumination properties, e.g. using a reflectance or lighting model
• CPC Class Codes: G06V 10/993 Evaluation of the quality of the acquired pattern

Abstract

An approach is provided for automated analysis and classification of quality characteristics associated with captured imagery that may be used in an application such as a map application. The approach includes determining digital data associated with a region of interest in an image. The approach also comprises processing and/or facilitating a processing of the digital data to determine one or more quality attributes associated with the region of interest. The approach further comprises causing, at least in part, a comparison of the one or more quality attributes to one or more criteria. The approach also comprises causing, at least in part, a generation of one or more classifications for the image based, at least in part, on the comparison.

Claims

1. A method comprising:

determining digital data associated with a region of interest in an image;

processing and/or facilitating a processing of the digital data to determine one or more quality attributes associated with the region of interest;

causing, at least in part, a comparison of the one or more quality attributes to one or more criteria; and

causing, at least in part, a generation of one or more classifications for the image based, at least in part, on the comparison.

2. A method of claim 1, further comprising:

determining the region of interest based, at least in part, on one or more parameters associated with the image.

3. A method of claim 1, further comprising:

determining the one or more criteria based, at least in part, on one or more other classifications associated with one or more other images, one or more user inputs, or a combination thereof.

4. A method of claim 3, further comprising:

causing, at least in part, a training of one or more classification models based, at least in part, on the one or more classifications; and

causing, at least in part, a classification of one or more other images based, at least in part, on the one or more classification models.

5. A method of claim 1, further comprising:

determining the quality attributes based, at least in part, on exposure-sensitive attributes including highlights, shadows, dark pixels, washed-out pixels, under-saturated pixels, over-saturated pixels, or a combination thereof.

6. A method of claim 5, further comprising:

causing, at least in part, a classification of pixels in the region of interest as dark pixels based, at least in part, on a percentage of gray pixels being less than a threshold.

7. A method of claim 5, further comprising:

causing, at least in part, a classification of pixels in the region of interest as washed-out pixels based, at least in part, on a percentage of gray pixels being greater than a threshold.

8. A method of claim 5, further comprising:

causing, at least in part, a classification of pixels in the region of interest as under-saturated pixels based, at least in part, on a percentage of saturation pixels being less than a threshold.

9. A method of claim 5, further comprising:

causing, at least in part, a classification of pixels in the region of interest as over-saturated pixels based, at least in part, on a percentage of saturation pixels being greater than a threshold.

10. A method of claim 1, wherein the one or more classifications include one or more scores associated with a normal exposure, over exposure, under exposure, or a combination thereof.

11. An apparatus comprising:

at least one processor; and

at least one memory including computer program code for one or more programs,

the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to perform at least the following,

determine digital data associated with a region of interest in an image;

process and/or facilitate a processing of the digital data to determine one or more quality attributes associated with the region of interest;

cause, at least in part, a comparison of the one or more quality attributes to one or more criteria; and

cause, at least in part, a generation of one or more classifications for the image based, at least in part, on the comparison.

12. An apparatus of claim 11, wherein the apparatus is further caused to:

determine the region of interest based, at least in part, on one or more parameters associated with the image.

13. An apparatus of claim 11, wherein the apparatus is further caused to:

determine the one or more criteria based, at least in part, on one or more other classifications associated with one or more other images, one or more user inputs, or a combination thereof.

14. An apparatus of claim 13, wherein the apparatus is further caused to:

cause, at least in part, a training of one or more classification models based, at least in part, on the one or more classifications; and

cause, at least in part, a classification of one or more other images based, at least in part, on the one or more classification models.

15. An apparatus of claim 11, wherein the apparatus is further caused to:

determine the quality attributes based, at least in part, on exposure-sensitive attributes including highlights, shadows, dark pixels, washed-out pixels, under-saturated pixels, over-saturated pixels, or a combination thereof.

16. An apparatus of claim 15, wherein the apparatus is further caused to:

cause, at least in part, a classification of pixels in the region of interest as dark pixels based, at least in part, on a percentage of gray pixels being less than a threshold; and

cause, at least in part, a classification of pixels in the region of interest as washed-out pixels based, at least in part, on a percentage of gray pixels being greater than a threshold.

17. An apparatus of claim 15, wherein the apparatus is further caused to:

cause, at least in part, a classification of pixels in the region of interest as under-saturated pixels based, at least in part, on a percentage of saturation pixels being less than a threshold; and

cause, at least in part, a classification of pixels in the region of interest as over-saturated pixels based, at least in part, on a percentage of saturation pixels being greater than a threshold.

18. A computer-readable non-transitory storage medium carrying one or more sequences of one or more instructions which, when executed by one or more processors, cause an apparatus to perform:

determine digital data associated with a region of interest in an image;

process and/or facilitate a processing of the digital data to determine one or more quality attributes associated with the region of interest;

cause, at least in part, a comparison of the one or more quality attributes to one or more criteria; and

cause, at least in part, a generation of one or more classifications for the image based, at least in part, on the comparison.

19. A computer-readable non-transitory storage medium of claim 18, wherein the apparatus is further caused to perform:

determine the one or more criteria based, at least in part, on one or more other classifications associated with one or more other images, one or more user inputs, or a combination thereof.

20. A computer-readable non-transitory storage medium of claim 19, wherein the apparatus is further caused to perform:

cause, at least in part, a training of one or more classification models based, at least in part, on the one or more classifications; and

cause, at least in part, a classification of one or more other images based, at least in part, on the one or more classification models.

21-48. -48. (canceled)