METHODS AND APPARATUS TO CAPTURE PHOTOGRAPHS USING MOBILE DEVICES

• US 20190052801A1
• Filed: 10/18/2018
• Published: 02/14/2019
• Est. Priority Date: 06/18/2015
• Status: Active Grant

• Current Assignee: The Nielsen Company LLC (MOOD Media Corporation)
• Original Assignee: The Nielsen Company LLC (MOOD Media Corporation)
• Inventors: Garcia Morate, Diego, Hurtado Gacia, Antonio, Tovar Velasco, Javier, Barrales Ruiz, Carlos
• Granted Patent: US 10,735,645 B2
• Time in Patent Office: Days
• CPC Class Codes: G06T 2207/30168 Image quality inspection
• CPC Class Codes: G06T 2207/30244 Camera pose
• CPC Class Codes: G06T 3/4038 Image mosaicing, e.g. composing plane images from plane sub-images
• CPC Class Codes: G06T 5/73 Deblurring; Sharpening
• CPC Class Codes: G06T 7/0002 Inspection of images, e.g. flaw detection
• CPC Class Codes: G06T 7/73 using feature-based methods
• CPC Class Codes: G06V 10/16 using multiple overlapping images; Image stitching
• CPC Class Codes: G06V 10/17 using hand-held instruments
• CPC Class Codes: G06V 10/245 by locating a pattern; Special marks for positioning
• CPC Class Codes: H04N 23/635 Region indicators; Field of view indicators
• CPC Class Codes: H04N 23/64 Computer-aided capture of images, e.g. transfer from script file into camera, check of taken image quality, advice or proposal for image composition or decision on when to take image
• CPC Class Codes: H04N 23/6811 based on the image signal

Abstract

Methods and apparatus to capture photographs using mobile devices are disclosed. An example apparatus includes a photograph capturing controller to capture a photograph with a mobile device. The apparatus further includes a blurriness analyzer to determine a probability of blurriness of the photograph. The example apparatus also includes a photograph capturing interface to prompt a user to capture a new photograph when the probability of blurriness exceeds a blurriness threshold.

Claims

1. An apparatus comprising:

a photograph capturing controller to capture a photograph with a mobile device;

a blurriness analyzer to determine a probability of blurriness of the photograph; and

a photograph capturing interface to prompt a user to capture a new photograph when the probability of blurriness exceeds a blurriness threshold.

2. The apparatus of claim 1, wherein the blurriness analyzer is to determine the probability of blurriness by:

applying an edge detection filter to the photograph;

identifying pixels having a pixel value above a pixel value threshold;

estimating a variance of pixel values corresponding to the identified pixels; and

calculating the probability of blurriness based on the estimated variance.

3. The apparatus of claim 2, wherein the variance of the pixel values is a first variance of a plurality of variances of the pixel values, the first variance associated with a first area of a plurality of areas of the photograph, the blurriness analyzer to estimate the first variance based on the pixels identified within the first area.

4. The apparatus of claim 3, wherein the blurriness analyzer is to calculate the probability of blurriness by applying a logistic model to the plurality of variances of the pixel values corresponding to the plurality of areas of the photograph.

5. The apparatus of claim 1, wherein the blurriness analyzer is to:

divide the photograph into separate areas; and

determine the probability of blurriness based on an analysis of a subset of the separate areas corresponding to less than all of the separate areas.

6. The apparatus of claim 5, wherein the separate areas within the subset are spaced apart from a central region of the photograph.

7. The apparatus of claim 5, wherein the separate areas within the subset are adjacent sides of the photograph.

8. A non-transitory computer readable medium comprising instructions that, when executed, causes a mobile device to at least:

capture a photograph;

determine a probability of blurriness of the photograph; and

prompt a user to capture a new photograph when the probability of blurriness exceeds a blurriness threshold.

9. The non-transitory computer readable medium of claim 8, wherein the instructions further cause the mobile device to determine the probability of blurriness by:

applying an edge detection filter to the photograph;

identifying pixels having a pixel value above a pixel value threshold;

estimating a variance of pixel values corresponding to the identified pixels; and

calculating the probability of blurriness based on the estimated variance.

10. The non-transitory computer readable medium of claim 9, wherein the variance of the pixel values is a first variance of a plurality of variances of the pixel values, the first variance associated with a first area of a plurality of areas of the photograph, the instructions further causing the mobile device to estimate the first variance based on the pixels identified within the first area.

11. The non-transitory computer readable medium of claim 10, wherein the instructions further cause the mobile device to calculate the probability of blurriness by applying a logistic model to the plurality of variances of the pixel values corresponding to the plurality of areas of the photograph.

12. The non-transitory computer readable medium of claim 8, the instructions further cause the mobile device to:

divide the photograph into separate areas; and

determine the probability of blurriness based on an analysis of a subset of the separate areas corresponding to less than all of the separate areas.

13. The non-transitory computer readable medium of claim 12, wherein the separate areas within the subset are spaced apart from a central region of the photograph.

14. The non-transitory computer readable medium of claim 12, wherein the separate areas within the subset are adjacent sides of the photograph.

15. A method comprising:

capturing, by executing an instruction with a processor, a photograph with a mobile device;

determining, by executing an instruction with the processor, a probability of blurriness of the photograph; and

prompting, by executing an instruction with the processor, a user to capture a new photograph when the probability of blurriness exceeds a blurriness threshold.

16. The method of claim 15, wherein the determining of the probability of blurriness includes:

applying an edge detection filter to the photograph;

identifying pixels having a pixel value above a pixel value threshold;

estimating a variance of pixel values corresponding to the identified pixels; and

calculating the probability of blurriness based on the estimated variance.

17. The method of claim 16, wherein the variance of the pixel values is a first variance of a plurality of variances of the pixel values, the first variance associated with a first area of a plurality of areas of the photograph, the method further including estimating the first variance based on the pixels identified within the first area.

18. The method of claim 17, wherein the calculating of the probability of blurriness includes applying a logistic model to the plurality of variances of the pixel values corresponding to the plurality of areas of the photograph.

19. The method of claim 15, further including:

dividing the photograph into separate areas; and

determining the probability of blurriness based on an analysis of a subset of the separate areas corresponding to less than all of the separate areas.

20. The method of claim 19, wherein the separate areas within the subset are spaced apart from a central region of the photograph.