Identifying consumer products in images

• US 9,858,481 B2
• Filed: 11/23/2015
• Issued: 01/02/2018
• Est. Priority Date: 11/23/2015
• Status: Active Grant

• Current Assignee: Lexmark International Incorporated (Ninestar Corporation)
• Original Assignee: Lexmark International Incorporated (Ninestar Corporation)
• Inventors: Booth, Robert Reed
• Primary Examiner(s): CONNER, SEAN M
• Application Number:
  US14/948,499
• Publication Number:
  US 20170147881A1
• Time in Patent Office: 771 Days
• Field of Search: None
• CPC Class Codes: G06T 3/60 Rotation of whole images or parts thereof
• CPC Class Codes: G06V 10/32 Normalisation of the pattern dimensions
• CPC Class Codes: G06V 10/457 by analysing connectivity, e.g. edge linking, connected component analysis or slices
• CPC Class Codes: G06V 10/462 Salient features, e.g. scale invariant feature transforms [SIFT]
• CPC Class Codes: G06V 10/757 Matching configurations of points or features

Abstract

Systems and methods identify consumer products in images. Known consumer products are captured as grayscale or color images. They are converted to binary at varying thresholds. Connected components in the binary images identify image features according to pixels of a predetermined size, shape, solidity, aspect ratio, and the like. The image features are stored and searched for amongst image features similarly extracted from unknown images of consumer products. Identifying correspondence between the features of the images lends itself to identifying or not known consumer products.

Claims

1. A method for identifying consumer products in images, comprising:

capturing first images of known consumer products, pixels of the first images corresponding to grayscale or color;

converting to binary the pixels of the first images according to three different thresholds to obtain three binary first images having pluralities of binary pixels;

receiving second images of unknown consumer products, pixels of the second images corresponding to grayscale or color;

converting to binary the pixels of the second images according to said three different thresholds to obtain three binary secondary images having pluralities of binary pixels;

finding three image features corresponding to one another in each of the three binary first images and the three binary secondary images;

devising triangles between the three image features in said each of the first and the second images; and

determining if the triangles are similar to identify or not possible matches of the known consumer products within the second images, thus identifying or not known consumer products at a location from whence the second images were captured.

2. The method of claim 1, further including determining a centroid pixel in said three image features corresponding to one another in said each of the three binary first images and the three binary secondary images.

3. The method of claim 2, further including determining line lengths between each of the three centroid pixels.

4. The method of claim 3, further including comparing the line lengths between said each of the first and second images.

5. The method of claim 1, further including restricting a pixel distance between the three image features during the finding of the three image features.

6. The method of claim 1, further including determining whether or not the three image features reside in a corresponding portion of the first and second images.

7. The method of claim 1, further including determining whether or not the three image features have a corresponding orientation in the first and second images.

8. The method of claim 1, further including determining whether or not the three image features correspond mostly to black or white image features.

9. The method of claim 1, further including determining an aspect ratio in pixels for the three image features.