Hybrid template matching for imaging applications

• US 7,421,147 B2
• Filed: 08/02/2005
• Issued: 09/02/2008
• Est. Priority Date: 08/02/2005
• Status: Expired due to Fees

• Current Assignee: Intel Corporation
• Original Assignee: Xerox Corporation (Xerox Holdings Corp.)
• Inventors: Tse, Francis Kapo, Lee, Chia-Hao, Nagarajan, Ramesh
• Primary Examiner(s): Ahmed; Samir A.
• Assistant Examiner(s): Koziol; Stephen R
• Application Number:
  US11/196,144
• Publication Number:
  US 20070031061A1
• Time in Patent Office: 1,127 Days
• Field of Search: 382/159, 382/209, 382/217, 382/232, 382/276
• US Class Current: 382/276
• CPC Class Codes: G06T 2207/20081 Training; Learning
• CPC Class Codes: G06T 5/92 based on global image properties

Abstract

Training sets consisting of pairs of input images and desired images can be used to produce maps intended for future use in transforming input images into final images. The maps, however, often have improper elements based on incomplete or noisy data. The noisy data and incomplete can be identified via statistics derived from the training set. The map can output a code value instead of a substitution for an improper element. An additional stage of image transformation can identify improper pixels having the code value and replace the code value with an analytic value that is more appropriate.

Claims

1. A method comprising:

obtaining a map comprising at least one map element comprising a map template and a substitution and obtaining an input image comprising at least one input template;

a step for creating a transitional image comprising transitional pixels comprising at least one code value pixel having a value equal to a code value; and

a step for creating an output image from the transitional image.

2. The method of claim 1 wherein the transitional image is created in place from the input image.

3. The method of claim 1 wherein the output image is created in place from the transitional image.

4. The method of claim 1 wherein the input image is a binary image and the output image is a gray scale image.

5. The method of claim 1 further comprising using linear interpolation to find at least one analytic value wherein the at least one code value pixel is mapped to at least one output image pixel having the at least one analytic value.

6. The method of claim 1 further comprising using smoothing to find at least one analytic value wherein the at least one code value pixel is mapped to at least one output image pixel having the at least one analytic value.

7. The method of claim 1 further comprising using frequency interpolation to find at least one analytic value wherein the at least one code value pixel is mapped to at least one output image pixel having the at least one analytic value.

8. The method of claim 1 further comprising using a neighborhood mean as one of at least one analytic value wherein the at least one code value pixel is mapped to at least one output image pixel having the at least one analytic value.

9. The method of claim 1 further comprising using a neighborhood median as one of at least one analytic value wherein the at least one code value pixel is mapped to at least one output image pixel having the at least one analytic value.

10. The method of claim 1 further comprising using a text tag or image tag to guide the choice of the one of at least one analytic value that is copied to the output pixel.

11. A method comprising:

obtaining a training set comprising at least one training pair comprising a input image and desired image and creating an analysis set comprising at least one candidate comprising a candidate template and substitution statistics comprising at least one statistic; and

a step for classifying each of the at least one candidate as a normal candidate or a rogue candidate; and

a step for creating a map comprising a plurality of map elements each comprising a map template and a map substitution wherein rogue candidate result in map elements having map substitutions equaling a code value.

12. The method of claim 11 wherein a rogue candidate is a candidate having substitution statistics indicating a variance higher than a variance threshold.

13. The method of claim 11 wherein a rogue candidate is a candidate having substitution statistics indicating a spread higher than a spread threshold.

14. The method of claim 11 wherein a rogue candidate is a candidate having substitution statistics indicating symmetry lower than a symmetry threshold.

15. The method of claim 11 wherein a rogue candidate is a candidate having substitution statistics indicating a population less than a population threshold.

16. The method of claim 11 wherein at least one of the map substitutions is a mean.

17. The method of claim 11 wherein at least one of the map substitutions is a median.

18. The method of claim 11 wherein at least one of the map substitutions is a most common value.

19. The method of claim 11 wherein the input image in each of the at least one training pair is a half tone image and the desired image in each of the at least one training pair is a gray scale image.

20. A system comprising:

a storage device storing a map comprising a plurality of map elements each comprising a map template and a substitution wherein the substitution of at least one of the map elements equals a code value;

a means for obtaining an input image;

a means for producing a transitional image wherein the transitional image comprises a plurality of transitional pixels and at least one of the transitional pixels has a value equaling the code value; and

a means for producing an output image.