HIERARCHICAL IMAGE CLASSIFICATION SYSTEM

• US 20140270347A1
• Filed: 03/13/2013
• Published: 09/18/2014
• Est. Priority Date: 03/13/2013
• Status: Abandoned Application

• Current Assignee: Sharp Laboratories of America Incorporated (Hon Hai Precision Industry Co., Ltd.)
• Original Assignee: Sharp Laboratories of America Incorporated (Hon Hai Precision Industry Co., Ltd.)
• Inventors: XU, Xinyu, VAN BEEK, Petrus J.L., CHEN, Xu
• Application Number:
  US13/798,760
• Publication Number:
  US 20140270347A1
• Time in Patent Office: Days
• US Class Current: 382/103
• CPC Class Codes: G06F 18/24323 Tree-organised classifiers
• CPC Class Codes: G06T 2207/20036 Morphological image processing
• CPC Class Codes: G06T 2207/20081 Training; Learning
• CPC Class Codes: G06T 2207/30121 CRT, LCD or plasma display
• CPC Class Codes: G06T 7/001 using an image reference approach
• CPC Class Codes: G06T 7/344 involving models
• CPC Class Codes: G06V 10/764 using classification, e.g. of video objects

Abstract

A technique for image processing that includes receiving a model image, an input image, and registering the input image with the model image. A modified input image is determined that includes a first component that is substantially free of error components with respect to the model image and a second component that is substantially free of non-error aspects with respect to the model image. The technique determines an improved alignment of the modified input image with the model image where the improved alignment and the first and second components are determined jointly.

Claims

1. A method for image processing comprising:

(a) learning a model hierarchical classification structure of a plurality of different objects, wherein said hierarchical classification structure includes one multi-class classifier or multiple binary classifiers in the first layer for categorizing a plurality of first layer classes each of which is characterizing one of said plurality of different objects, and one multi-class classifier or multiple binary classifiers in the second layer for categorizing a plurality of second layer classes wherein each of said second layer of classes further characterizes one of plurality of first layer classes;

(b) receiving an input image;

(c) categorizing said input image using a statistical model using said first layer classifier(s) of the model hierarchical classification structure for said first layer of said plurality of first layer classes;

(d) further categorizing said input image using a statistical model using said second layer classifier(s) of the model hierarchical classification structure for said second layer of said plurality of second layer classes, where said categorizing of step (c) among said first layer of said plurality of first layer classes is independent of the classification decision of said categorizing of step(d) among said second layer of said plurality of second layer classes.

2. The method of claim 1 further categorizing said input image using a statistical model using said model hierarchical classification structure for at least one additional layer of classes.

3. The method of claim 1 wherein said categorizing of said input image using said statistical model using said model hierarchical classification structure for said second layer of said plurality of second layer classes includes at least one of (1) only those said plurality of second layer classes that further categorize one or more of said first plurality of classes selected as a result of step(c) and (2) is dependent of the classification decision of said categorizing among said first layer of said plurality of first layer classes,

4. The method of claim 1 wherein said model hierarchical classification structure is generated in a top-down fashion by either training a single multi-class classifier for each internal class node at each layer, or training multiple binary classifiers for each internal class node at each layer.

5. The method of claim 1 wherein said classification model of said each internal classifier of said hierarchical classification structure includes at least one of a SVM, a Random Trees, a Neural Network, a Decision Trees and a Bayesian Classifier.

6. The method of claim 1 wherein said model hierarchical classification structure wherein each internal classifier associated with each internal class node includes a rule set and a feature set for discriminating the objects of one child-class of said internal class node from the objects of the other child-class of said internal class node.

7. The method of claim 6 wherein the set of features used at said each internal node of said model hierarchical classification structure may be automatically discovered by at least one of (1) a feature selection technique (e.g., using mutual information between a feature F and a category C), or and (2) defined by an operator where the operator selects discriminating features for differentiating the sub-classes.

8. A method for image processing comprising:

(a) providing a model image;

(b) providing a landmark image defining relevant structures within said model image;

(b) receiving an input image;

(c) aligning said input image with said model image based at least in part upon said landmark image;

(d) wherein features of said input image corresponding to greater discriminative landmarks of said landmark image have a higher contribution to said alignment than less discriminate landmarks of said landmark image.

9. The method of claim 8 wherein said aligning includes a matching score of a plurality of candidates.

10. The method of claim 9 wherein said discriminative landmarks selects among a plurality of said candidates.

11. A method for image processing comprising:

(a) providing a model image;

(b) receiving an input image;

(c) aligning said input image with said model image based at least upon edges of said input image and edges of said model image;

(d) scoring said aligning based upon a different contribution for said edges of said input image that match with said edges of said model image and a contribution for edges of said model image that are not matched with said input image.

12. The method of claim 11 wherein said scoring for one of said contributions is a negative score.

13. The method of claim 12 wherein said scoring for one of said contributions is a positive score.

14. A method for image processing comprising:

(a) providing a model image;

(b) receiving an input image;

(c) estimating a blur of said input image;

(d) aligning said input image with said model image if said estimated blur is less than a threshold value.

15. The method of claim 14 wherein said blur is estimated based upon edge width.

16. The method of claim 15 wherein said edge width is based upon edges of landmark structures in said input image.

17. A method for image processing comprising:

(a) providing a model image;

(b) providing a landmark image defining relevant structures within said model image;

(c) receiving an input image;

(d) modifying said model image by replacing a background region of said model image, as defined by said landmark image, with a dominant background color of said input image;

(e) modifying said model image by replacing a landmark region of said model image, as defined by said landmark image, with a dominant landmark color of said input image;

(f) detecting defects within said input image by comparing said input image with said modified model image.

18. The method of claim 17 wherein said detecting defects is based upon a difference between said input image and said modified model image.

19. A method for image processing comprising:

(a) providing a model image;

(b) providing a landmark image defining relevant structures within said model image;

(c) receiving an input image;

(d) detecting defects within said input image by comparing said input image with said modified model image based upon a dilation of landmarks defined by said landmark image.

20. The method of claim 19 wherein said comparing is further based upon attenuating small variations.

21. The method of claim 20 wherein said comparing is further based upon applying an adaptive threshold.

22. The method of claim 21 wherein said comparing is further based upon applying a relative threshold.

23. The method of claim 19 wherein said dilation of landmarks defined by said landmark image includes (1) dilation of landmark straight boundaries, and (2) dilation of corners of said landmark boundaries.

24. A method for image processing comprising:

(a) providing a model image;

(b) receiving an input image;

(c) determining a difference image between said model image and said input image;

(d) identifying defects in said difference image based upon using a plurality of detectors, each of said plurality of detectors being different from one another, one of said plurality of detectors identifying curved boundaries connected with a landmark boundary of a landmark image, and another of said plurality of detectors identifying straight lines a landmark boundary of said landmark image.

25. The method of claim 24 wherein a first of said detectors is a COAT detector, a second of said detectors is a SANSO detector.

26. The method of claim 24 wherein one of said identified defects is selected based upon overlapping.

27. A method for image processing comprising:

(a) providing a model image;

(b) receiving an input image;

(c) determining a difference image between said model image and said input image;

(d) identifying first defects in said difference image;

(e) identify second defects in said input image based upon a color distribution of said input image;

(f) selecting one of said first defects and said second defects.

28. The method of claim 27 wherein said second defects are GI defects.

29. The method of claim 27 wherein said first defects are at least one of SANSO, UWANO, and COAT.

30. A method for image processing comprising:

(a) providing a model image;

(b) receiving an input image;

(c) identifying defects in said input image based upon color fringes in said input image and based upon color fringes in a difference image between said model image and said input image.

31. The method of claim 30 wherein said identifying defects is GI defects.