Automatic design of morphological algorithms for machine vision

• US 20030174892A1
• Filed: 01/09/2003
• Published: 09/18/2003
• Est. Priority Date: 01/09/2002
• Status: Active Grant

• Current Assignee: Siemens Corp. (Siemens AG)
• Original Assignee: Siemens Corporate Research Incorporated (Siemens AG)
• Inventors: Ramesh, Visvanathan, Boult, Terrance E., Gao, Xiang
• Granted Patent: US 7,428,337 B2
• Time in Patent Office: Days
• US Class Current: 382/225
• CPC Class Codes: G06F 18/21 Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
• CPC Class Codes: G06V 10/70 using pattern recognition or machine learning optical pattern recognition or electronic computations therefor G06V10/88
• CPC Class Codes: G06V 20/62 Text, e.g. of license plates, overlay texts or captions on TV images

Abstract

The present invention provides a technique for automated selection of a parameterized operator sequence to achieve a pattern classification task. A collection of labeled data patterns is input and statistical descriptions of the inputted labeled data patterns are then derived. Classifier performance for each of a plurality of candidate operator/parameter sequences is determined. The optimal classifier performance among the candidate classifier performances is then identified. Performance metric information, including, for example, the selected operator sequence/parameter combination, will be outputted. The operator sequences selected can be chosen from a default set of operators, or may be a user-defined set. The operator sequences may include any morphological operators, such as, erosion, dilation, closing, opening, close-open, and open-close.

Claims

1. A method for automated selection of a parameterized operator sequence to achieve a pattern classification task, comprising the steps of:

inputting a collection of labeled data patterns;

deriving statistical descriptions of the inputted labeled data patterns;

determining the criterion function which will be used to derive the classifier performance;

determining classifier performance for each of a plurality of candidate operator sequences and corresponding parameter values, using the derived statistical descriptions;

identifying an optimal classifier performance among the determined classifier performances according to specified criteria; and

selecting the operator sequence and corresponding parameter values, associated with the identified optimal classifier performance.

2. The method of claim 1, further including the step of outputting performance metric information.

3. The method of claim 2, wherein the outputted performance metric information includes the selected operator sequence and corresponding parameter values.

4. The method of claim 1, wherein the step of deriving statistical descriptions for input patterns includes deriving the statistical descriptions using a probability model.

5. The method of claim 4, wherein the probability model is a mixture of Hidden Markov Models (HMMs).

6. The method of claim 4, wherein the probability model is a Bayesian network.

7. The method of claim 4, wherein the probability model employs a non-parametric density representation.

8. The method of claim 1, wherein the inputted collection of data patterns includes at least one of patterns of interest and patterns of non-interest.

9. The method of claim 1, wherein the plurality of operator sequences includes default operator sequences.

10. The method of claim 1, wherein the plurality of operator sequences includes operator sequences defined by a user.

11. The method of claim 1, wherein the specified criteria relates to maximum expected classifier performance.

12. The method of claim 11, wherein the maximum expected classifier performance relates to balancing the tradeoff between false alarm errors and miss detection errors.

13. The method of claim 1, wherein the specified criteria are defined by a user.

14. The method of claim 1, wherein the plurality of operator sequences includes at least one of erosion, dilation, closing, opening, close-open, and open-close.

15. The method of claim 1, wherein the plurality of operator sequences includes an operator that maps an input Boolean vector to an output Boolean vector.

16. The method of claim 1, wherein the plurality of operator sequences includes an operator that is defined as successive application of a 1D filter in two orthogonal directions.

17. The method of claim 1, wherein the inputted collection of data patterns includes gray-level data transformed to a binary representation.

18. The method of claim 1, wherein the inputted collection of data patterns includes color data transformed to a binary representation.

19. The method of claim 1, wherein the step of determining classifier performance comprises the steps of:

for each candidate operator sequence and corresponding parameter values,

constructing an Embeddable Markov Chain (EMC), given the derived statistical descriptions for the input data patterns and output statistic to be calculated; and

calculating output statistics using the EMC the output statistics a function of the derived statistical descriptions for the inputted data patterns and a Boolean transformation.

20. The method of claim 19, wherein the step of constructin an EMC comprises the steps of:

(a) constructing a state space;

(b) building a state-transition graph with associated state-transition probabilities for the candidate operator sequence; and

a) partitioning the state-space.

21. A method for determining optimal classifier performance of a plurality of candidate operator sequences and corresponding parameter values, comprising the steps of:

for each candidate operator sequence and corresponding parameter values,

(a) constructing an Embeddable Markov Chain (EMC), given statistical descriptions for inputted data patterns and output statistic to be calculated;

(b) calculating the output statistics using the EMC; and

(c) selecting an optimal operator sequence and corresponding parameter values using the output statistics, according to specified criteria.

22. The method of claim 21, wherein the step of constructing an EMC comprises the steps of:

a) constructing a state space;

b) building a state-transition graph with associated state-transition probabilities for the candidate operator sequence; and

c) partitioning the state-space.

23. A program storage device readable by a machine, tangibly embodying a program of instructions executable on the machine to perform method steps for automated selection of a parameterized operator sequence to achieve a pattern classification task, the method steps comprising:

inputting a collection of labeled data patterns;

deriving statistical descriptions of the inputted labeled data patterns;

determining the criterion function which will be used to derive the classifier performance;

determining classifier performance for each of a plurality of candidate operator sequences and corresponding parameter values, using the derived statistical descriptions;

identifying an optimal classifier performance among the determined classifier performances according to specified criteria; and

selecting the operator sequence and corresponding parameter values, associated with the identified optimal classifier performance.