Pattern recognition method and apparatus for feature selection and object classification

• US 20060050953A1
• Filed: 06/20/2005
• Published: 03/09/2006
• Est. Priority Date: 06/18/2004
• Status: Abandoned Application

• Current Assignee: Eaton Corp. (Wisconsin) (Eaton Corporation PLC.)
• Original Assignee: Eaton Corp. (Wisconsin) (Eaton Corporation PLC.)
• Inventors: Farmer, Michael, Bapna, Shweta
• Application Number:
  US11/157,466
• Publication Number:
  US 20060050953A1
• Time in Patent Office: Days
• US Class Current: 382/159
• CPC Class Codes: B60R 21/01538 for image processing, e.g. cameras or sensor arrays
• CPC Class Codes: G06F 18/211 Selection of the most significant subset of features
• CPC Class Codes: G06V 10/50 by performing operations within image blocks; by using histograms, e.g. histogram of oriented gradients [HoG]; by summing image-intensity values; Projection analysis

Abstract

Methods and apparatus for processing features sampled and stored in a computing system are disclosed. Pattern recognition techniques are disclosed that facilitate decision making functions in computing systems, such as, for example, vehicle occupant safety systems and data mining applications. The disclosed correlation processing methods and apparatus improve the accuracy of data pattern recognition systems, including image processing systems.

Claims

1. A feature selection method for use in a data processing system, wherein the data processing system samples data containing a plurality of features associated with the data, and wherein the data processing system maintains an initial training data set, and wherein the initial training data set includes a plurality of features associated with the initial training data, comprising:

(a) sampling the data to derive at least one feature associated with the sampled data;

(b) synthesizing a feature vector from the at least one feature derived during step (a), wherein the feature vector includes one or more features associated with the data sampled at step (a);

(c) normalizing the feature vector synthesized at step (b), thereby creating a normalized feature vector;

(d) performing a non-parametric pair-wise feature test upon the normalized feature vector, wherein adjacent elements in the normalized feature vector are compared in a pair-wise manner thereby generating a plurality of tested features, wherein the tested features represent statistical relationships between the adjacent elements of the normalized feature vector;

(e) performing correlation processing upon the normalized feature vector, wherein the correlation processing includes;

(1) sorting the tested features generated in step (d);

(2) organizing the sorted tested features into a correlation matrix; and

(3) creating a correlation coefficient matrix corresponding and associated to the correlation matrix, wherein the correlation coefficient matrix includes information indicative of correlation between the tested features; and

(f) removing a selected feature from a training set if the selected feature is determined to be highly correlated to one or more other features in the training set based on the correlation processing performed in step (e).

2. The feature selection method of claim 1, wherein the sampled data comprises a plurality of images, and wherein the synthesizing step (b) further comprises creating a segmented image from a selected one of the plurality of images, and computing at least one mathematical moment of the segmented image.

3. The feature selection method of claim 2, further comprising computing at least one edge image from the segmented image.

4. The method of claim 3, wherein the at least one edge image is computed using geometric moments of the at least one edge image, wherein the geometric moments are computed in accordance with the following mathematical expression:

${\mathrm{\mu <br><br>}}_{\mathrm{mn}}=\underset{i=1}{\overset{M}{\sum <br><br>}}<br><br>\text{\hspace{1em}<br><br>}<br><br>\underset{j=1}{\overset{N}{\sum <br><br>}}<br><br>\text{\hspace{1em}<br><br>}<br><br>I<br><br>\left(i,j\right)·<br><br>{x<br><br>\left(i\right)}^m·<br><br>{y<br><br>\left(j\right)}^n.$

5. The method of claim 1, wherein the step (e)(1) of sorting the tested features further comprises ranking the tested features in descending order of Mann-Whitney z values.

6. The method of claim 5, wherein the Mann-Whitney z values are compared to a threshold, and wherein the Mann-Whitney z values exceeding the threshold are retained for further analysis.

7. The method of claim 1, wherein the step (e)(1) of sorting further comprises determining at least one feature that has a best pair-wise feature separability.

8. The method of claim 1, wherein the step (e)(1) of sorting further comprises computing a combined statistic for each of the tested features.

9. The method of claim 1, wherein the correlation coefficient matrix is computed in accordance with the following mathematical expression:

Correl_coeff(A,B)=Cov(A,B)/sqrt(Var(A)*Var(B)), wherein A and B comprise adjacent elements of the normalized vector.

15. An automated vehicle safety system, comprising:

(a) an imaging device capable of obtaining images of a vehicle occupant;

(b) a computing device, operatively coupled to the imaging device, wherein the computing device is configured to select features of the images of the vehicle occupants in accordance with the feature selection method set forth in claim 1, and wherein the vehicle occupant is classified as one of a plurality of classifications based upon the features selected in accordance with the feature selection method; and

(c) an automated safety device, responsive to the computing device, wherein the safety device is selectively deployed based on the vehicle occupant classification as determined by the computing device.

16. An safety equipment deployment system in a vehicle having a vision-based peripheral capable of capturing images of a vehicle occupant and storing the images in a memory for subsequent processing by a digital signal processor (DSP), comprising:

(a) a DSP configured to synthesize a plurality of feature arrays based upon the occupant images and storing the feature arrays in the memory, wherein the DSP is further configured to implement the feature selection method set forth in claim 1, and wherein the DSP classifies the vehicle occupant into one of a plurality of occupant classifications based upon the features selected by the feature selection method; and

(b) a vehicle safety device, responsive to the DSP, wherein the safety device is selectively deployed based on the vehicle occupant classification as determined by the DSP.

17. The system of claim 16, wherein the DSP is further configured to compute at least one mathematical moment of a segmented image.

18. The system of claim 17, wherein the DSP is further configured to compute at least one edge image from the segmented image.

19. The system of claim 18, wherein the DSP is further adapted to convert the at least one edge image into a one dimensional vector representation by computing mathematical moments of the at least one edge image.

20. The system of claim 16, wherein the vehicle safety device comprises an airbag.

10. A method of classifying an occupant of a vehicle interior into one of a plurality of occupant classifications, wherein images of the vehicle interior are captured by an imaging device, comprising:

(a) obtaining at least one image of the vehicle interior;

(b) synthesizing at least two feature arrays based upon the at least one image obtained during step (a);

(c) processing the at least two feature arrays synthesized in step (b) in accordance with a feature selection process, wherein the feature selection process normalizes the feature arrays and compares the at least two arrays to determine a significance of correlation between the arrays; and

(d) classifying the vehicle occupant as one of the plurality of occupant classifications.

11. The method of claim 10, wherein the synthesizing step further comprises computing at least one mathematical moment of a selected image, wherein the selected image is further processed and converted into a segmented image.

12. The method of claim 11, further comprising computing at least one edge image from the segmented image.

13. The method of claim 12, wherein the at least one edge image is computed using geometric moments of the segmented image, in accordance with the following mathematical expression:

${\mathrm{\mu <br><br>}}_{\mathrm{mn}}=\underset{i=1}{\overset{M}{\sum <br><br>}}<br><br>\text{\hspace{1em}<br><br>}<br><br>\underset{j=1}{\overset{N}{\sum <br><br>}}<br><br>\text{\hspace{1em}<br><br>}<br><br>I<br><br>\left(i,j\right)·<br><br>{x<br><br>\left(i\right)}^m·<br><br>{y<br><br>\left(j\right)}^n.$

14. A data processing system, wherein the data processing system samples data containing a plurality of features associated with the data, and wherein the data processing system maintains an initial training data set, and wherein the initial training data set includes a plurality of features associated with the initial training data, comprising:

(a) means for sampling the data to derive at least one feature associated with the sampled data;

(b) means, responsive to the sampling means, for synthesizing a feature vector from the at least one feature derived by the sampling means, wherein the feature vector includes one or more features associated with the sampled data;

(c) means, responsive to the synthesizing means, for normalizing the synthesized feature vector, thereby creating a normalized feature vector;

(d) means, coupled to the normalizing means, for performing a non-parametric pair-wise feature test upon the normalized feature vector, wherein adjacent elements in the normalized feature vector are compared in a pair-wise manner thereby generating a plurality of tested features, and wherein the tested features represent statistical relationships between the adjacent elements of the normalized feature vector;

(e) means, coupled to the non-parametric pair-wise feature test performing means, for performing correlation processing upon the normalized feature vector, wherein the correlation processing includes;

(1) means for sorting the tested features;

(2) means, responsive to the sorting means, for organizing the sorted tested features into a correlation matrix; and

(3) means, responsive to the organizing means, for creating a correlation coefficient matrix corresponding and associated to the correlation matrix, wherein the correlation coefficient matrix includes information indicative of correlation between the tested features; and

(f) means, responsive to the correlation processing means, for removing a selected feature from a training set if the selected feature is determined to be highly correlated to one or more other features in the training set.