Error propogation and variable-bandwidth mean shift for feature space analysis

• US 20040081359A1
• Filed: 03/06/2003
• Published: 04/29/2004
• Est. Priority Date: 03/06/2002
• Status: Active Grant

• Current Assignee: Palo Alto Networks Incorporated
• Original Assignee: Siemens Corporate Research Incorporated (Siemens AG)
• Inventors: Ramesh, Visvanathan, Comaniciu, Dorin, Bascle, Benedicte, Mittal, Anurag
• Granted Patent: US 7,437,006 B2
• Time in Patent Office: Days
• US Class Current: 382/181
• CPC Class Codes: G06V 10/25 Determination of region of interest [ROI] or a volume of interest [VOI]
• CPC Class Codes: G06V 10/48 by mapping characteristic values of the pattern into a parameter space, e.g. Hough transformation

Abstract

The present invention comprises using error propagation for building feature spaces with variable uncertainty and using variable-bandwidth mean shift for the analysis of such spaces, to provide peak detection and space partitioning. The invention applies these techniques to construct and analyze Hough spaces for line and geometrical shape detection, as well as to detect objects that are represented by peaks in the Hough space. This invention can be further used for background modeling by taking into account the uncertainty of the transformed image color and uncertainty of the motion flow. Furthermore, the invention can be used to segment video data in invariant spaces, by propagating the uncertainty from the original space and using the variable-bandwidth mean shift to detect peaks. The invention can be used in a variety of applications such as medical, surveillance, monitoring, automotive, augmented reality, and inspection.

Claims

1. A method for feature space analysis comprising the steps of:

providing input data comprising at least one of a plurality of objects of interest and a background, to be analyzed from at least one of a plurality of domains;

developing an uncertainty model of said input data in a feature space; and

using variable bandwidth mean shift to detect said at least one of a plurality of objects of interest within said feature space.

2. The method of claim 1, wherein said step of developing an uncertainty model includes deriving said uncertainty model through error propagation.

3. The method of claim 1, wherein said feature space includes joint spatial-color space.

4. The method of claim 1, wherein said feature space includes invariant space.

5. The method of claim 1, wherein said feature space includes parameter space.

6. The method of claim 1, wherein said feature space includes joint motion-color space.

7. The method of claim 1, wherein said at least one of a plurality of domains includes one or more of medical, surveillance, monitoring, automotive, inspection, and augmented reality.

8. The method of claim 1, wherein said step of using variable bandwidth mean shift to detect said at least one of a plurality of objects of interest within said feature space includes detecting an object represented by a peak in a Hough Transform.

9. The method of claim 8, wherein said step of using variable bandwidth mean shift to detect said at least one of a plurality of objects of interest within said feature space further includes clustering said feature space for image segmentation.

10. A program storage device readable by machine, tangibly embodying a program of instructions executable by the machine to perform method steps for feature space analysis, the method steps comprising:

providing input data comprising at least one of a plurality of objects of interest and a background, to be analyzed from at least one of a plurality of domains;

developing an uncertainty model of said input data in a feature space; and

using variable bandwidth mean shift to detect said at least one of a plurality of objects of interest within said feature space.

11. The program storage device of claim 10, wherein said step of developing an uncertainty model includes deriving said uncertainty model through error propagation.

12. The program storage device of claim 10, wherein said feature space of said using variable bandwidth mean shift method step includes joint spatial-color space.

13. The program storage device of claim 10, wherein said feature space of said using variable bandwidth mean shift method step includes invariant space.

14. The program storage device of claim 10, wherein said feature space of said using variable bandwidth mean shift method step includes parameter space.

15. The program storage device of claim 10, wherein said feature space of said using variable bandwidth mean shift method step includes joint motion-color space.

16. The program storage device of claim 10, wherein said at least one of a plurality of domains of said providing input data method step includes one or more of medical, surveillance, monitoring, automotive, inspection, and augmented reality.

17. The program storage device of claim 10, wherein said step of using variable bandwidth mean shift to detect said at least one of a plurality of objects of interest within said feature space includes detecting an object represented by a peak in a Hough Transform.

18. The program storage device of claim 17, wherein said step of using variable bandwidth mean shift to detect said at least one of a plurality of objects of interest within said feature space further includes clustering said feature space for image segmentation.

19. A method for feature space analysis comprising the step of:

modeling a background of an video image using uncertainties and multiple features comprising one or more of color, texture, and motion.

20. The method of claim 19, wherein said uncertainties include one or more of distance and probabilities.

21. The method of claim 19, further comprising the step of analyzing a video frame and adding a vector of features to said background model.

22. The method of claim 19, further comprising the step of analyzing a video frame and detecting a change by evaluating a vector of features and said background model.

23. The method of claim 22, wherein the step of analyzing a video frame and detecting a change by evaluating a vector of features and said background model includes determining if said vector of features exceeds a threshold and adjusting said background model accordingly.

24. The method of claim 22, further comprising the step of applying morphological operations to said detections.

25. A program storage device readable by machine, tangibly embodying a program of instructions executable by the machine to perform method steps for feature space analysis, the method step comprising:

modeling a background of an video image using uncertainties and multiple features comprising one or more of color, texture, and motion.

26. The program storage device of claim 25, wherein said uncertainties of said modeling step includes one or more of distance and probabilities.

27. The program storage device of claim 25, wherein the method further comprises the step of analyzing a video frame and adding a vector of features to said background model.

28. The program storage device of claim 25, wherein the method further comprises the step of analyzing a video frame and detecting a change by evaluating a vector of features and said background model.

29. The program storage device of claim 28, wherein said step of analyzing a video frame and detecting a change by evaluating a vector of features and said background model includes determining if said vector of features exceeds a threshold and adjusting said background model accordingly.

30. The program storage device of claim 28, wherein the method further comprises the step of applying morphological operations to said detections.