Object shape determination method and system therefor

• US 7,330,035 B2
• Filed: 05/21/2004
• Issued: 02/12/2008
• Est. Priority Date: 05/31/2003
• Status: Expired due to Fees

• Current Assignee: Koninklijke Philips Electronics N.V. (Koninklijke Philips N.V.)
• Original Assignee: Koninklijke Philips Electronics N.V. (Koninklijke Philips N.V.)
• Inventors: Van Berkel, Cornelis
• Primary Examiner(s): Nguyen; Vincent Q.
• Assistant Examiner(s): Nguyen; Hoai-An D.
• Application Number:
  US10/557,656
• Publication Number:
  US 20070063707A1
• Time in Patent Office: 1,362 Days
• Field of Search: 324/671, 324/663, 324/658, 324/649, 324/600, 324/662, 324/690, 324/687, 324/688, 340/870.37
• US Class Current: 324/671
• CPC Class Codes: G06F 3/044 by capacitive means

Abstract

An unknown object shape is computed using repeated iterations of electrostatic calculations on a predetermined shaped object and sensed electrostatic measurements. The predetermined object shape is morphed toward the unknown object shape at each iteration until the object shape is determined. The morphing of the shape is based on calculating the zero contour of the electrostatic potential, which potential depends on a charge distribution applied to the predetermined shape.

Claims

1. A method for determining the shape of an unknown object placed in an electrostatic sensing region extending from sensor apparatus comprising a plurality of electrostatic receiving means and at least one electrostatic transmission means, the method comprising:

measuring the change in charge induced by said unknown object on each receiving means and storing said charge measurements as a first charge image dataset C_f,

retrieving a predetermined shape dataset representing point co-ordinates which define said predetermined shape,

determining for the first charge image dataset C_f a charge distribution dataset q′

representing the charge distribution required on the predetermined shape to substantially result in the measured charge image C_f,

calculating the electrostatic potential distribution in the sensing region corresponding to said charge distribution dataset q′

,

generating a new shape dataset having co-ordinates corresponding to points in the sensing region where the electrostatic potential distribution is close to zero, and

determining the new shape dataset as representing the shape of the unknown object in the sensing region by comparing the difference between the new shape dataset and the predetermined shape dataset against a predetermined threshold.

2. The method of claim 1, wherein the determination of the charge image distribution q′

comprises calculating for the predetermined shape;

M, a matrix dataset comprising elements describing the electrostatic exchange potential between each point co-ordinate of the predetermined shape and each point co-ordinate of the receiving means,

G, a matrix dataset comprising elements describing the electrostatic exchange potential between each point co-ordinate of the predetermined shape and each other point of the predetermined shape,

t, a vector dataset describing the electrostatic exchange potential between each point co-ordinate of the predetermined shape and the point co-ordinate of the transmission means, and

wherein a charge distribution q₀ on the predetermined shape is used to regularise said determination of q′

from C_f=Mq′

.

3. The method of claim 2, wherein q₀ is calculated from t=Gq₀.

4. The method of claim 1, wherein the comparison step comprises calculating a secondary charge image distribution C_f′

for the new shape dataset, and wherein the difference for comparison against the predetermined threshold is computed by comparing the sum of the squared differences between C_f′ and

C_f′

.

5. The method of claim 1, wherein the point co-ordinates of said predetermined shape dataset define triangular elements of said known shape.

6. The method of claim 5, wherein the new shape dataset is generated by extrapolating the distance by which to move each triangular element in the sensing region to points where the electrostatic potential distribution is zero.

7. The method of claim 1, wherein the predetermined threshold is 5%.

8. A method for controlling a device comprising:

determining an object shape by the method of claim 1,

selecting from a list of control actions associated with predefined shapes a control action associated with said determined object shape, and executing said selected control action.

9. A system for determining the shape of an unknown object placed in an electrostatic sensing region extending from sensor apparatus comprising at least one electrostatic transmission means and a plurality of electrostatic receiving means, storage means for storing a predetermined shape dataset, and processing means for carrying out the method of claim 1.

10. The system of claim 9, further comprising:

selecting means for selecting from a stored list of control actions associated with predefined shapes a control action associated with said determined object shape, and

control means for executing said selected control action.

11. The method of claim 2, wherein the comparison step comprises calculating a secondary charge image distribution C_f′

for the new shape dataset, and wherein the difference for comparison against the predetermined threshold is computed by comparing the sum of the squared differences between C_f′ and

C_f′

.

12. A program code carrier carrying program code which when supplied to processing means cause said processor to carry out the method of claim 1.

13. The method of claim 3, wherein the comparison step comprises calculating a secondary charge image distribution C_f′

for the new shape dataset, and wherein the difference for comparison against the predetermined threshold is computed by comparing the sum of the squared differences between C_f′ and

C_f′

.

14. The method of claim 13, wherein the point co-ordinates of said predetermined shape dataset define triangular elements of said known shape.

15. The method of claim 3, wherein the point co-ordinates of said predetermined shape dataset define triangular elements of said known shape.

16. The method of claim 4, wherein the point co-ordinates of said predetermined shape dataset define triangular elements of said known shape.

17. The method of claim 2, wherein the point co-ordinates of said predetermined shape dataset define triangular elements of said known shape.

18. The method of claim 17, wherein the new shape dataset is generated by extrapolating the distance by which to move each triangular element in the sensing region to points where the electrostatic potential distribution is zero.

19. The method of claim 15, wherein the new shape dataset is generated by extrapolating the distance by which to move each triangular element in the sensing region to points where the electrostatic potential distribution is zero.

20. The method of claim 16, wherein the new shape dataset is generated by extrapolating the distance by which to move each triangular element in the sensing region to points where the electrostatic potential distribution is zero.