Systems and methods of tracking moving hands and recognizing gestural interactions

1. A method of determining command input to a machine responsive to gestures in three dimensional (3D) sensory space, the method comprising:

determining a variance between a point on a set of observation information based on an image captured at time t 1 and a corresponding point on at least one of a set of 3D capsules fitted to another set of observation information based on an image captured at time t 0 by:

pairing point sets from points on a surface of the observation information with points on the 3D capsules, wherein normal vectors to points on the set of observation information are parallel to normal vectors to points on the 3D capsules; and

determining the variance comprising a reduced root mean squared deviation (RMSD) of distances between paired point sets; and

responsive to the variance, adjusting the 3D capsules; and

determining a gesture performed by the at least a portion of a hand based on the 3D capsules as adjusted; and

interpreting the gesture as providing command input to a machine.

2. The method of claim 1 , wherein adjusting the 3D capsules further includes improving conformance of the 3D capsules to at least one of length, width, orientation, and arrangement of portions of the observation information.

3. The method of claim 1 , further including:

determining span modes of the hand, wherein the span modes include at least a finger width span mode and a palm width span mode; and

using span width parameters for the finger width span mode and the palm width span mode to initialize 3D capsules of a 3D model of the hand.

4. The method of claim 1 , further including:

determining span modes of the hand, wherein the span modes include at least a finger width span mode, a palm width span mode, and a wrist width span mode; and

using span width parameters for the finger width span mode, palm width span mode, and wrist width span mode to initialize a 3D model of the hand and corresponding arm.

5. The method of claim 1 , further including interpreting the gesture as selecting one or more heterogeneous devices.

6. The method of claim 1 , further including interpreting the gesture as selecting one or more heterogeneous marker images that trigger augmented illusions.

7. The method of claim 1 , further including automatically switching the machine under control from one operational mode to another in response to interpreting the gesture.

8. The method of claim 1 , wherein determining the variance further includes determining whether the point on another set of observation information based on the image captured at time t 1 and the corresponding point on one of the 3D capsules fitted to the observation information defined based on the image captured at time t 0 are within a threshold closest distance.

9. The method of claim 1 , wherein determining the variance further includes:

pairing point sets on an observation information of the at least a portion of a hand with points on axes of the 3D capsules, wherein points on observation information lie on vectors that are normal to points on axes; and

determining a reduced root mean squared deviation (RMSD) of distances between paired point sets.

10. The method of claim 1 , further including determining at least one of a velocity of a portion of a hand, a state, a pose.

11. The method of claim 10 , wherein the determining a velocity further includes determining at least one of a velocity of one or more fingers, and a relative motion of a portion of the hand.

12. The method of claim 10 , wherein the determining a state further includes determining at least one of a position, an orientation, and a location of a portion of the hand.

13. The method of claim 10 , wherein the determining a pose further includes determining at least one of whether one or more fingers are extended or non-extended, one or more angles of bend for one or more fingers, a direction to which one or more fingers point, a configuration indicating a pinch, a grab, an outside pinch, and a pointing finger.

14. The method of claim 10 , further including determining whether a tool or object is present in the hand.

15. The method of claim 1 , further comprising:

determining gesture features for the at least a portion of a hand based on the 3D capsules; and

issuing a feature-specific command input to a machine under control based on the gesture features.

16. The method of claim 15 , wherein the gesture features include edge information for at least one of fingers of the hand and palm of the hand.

17. The method of claim 15 , wherein gesture features include at least one of joint angle and segment orientation information of the hand, and finger segment length information for fingers of the hand.

18. The method of claim 15 , wherein the gesture features include at least one of curling of the hand during gestural motion and a pose, a grab strength, a pinch strength and a confidence of the hand.

19. The method of claim 1 , further comprising:

determining biometric features for the at least a portion of a hand based on the 3D capsules;

authenticating the at least a portion of a hand based on the biometric features determined;

upon determining the command input indicated by gestural motion of the at least a portion of a hand, determining whether the at least a portion of a hand is authorized to issue the command input; and

issuing an authorized command input to a machine under control.

20. The method of claim 19 , wherein the biometric features determined include at least one of measurements across a palm of the hand and finger width at a first knuckle of the hand.

21. A non-transitory computer readable storage medium impressed with computer program instructions to determine command input to a machine responsive to gestures in three dimensional (3D) sensory space, which instructions, when executed on a processor, implement actions comprising:

determining a variance between a point on a set of observation information based on an image captured at time t 1 and a corresponding point on at least one of a set of 3D capsules fitted to another set of observation information based on an image captured at time t 0 by:

pairing point sets from points on a surface of the observation information with points on the 3D capsules, wherein normal vectors to points on the set of observation information are parallel to normal vectors to points on the 3D capsules; and

determining the variance comprising a reduced root mean squared deviation (RMSD) of distances between paired point sets; and

responsive to the variance, adjusting the 3D capsules; and

determining a gesture performed by the at least a portion of a hand based on the 3D capsules as adjusted; and

interpreting the gesture as providing command input to a machine.

22. A system to determine command input to a machine responsive to gestures in three dimensional (3D) sensory space, comprising:

a processor and a computer readable storage medium storing computer instructions configured to cause the processor to:

determining a variance between a point on a set of observation information based on an image captured at time t 1 and a corresponding point on at least one of a set of 3D capsules fitted to another set of observation information based on an image captured at time t 0 by:

pairing point sets from points on a surface of the observation information with points on the 3D capsules, wherein normal vectors to points on the set of observation information are parallel to normal vectors to points on the 3D capsules; and

determining the variance comprising reduced root mean squared deviation (RMSD) of distances between paired point sets; and

responsive to the variance, adjusting the 3D capsules; and

determine a gesture performed based on the 3D capsules as adjusted; and

interpret the gesture as providing command input to a machine.