Hand pointing estimation for human computer interaction
First Claim
1. A method for estimating a finger pointing direction using an active appearance model, which tracks a plurality of landmarks on the hand, corresponding to landmarks of hands in a training image set to which a principal component analysis is applied to formulate a statistical model of the hand, comprising:
- detecting a hand in each of at least two images acquired from different angles with at least one automated processor, comprising generating Haar-like features from the at least two images, implementing a cascade detector using AdaBoost, wherein portions of each image are scanned over different translations, scales and rotations to find a best fit for the hand, and locating a center of the hand and a position of the wrist in each image;
warping the detected hand in each image from a Cartesian Coordinate representation to a Polar Coordinate representation, with the center of the hand at the pole, and the polar angle determined by a position of the wrist with respect to the center of the hand;
applying the active appearance model to find a best fit for visible landmarks of the hand in each image;
combining the best fit for the visible landmarks of the hand in each image to the active appearance model to infer a three dimensional position of each visible landmark in each image;
determining at least two visible features of a finger extending from the hand, to define a pointing gesture in each image; and
determining with the at least one automated processor, at least one of a three dimensional pointing vector of the finger and a target of the pointing gesture.
3 Assignments
0 Petitions
Accused Products
Abstract
Hand pointing has been an intuitive gesture for human interaction with computers. A hand pointing estimation system is provided, based on two regular cameras, which includes hand region detection, hand finger estimation, two views'"'"' feature detection, and 3D pointing direction estimation. The technique may employ a polar coordinate system to represent the hand region, and tests show a good result in terms of the robustness to hand orientation variation. To estimate the pointing direction, Active Appearance Models are employed to detect and track, e.g., 14 feature points along the hand contour from a top view and a side view. Combining two views of the hand features, the 3D pointing direction is estimated.
-
Citations
20 Claims
-
1. A method for estimating a finger pointing direction using an active appearance model, which tracks a plurality of landmarks on the hand, corresponding to landmarks of hands in a training image set to which a principal component analysis is applied to formulate a statistical model of the hand, comprising:
-
detecting a hand in each of at least two images acquired from different angles with at least one automated processor, comprising generating Haar-like features from the at least two images, implementing a cascade detector using AdaBoost, wherein portions of each image are scanned over different translations, scales and rotations to find a best fit for the hand, and locating a center of the hand and a position of the wrist in each image; warping the detected hand in each image from a Cartesian Coordinate representation to a Polar Coordinate representation, with the center of the hand at the pole, and the polar angle determined by a position of the wrist with respect to the center of the hand; applying the active appearance model to find a best fit for visible landmarks of the hand in each image; combining the best fit for the visible landmarks of the hand in each image to the active appearance model to infer a three dimensional position of each visible landmark in each image; determining at least two visible features of a finger extending from the hand, to define a pointing gesture in each image; and determining with the at least one automated processor, at least one of a three dimensional pointing vector of the finger and a target of the pointing gesture. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
-
-
11. A method for estimating a finger pointing direction using an active appearance model, which tracks a plurality of landmarks on the hand, corresponding to landmarks of hands in a training image set to which a principal component analysis is applied to formulate a statistical model of the hand, comprising:
-
capturing at least two images of a hand from different directions; detecting the hand in each image and locating a center of the hand with at least one automated processor, comprising generating Haar-like features from the at least two images, and implementing a cascade detector using AdaBoost, wherein portions of each image are scanned over different translations, scales and rotations to find a best fit for the hand; determining a position of the wrist in each image; warping the detected hand in each image from a Cartesian Coordinate representation to a Polar Coordinate representation, with the center of the hand at the pole, and the polar angle determined by a position of the wrist with respect to the center of the hand; applying the active appearance model to find a best fit for the hand in each image; combining the fit of the hand in each image to the active appearance model to infer a three dimensional position of each visible point in an image; determining two points at different distances along a finger extending from the hand, to define a pointing vector in the at least two images, wherein the two points are selected based on a reliability of the resulting vector; and determining with at least one automated processor, a three dimensional pointing vector of the finger. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18)
-
-
19. An apparatus for estimating a finger pointing direction, comprising:
-
a memory configured to store parameters of an active appearance model of a hand, which includes a plurality of landmarks on the hand, corresponding to landmarks of hands in a training image set to which a principal component analysis is applied; an input configured to receive at least two images acquired from different angles; at least one processor configured to receive the input, access the memory, and produce an output based on the at least two images and the parameters in the memory; and an output port, communicating data responsive to a finger pointing direction of the hand, wherein the at least one processor is further configured to; detect a hand in each of the at least two images acquired from different angles, by generating Haar-like features from the at least two images, and implementing a cascade detector using AdaBoost, wherein portions of each image are scanned over different translations, scales and rotations to find a best fit for the hand, and locating a center of the hand and a position of the wrist in each image; warp the detected hand in each image from a Cartesian Coordinate representation to a Polar Coordinate representation, with the center of the hand at the pole, and the polar angle determined by a position of the wrist with respect to the center of the hand; apply the active appearance model, based on the parameters in the memory, to find a best fit for visible landmarks of the hand in each image; combine the best fit for the visible landmarks of the hand in each image to the active appearance model to infer a three dimensional position of each visible landmark in each image; determine at least two visible features of a finger extending from the hand, to define a pointing gesture in each image; and determine a pointing direction comprising at least one of a three dimensional pointing vector of the finger and a target of the pointing gesture. - View Dependent Claims (20)
-
Specification