Human Computer Interfaces (HCl) may allow a user to interact with a computer via a variety of mechanisms, such as hand, head, and body gestures. Various of the disclosed embodiments allow information captured from a depth camera on an HCl system to be used to recognize such gestures. Particularly, the HCl system's depth sensor may capture depth frames of the user's movements over time. To discern gestures from these movements, the system may group portions of the user's anatomy represented by the depth data into classes. “Features” which reflect distinguishing features of the user's anatomy may be used to accomplish this classification. Some embodiments provide improved systems and methods for generating and/or selecting these features. Features prepared by various of the disclosed embodiments may be less susceptible to overfitting training data and may more quickly distinguish portions of the user's anatomy.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A device configured to generate a distribution of feature offsets comprising: at least one processor; at least one memory comprising instructions configured to cause the at least one processor to perform a method comprising: determining a Gaussian function; sampling the Gaussian function to generate a first distribution; generating a second distribution by iteratively sampling the first distribution and selecting elements of the first distribution best satisfying a metric; and publishing the second distribution as a plurality of feature offsets. 2. The device of claim 1, wherein the metric considers the distance of each element to the origin of the second distribution. 3. The device of claim 1, wherein the metric considers the distance of each element to the existing members of the second distribution. 4. The device of claim 1, wherein the metric considers the distance of each element to the origin of the second distribution and the distance of each element to the existing members of the second distribution. 5. The device of claim 1, wherein the metric comprises the formula: Sub_Metric   ( P , W ) = e - ( P x + W x 2 ) 2 - ( P y + W y 2 ) 2  ( P x - W x ) 2 + ( P y - W y ) 2 6. The device of claim 1, the method further comprising: receiving a plurality of training images; and generating a plurality of trees from the training images and the second distribution. 7. The device of claim 1, wherein the device is coupled to a Human Computer Interaction (HCl) system. 8. A computer-implemented method to generate a distribution of feature offsets comprising. determining a Gaussian function; sampling the Gaussian function to generate a first distribution; generating a second distribution by iteratively sampling the first distribution and selecting elements of the first distribution best satisfying a metric; and publishing the second distribution as a plurality of feature offsets. 9. The computer-implemented method of claim 8, wherein the metric considers the distance of each element to the origin of the second distribution. 10. The computer-implemented method of claim 8, wherein the metric considers the distance of each element to the existing members of the second distribution. 11. The computer-implemented method of claim 8, wherein the metric considers the distance of each element to the origin of the second distribution and the distance of each element to the existing members of the second distribution. 12. The computer-implemented method of claim 8, wherein the metric comprises the formula: Sub_Metric   ( P , W ) = e - ( P x + W x 2 ) 2 - ( P y + W y 2 ) 2  ( P x - W x ) 2 + ( P y - W y ) 2 13. The computer-implemented method of claim 8, the method further comprising: receiving a plurality of training images; and generating a plurality of trees from the training images and the second distribution. 14. The computer-implemented method of claim 8, wherein the device is coupled to a Human Computer Interaction (HCl) system. 15. A non-transitory computer-readable medium comprising instructions configured to cause one or more computer processors to cause a method to be performed comprising: determining a Gaussian function; sampling the Gaussian function to generate a first distribution; generating a second distribution by iteratively sampling the first distribution and selecting elements of the first distribution best satisfying a metric; and publishing the second distribution as a plurality of feature offsets. 16. The non-transitory computer-readable medium of claim 15, wherein the metric considers the distance of each element to the origin of the second distribution. 17. The non-transitory computer-readable medium of claim 15, wherein the metric considers the distance of each element to the existing members of the second distribution. 18. The non-transitory computer-readable medium of claim 15, wherein the metric considers the distance of each element to the origin of the second distribution and the distance of each element to the existing members of the second distribution. 19. The non-transitory computer-readable medium of claim 15, wherein the metric comprises the formula: Sub_Metric   ( P , W ) = e - ( P x + W x 2 ) 2 - ( P y + W y 2 ) 2  ( P x - W x ) 2 + ( P y - W y ) 2 20. The non-transitory computer-readable medium of claim 15, the method further comprising: receiving a plurality of training images; and generating a plurality of trees from the training images and the second distribution.