Abstract: Various of the disclosed embodiments provide Human Computer Interfaces (HCI) that incorporate depth sensors at multiple positions and orientations. The depth sensors may be used in conjunction with a display screen to permit users to interact dynamically with the system, e.g., via gestures. Calibration methods for orienting depth values between sensors are also presented. The calibration methods may generate both rotation and translation transformations that can be used to determine the location of a depth value acquired in one sensor from the perspective of another sensor. The calibration process may itself include visual feedback to direct a user assisting with the calibration. In some embodiments, floor estimation techniques may be used alone or in conjunction with the calibration process to facilitate data processing and gesture identification.

Abstract: Various of the disclosed embodiments present systems and methods for distinguishing portions of a virtual model associated with a clothing article from portions of the virtual model not associated with the clothing article. Some embodiments facilitate quick and effective separation by employing a feature vector structure conducive to separation by a linear classifier. Such efficient separation may be especially beneficial in applications requiring the rapid scanning of large quantities of clothing while retaining high-fidelity representations of the clothing's geometry. Some embodiments further accommodate artist participation in the filtering process as well as scanning of articles from a variety of orientations and with a variety of supporting structures.

Abstract: Human Computer Interfaces (HCI) 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 HCI system to be used to recognize such gestures. Particularly, by training a classifier using vectors having both base and extended components, more accurate classification results may be subsequently obtained. The base vector may include a leaf-based assessment of the classification results from a forest for a given depth value candidate pixel. The extended vector may include additional information, such as the leaf-based assessment of the classification results for one or more pixels related to the candidate pixel. Various embodiments employ this improved structure with various optimization methods and structure to provide more efficient in-situ operation.

Abstract: Various of the disclosed embodiments present depth-based user interaction systems. By anticipating various installation constraints and factors into the interface's design, various of the disclosed embodiments facilitate benefits such as complementary depth fields of view and display orientation flexibility. Some embodiments include a frame housing for the depth sensors. Within the housing, depth sensors may be affixed to a mount, such that each sensor's field of view is at a disparate angle. These disparate angles may facilitate gesture recognitions that might otherwise be difficult or impossible to achieve. When mounted in connection with a modular unit, the housing may provide a versatile means for integrating multiple module units into a composite interface.

Abstract: Human Computer Interfaces (HCI) 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 HCI system to be used to recognize such gestures. Particularly, the HCI 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.

Abstract: Various of the disclosed embodiments present depth-based user interaction systems facilitating natural and immersive user interactions. Particularly, various embodiments integrate immersive visual presentations with natural and fluid gesture motions. This integration facilitates more rapid user adoption and more precise user interactions. Some embodiments may take advantage of the particular form factors disclosed herein to accommodate user interactions. For example, dual depth sensor arrangements in a housing atop the interface's display may facilitate depth fields of view accommodating more natural gesture recognition than may be otherwise possible. In some embodiments, these gestures may be organized into a framework for universal control of the interface by the user and for application-specific control of the interface by the user.

Abstract: Human Computer Interfaces (HCI) 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 HCI system to be used to recognize such gestures. Particularly, the HCI 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. This grouping may require that the relevant depth data be extracted from the depth frame. Such extraction may itself require that appropriate clipping planes be determined. Various of the disclosed embodiments better establish floor planes from which such clipping planes may be derived.