Abstract: Systems and methods for combining three-dimensional tracking of a user's movements with a three-dimensional user interface display is described. A tracking module processes depth data of a user performing movements, for example, movements of the user's hand and fingers. The tracked movements are used to animate a representation of the hand and fingers, and the animated representation is displayed to the user using a three-dimensional display. Also displayed are one or more virtual objects with which the user can interact. In some embodiments, the interaction of the user with the virtual objects controls an electronic device.

Abstract: Technologies for learning body part geometry are described. In some embodiments the technologies include systems, methods and computer readable medium for extracting biometric information from a body part of a user, such as the user's hand. In some instances the extraction is performed with the aid of a calibrated computer model of the body part in question. Body part information may be saved in a data structure for use as a biometric template. The Biometric authentication processes utilizing the technologies are also described.

Abstract: A method for modeling and tracking a subject using image depth data includes locating the subject's trunk in the image depth data and creating a three-dimensional (3D) model of the subject's trunk. Further, the method includes locating the subject's head in the image depth data and creating a 3D model of the subject's head. The 3D models of the subject's head and trunk can be exploited by removing pixels from the image depth data corresponding to the trunk and the head of the subject, and the remaining image depth data can then be used to locate and track an extremity of the subject.

Abstract: The present disclosure describes a target tracker that evaluates frames of data of one or more targets, such as a body part, body, and/or object, acquired by a depth camera. Positions of the joints of the target(s) in the previous frame and the data from a current frame are used to determine the positions of the joints of the target(s) in the current frame. To perform this task, the tracker proposes several hypotheses and then evaluates the data to validate the respective hypotheses. The hypothesis that best fits the data generated by the depth camera is selected, and the joints of the target(s) are mapped accordingly.

Abstract: Systems and methods for projecting graphics onto an available surface, tracking a user's interactions with the projected graphics, and providing feedback to the user regarding the tracked interactions are described. In some embodiments, the feedback is provided via updated projected graphics onto the surface. In some embodiments, the feedback is provided via an electronic screen.

Abstract: A system and method for creating a gesture and generating a classifier that can identify the gesture for use with an application is described. The designer constructs a training set of data containing positive and negative examples of the gesture. Machine learning algorithms are used to compute the optimal classification of the training data into positive and negative instances of the gesture. The machine learning algorithms generate a classifier which, given input data, makes a decision on whether the gesture was performed in the input data or not.

Abstract: A system and method for using multiple perceptual sensing technologies to capture information about a user's actions and for synergistically processing the information is described. Non-limiting examples of perceptual sensing technologies include gesture recognition using depth sensors, two-dimensional cameras, gaze detection, and/or speech recognition. The information captured about a user's gestures using one type of sensing technology is often not able to be captured with another type of technology. Thus, using multiple perceptual sensing technologies allows more information to be captured about the user's gestures. Further, by synergistically leveraging the information acquired using multiple perceptual sensing technologies, a more natural user interface can be created for a user to interact with an electronic device.

Abstract: A method of image acquisition and data pre-processing includes obtaining from a sensor an image of a subject making a movement. The sensor may be a depth camera. The method also includes selecting a plurality of features of interest from the image, sampling a plurality of depth values corresponding to the plurality of features of interest, projecting the plurality of features of interest onto a model utilizing the plurality of depth values, and constraining the projecting of the plurality of features of interest onto the model utilizing a constraint system. The constraint system may comprise an inverse kinematics solver.

Abstract: The present disclosure describes a target tracker that evaluates frames of data of one or more targets, such as a body part, body, and/or object, acquired by a depth camera. Positions of the joints of the target(s) in the previous frame and the data from a current frame are used to determine the positions of the joints of the target(s) in the current frame. To perform this task, the tracker proposes several hypotheses and then evaluates the data to validate the respective hypotheses. The hypothesis that best fits the data generated by the depth camera is selected, and the joints of the target(s) are mapped accordingly.

Abstract: A method for modeling and tracking a subject using image depth data includes locating the subject's trunk in the image depth data and creating a three-dimensional (3D) model of the subject's trunk. Further, the method includes locating the subject's head in the image depth data and creating a 3D model of the subject's head. The 3D models of the subject's head and trunk can be exploited by removing pixels from the image depth data corresponding to the trunk and the head of the subject, and the remaining image depth data can then be used to locate and track an extremity of the subject.

Abstract: A system and method for close range object tracking are described. Close range depth images of a user's hands and fingers are acquired using a depth sensor. Movements of the user's hands and fingers are identified and tracked. This information is used to permit the user to interact with a virtual object, such as an icon or other object displayed on a screen, or the screen itself.

Abstract: A system and method for combining depth images taken from multiple depth cameras into a composite image are described. The volume of space captured in the composite image is configurable in size and shape depending upon the number of depth cameras used and the shape of the cameras' imaging sensors. Tracking of movements of a person or object can be performed on the composite image. The tracked movements can subsequently be used by an interactive application.

Abstract: A system and method for adjusting the parameters of a camera based upon the elements in an imaged scene are described. The frame rate at which the camera captures images can be adjusted based upon whether the object of interest appears in the camera's field of view to improve the camera's power consumption. The exposure time can be set based on the distance of an object form the camera to improve the quality of the acquired camera data.

Abstract: A method for modeling and tracking a subject using image depth data includes locating the subject's trunk in the image depth data and creating a three-dimensional (3D) model of the subject's trunk. Further, the method includes locating the subject's head in the image depth data and creating a 3D model of the subject's head. The 3D models of the subject's head and trunk can be exploited by removing pixels from the image depth data corresponding to the trunk and the head of the subject, and the remaining image depth data can then be used to locate and track an extremity of the subject.

Abstract: A system and method are provided for object tracking using depth data, amplitude data and/or intensity data. In some embodiments, time of flight (ToF) sensor data may be used to enable enhanced image processing, the method including acquiring depth data for an object imaged by a ToF sensor; acquiring amplitude data and/or intensity data for an object imaged by a ToF sensor; applying an image processing algorithm to process the depth data and the amplitude data and/or the intensity data; and tracking object movement based on an analysis of the depth data and the amplitude data and/or the intensity data.

Abstract: Systems and methods for combining three-dimensional tracking of a user's movements with a three-dimensional user interface display is described. A tracking module processes depth data of a user performing movements, for example, movements of the user's hand and fingers. The tracked movements are used to animate a representation of the hand and fingers, and the animated representation is displayed to the user using a three-dimensional display. Also displayed are one or more virtual objects with which the user can interact. In some embodiments, the interaction of the user with the virtual objects controls an electronic device.

Abstract: A system and method for creating a gesture and generating a classifier that can identify the gesture for use with an application is described. The designer constructs a training set of data containing positive and negative examples of the gesture. Machine learning algorithms are used to compute the optimal classification of the training data into positive and negative instances of the gesture. The machine learning algorithms generate a classifier which, given input data, makes a decision on whether the gesture was performed in the input data or not.

Abstract: A system and method for close range object tracking are described. Close range depth images of a user's hands and fingers or other objects are acquired using a depth sensor. Using depth image data obtained from the depth sensor, movements of the user's hands and fingers or other objects are identified and tracked, thus permitting the user to interact with an object displayed on a screen, by using the positions and movements of his hands and fingers or other objects.

Abstract: A system and method of providing an interactive training program by monitoring a user's actions is disclosed. A user is prompted through the interactive training program to perform certain movements, the user's movements are monitored relative to the prompted movements, and feedback is provided to the user through the program.

Abstract: There is provided a system for tracking a region of interest in a video includes an identifier for identifying the region of interest and determining a location of the region of interest in a first frame of a video sequence, and a tracker for locating the region of interest in at least a second frame, based on a location of the region of interest in the first frame. The system also includes a recovery manager for determining whether the tracker has correctly located the region of interest. There is also provided a method for tracking a region of interest in a video.