Abstract: A system and method are provided for processing game data to generate predictions for hypothetical or real future games. The method includes receiving input data comprising at least one of: i) historical data for one or more previous games, comprising box score information, or ii) play-by-play game data for one or more previous games; and transforming the input data into an abstraction space in which the abstraction space provides an abstraction comprising a numerical representation of player and/or team attributes. The method also includes mapping the numerical representation into one or more predictions of attributes of the games using at least one machine learning technique; and providing output data comprising the one or more predictions of attributes of the games.

Abstract: A system and method for registering images to each other or registering images to templates to generate geometric or nonlinear registration transformation mappings is disclosed. The method includes obtaining a first image from an imaging device, and obtaining either a reference image from the imaging device, or a template, comprising a partial or full representation of contents of the first image. The reference image or the template is in a second modality, and the first image is in a first modality. At least one mapping function is applied to one of the reference image or template, or both the first image and the reference image or template by mapping pixel data of the template or reference image to the first image to generate an estimation of a parametric registration transformation. Output data comprising one or more parameters of the parametric registration transformation is provided.

Abstract: Method or system for reinforcement learning that simultaneously learns a DR distribution ? while optimizing an agent policy ? to maximize performance over the learned DR distribution; method or system for training a learning agent using data synthesized by a simulator based on both a performance of the learning agent and a range of parameters present in the synthesized data.

Abstract: A system and method are provided for registering input images to reference objects to generate output parameters. The method includes obtaining an input image and a reference object, wherein the reference object comprises a reference image or a template corresponding to a model of content in the reference image. The method also includes performing a registration of the input image by estimating a geometric transform and/or a set of non-linear transform parameters, without using predefined image features. The method also includes measuring a misalignment error between the input images and the reference object and generating a set of output parameters. The output parameters can include a set of one or more camera parameters or a set of geometric and non-linear transformation parameters.

Abstract: A system and method for calibrating and re-calibrating an imaging device. The image device includes at least one varying intrinsic or extrinsic parameter value based on a view of 3D shapes and a 2D template therefor. The method and system are operable to receive a sequence of images from the imaging device, wherein a planar surface with known geometry is observable from the sequence of images; perform an initial estimation of a set of camera parameters for a given image frame; perform a parameter adjustment operation to minimize a dissimilarity between the template and the sequence of images; perform a parameter adjustment operation to maximize similarities in either the camera's coordinate system or the template's coordinate system; and update an adaptive world template to incorporate an additional stable point from the observed images to the template.

Abstract: Method or system for reinforcement learning that simultaneously learns a DR distribution ? while optimizing an agent policy ? to maximize performance over the learned DR distribution; method or system for training a learning agent using data synthesized by a simulator based on both a performance of the learning agent and a range of parameters present in the synthesized data.

Abstract: A system and method are provided for registering input images to reference objects to generate output parameters. The method includes obtaining an input image and a reference object, wherein the reference object comprises a reference image or a template corresponding to a model of content in the reference image. The method also includes performing a registration of the input image by estimating a geometric transform and/or a set of non-linear transform parameters, without using predefined image features. The method also includes measuring a misalignment error between the input images and the reference object and generating a set of output parameters. The output parameters can include a set of one or more camera parameters or a set of geometric and non-linear transformation parameters.

Abstract: A system and method are provided for calibrating and re-calibrating an imaging device. The image device comprises at least one varying intrinsic or extrinsic parameter value based on a view of 3D shapes and a 2D template therefor. The method and system are operable to receive a sequence of images from the imaging device, wherein a planar surface with known geometry is observable from the sequence of images; perform an initial estimation of a set of camera parameters for a given image frame; perform a parameter adjustment operation to minimize a dissimilarity between the template and the sequence of images; perform a parameter adjustment operation to maximize similarities in either the camera's coordinate system or the template's coordinate system; and update an adaptive world template to incorporate an additional stable point from the observed images to the template.