A method of creating a scalable dynamic jointed skeleton (DJS) model for enhancing psychomotor leaning using augmented cognition methods realized by an artificial intelligence (AI) engine or image processor. The method involves extracting a DJS model from either live motion images of video files of an athlete, teacher, or expert to create a scalable reference model for using in training, whereby the AI engine extracts physical attributes of the subject including arm length, length, torso length as well as capturing successive movements of a motor skill such as swinging a gold club including position, stance, club position, swing velocity and acceleration, twisting, and more.

An apparatus for precisely and accurately measuring the loft and lie of a golf club, and a method of using such apparatus, is disclosed herein. The apparatus includes a clamp assembly powered by a pressure unit to immobilize the head of a golf club, a scoreline measurement unit to capture scoreline angle information, loft and lie measurement arrays that can be moved along horizontal y and x axes to capture loft and lie angle information of the golf club shaft, and a computer with an algorithm that uses information captured by the scoreline measurement unit and loft and lie measurement arrays to calculate final loft and lie angle values that are accurate within +/−0.125°.

The information processing device 4 includes an acquisition unit 41A and a structure matching unit 43A. The acquisition unit 41A is configured to acquire, from a captured image captured by a photographing unit 15A of a display device configured to display a virtual object superimposed on a view, multiple combinations of classification information of a structural feature point that is a feature point, in structure, of a target structure and position information of the structural feature point. The structure matching unit 43A is configured to generate coordinate transformation information by matching structure data “Ds” with the multiple combinations, the structure data Ds including information associated with a position and a class of each of structural feature points of the target structure, the coordinate transformation information relating to a coordinate transformation between a first coordinate system that is a coordinate system referred to by the display device and a second coordinate system that is a coordinate system adopted in the structure data Ds.

Methods and apparatus to generate photo-realistic three-dimensional models of a photographed environment are disclosed. An apparatus includes an object position calculator to determine a three-dimensional (3D) position of an object detected within a first image of an environment and within a second image of the environment. The apparatus further includes a 3D model generator to generate a 3D model of the environment based on the first image and the second image. The apparatus also includes a model integrity analyzer to detect a difference between the 3D position of the object and the 3D model. The 3D model generator automatically modifies the 3D model based on the difference in response to the difference satisfying a confidence threshold.

A computing system receives an image for processing. The image includes at least a player of interest. The computing system executes an image processing workflow for automatically processing the image in accordance with standards set by an organization associated with the computing system. The executing includes removing, via a background module, background pixels from the image. The executing further includes, after removing the background pixels, resizing, via a resizing module, the image to a set size defined by the organization. The executing further includes, after resizing the image, renaming, via a renaming module, the image to a name format defined by the organization. After executing the image processing workflow, the computing system saves the image to a database for retrieval. The removing, the resizing, and the renaming are performed without user intervention.

A plurality of tracking cameras is pointed towards a routine hovering area of an in-the-field sports participant who routinely hovers about that area. Spots within the hovering area are registered relative to a predetermined multi-dimensional coordinates reference frame (e.g., Xw, Yw, Zw, Tw) such that two-dimensional coordinates of 2D images captured by the tracking cameras can be converted to multi-dimensional coordinates of the reference frame. A body part recognizing unit recognizes 2D locations of a specific body part in the 2D captured images and a mapping unit maps them into the multi-dimensional coordinates of the reference frame. A multi-dimensional curve generator then generates a multi-dimensional motion curve describing motion of the body part based on the mapped coordinates (e.g., Xw, Yw, Zw, Tw). The generated multi-dimensional motion curve is used to discover cross correlations between play action motions of the in-the-field sports participant and real-world sports results.

A display method including the steps of: acquiring, at a first predetermined frequency, a first series of one-spatial-dimensional instant images of the finish line of a race; simultaneously acquiring a second series of two spatial-dimensional instant images of the same finish line (19) at a second predetermined frequency; forming a temporal image (10) of the photo finish type from the first series of instant images; choosing an instant (15, 22) of the temporal image (10); analysing a two-spatial-dimensional image of the second series, the image being correlated with the selected instant (15, 22) to determine at least one distance; and displaying the analysed two-dimensional image with the determined distance or distances. Also, a display system for implementing the method.

Embodiments relate to systems and methods for gaming monitoring. In particular, embodiments relate to systems and methods for gaming monitoring based on machine learning processes configured to analyse captured images to identify or detect game objects and game events to monitor games.

Embodiments of the systems and methods described herein provide a dynamic animation generation system that can apply a real-life video clip with a character in motion to a first neural network to receive rough motion data, such as pose information, for each of the frames of the video clip, and overlay the pose information on top of the video clip to generate a modified video clip. The system can identify a sliding window that includes a current frame, past frames, and future frames of the modified video clip, and apply the modified video clip to a second neural network to predict a next frame. The dynamic animation generation system can then move the sliding window to the next frame while including the predicted next frame, and apply the new sliding window to the second neural network to predict the following frame to the next frame.

A performance scanning system that operates to detect and measure surface parameters of a portion of an athlete and uses the surface parameters to determine the likelihood that the athlete's physical performance has been or will be impaired.