Purdue Research Foundation

Ayan Sinha

Karthik Ramani

Chiho Choi

Joon Hee Choi

A method for hand pose identification in an automated system includes providing map data of a hand of a user to a first neural network trained to classify features corresponding to a joint angle of a wrist in the hand to generate a first plurality of activation features and performing a first search in a predetermined plurality of activation features stored in a database in the memory to identify a first plurality of hand pose parameters for the wrist associated with predetermined activation features in the database that are nearest neighbors to the first plurality of activation features. The method further includes generating a hand pose model corresponding to the hand of the user based on the first plurality of hand pose parameters and performing an operation in the automated system in response to input from the user based on the hand pose model.

Method and system for hand pose detection

A method using a two-dimensional (2D) image representation of three-dimensional (3D) geometric objects in a machine learning framework has been developed. The method includes generating a single 2D geometry image corresponding to a 3D object model, and providing the single geometry image as input to a shape analysis task to enable shape analysis of the 3D object model based only on information encoded in the single 2D geometry image in the machine learning framework.

Method and apparatus for generating two-dimensional image data describing a three-dimensional image

A method for hand pose identification in an automated system includes providing depth map data of a hand of a user to a first neural network trained to classify features corresponding to a joint angle of a wrist in the hand to generate a first plurality of activation features and performing a first search in a predetermined plurality of activation features stored in a database in the memory to identify a first plurality of hand pose parameters for the wrist associated with predetermined activation features in the database that are nearest neighbors to the first plurality of activation features. The method further includes generating a hand pose model corresponding to the hand of the user based on the first plurality of hand pose parameters and performing an operation in the automated system in response to input from the user based on the hand pose model.

System for hand pose detection

A method for training a hierarchy of trained neural networks for hand pose detection includes training a first neural network to generate a first plurality of activation features that classify an input depth map data corresponding to a hand based on a wrist angle of the hand, the training using a plurality of depth maps of a hand with predetermined wrist angles as inputs to the first neural network during the training, and storing the first neural network in a memory after the training for use in classifying an additional depth map corresponding to a hand based on an angle of a wrist of the hand in the additional depth map.

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West Lafayette, IN, United States of America

Ayan Sinha