Abstract: The present disclosure relates to a method and a device for recognizing hand gestures in real-time. A shape is given as an input in a form of a binary image. The shape contour is partitioned into radial and angular spaces by an Angular Radial Bin distribution including multiple concentric circles and angular space partitions in a way that multiple angular radial sections are created denoted Angular Radial Bins. The ARB distribution is angle tilted through its centre of mass multiple times and the same procedure is repeated in order to capture a shape descriptor from different angle perspectives. A shape descriptor is calculated for each of an angle tilted instance of the ARB distribution belonging to a sequence of angle tilted instances of the ARB distribution.

Abstract: The disclosure relates to a device and a method in the device for recognizing a 3D gesture. The device is connected to a sensor and has access to a database of gesture images including indexable features of normalized gesture images. The indexable features include a position and an orientation for each pixel of edge images of the normalized gesture images. The method includes capturing (110) an image of the 3D gesture via the sensor, normalizing (120) the captured image, deriving (130) indexable features from the normalized captured image, and comparing (140) the derived indexable features with the indexable features of the database using a similarity function. The method also includes determining (150) a gesture image in the database matching the 3D gesture based on the comparison.