No abstract

Hand gestures are examples of fast and complex motions.Computers fail to track these in fast video, but sleight of hand fools humans as well: what happens too quickly we just cannot see. We show a 3D tracker for these types of motions that relies on the recognition of familiar configurations in 2D images (classification), and fills the gaps in-between (interpolation). We illustrate this idea with experiments on hand motions similar to finger spelling. The penalty for a recognition failure is often small: if two configurations are confused, they are often similar to each other, and the illusion works well enough, for instance, to drive a graphics animation of the moving hand. We contribute advances in both feature design and classifier training: our image features are invariant to image scale, translation, and rotation, and we propose a classification method that combines VQPCA with discrimination trees.

This paper presents a new framework for the computation of shape and motion from a sequence of images taken under perspective projection. The framework is based on two abstractions, the picture and trail loci, that represent respectively the set of all pictures of the same scene and the set of all trails that a point in the world can leave on the image for a given camera trajectory. These abstractions lead t o a remarkably clean relation between perspective and orthography. A shape and motion reconstruction method is developed for the case of a two-dimensional world, but all concepts also hold i n three dimensions. Experiments show that the method is rather immune to noise but critically dependent on camera calibration.