An Appearance-Based Framework for 3D Hand Shape Classification and Camera Viewpoint Estimation

An appearance-based framework for 3D hand shape classification and simultaneous camera viewpoint estimation is presented. Given an input image of a segmented hand, the most similar matches from a large database of synthetic hand images are retrieved. The ground truth labels of those matches, containing hand shape and camera viewpoint information, are returned by the system as estimates for the input image. Database retrieval is done hierarchically, by first quickly rejecting the vast majority of all database views, and then ranking the remaining candidates in order of similarity to the input. Four different similarity measures are employed, based on edge location, edge orientation, finger location and geometric moments.National Science Foundation (IIS-9912573, EIA-9809340

Special Topics of Gesture Recognition Applied in Intelligent Home Environments

. This report shows how to realize a gesture recognition system for controlling appliances in home environments. It gives a brief overview on an existing system and clarifies details on ergonomic remote control of devices by gestures with the help of a vision system. The focus is on the motion detection, object normalization and identification, the modelling and the prediction of motion by the Kalman Filter. A main interest was to show through the example ARGUS, how the Kalman Filter should be modelled and initialized for a physical human motion. The initialization problem of the Kalman Filter of a vision based system for human motion tracking differs from initializing for physical systems, where manuals report measurement errors. Most aspects mentioned in this report were implemented in the ARGUS prototype. 1 Introduction Human computer interfaces based on visual input (video) have found growing interest during the past several years. One reason may be the continuously falling expens..