We present a multi-PC/camera system that can perform 3D reconstruction and ellipsoid fitting of moving humans in real time. The system consists of five cameras. Each camera is connected to a PC which locally extracts the silhouettes of the moving person in the image captured by the camera. The five silhouette images are then sent, via local network, to a host computer to perform 3D voxel-based reconstruction by an algorithm called SPOT. Ellipsoids are then used to fit the reconstructed data. By using a simple and user-friendly interface, the user can display and observe, in real time and from any view-point, the 3D models of the moving human body. With a rate of higher than 15 frames per second, the system is able to capture non-intrusively, a sequence of human motions.

Article Computer vision in 3D interactivity (panel) Share on Authors: Mark Holler Intel Corp. Intel Corp.View Profile , Ingrid Carlbom Bell Labs Bell LabsView Profile , Steven Feiner Columbia Univ., New York, NY Columbia Univ., New York, NYView Profile , George Robertson Microsoft Research Microsoft ResearchView Profile , Demetri Terzopoulos Intel Corp; and Univ. of Toronto, Toronto, Ont., Canada Intel Corp; and Univ. of Toronto, Toronto, Ont., CanadaView Profile Authors Info & Claims SIGGRAPH '98: ACM SIGGRAPH 98 Conference abstracts and applicationsJuly 1998 Pages 220–222https://doi.org/10.1145/280953.281598Published:21 July 1998 0citation136DownloadsMetricsTotal Citations0Total Downloads136Last 12 Months3Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

The use of floating-gate nonvolatile memory technology for analog storage of connection strengths, or weights, has previously been proposed and demonstrated. The authors report the analog storage and multiply characteristics of a new floating-gate synapse and further discuss the architecture of a neural network which uses this synapse cell. In the architecture described 8192 synapses are used to interconnect 64 neurons fully and to connect the 64 neurons to each of 64 inputs. Each synapse in the network multiplies a signed analog voltage by a stored weight and generates a differential current proportional to the product. Differential currents are summed on a pair of bit lines and transferred through a sigmoid function, appearing at the neuron output as an analog voltage. Input and output levels are compatible for ease in cascade-connecting these devices into multilayer networks. The width and height of weight-change pulses are calculated. The synapse cell size is 2009 mu m/sup 2/ using 1- mu m CMOS EEPROM technology.<>