Presents a fast method for computing a bounding volume within which an observed object must lie from the observed motion of the occluding contour during a straight-line motion of the camera. The bounding volume is represented as a set of planar cross sections each consisting of multiple convex polygons within which the object lies. The algorithm's worst-case runtime performance is O(nmk) operations, where n is the number of viewpoints used, m is the number of polygons created during the execution of the algorithm, and k is a parameter dependent on the geometric complexity of the object being viewed. Experimental examples are demonstrated; bounding polygon computation from sampled contours in 20 images required less than one second on a 50 MHz i486 CPU.

This paper describes a vision based robotic system for grasping unfamiliar planar objects with a parallel jaw gripper. The input to the grasp synthesis system consists of projected edge data from a single camera video system calibrated to a planar working surface. A simple search procedure is used to find an acceptable grasp. Our grasp analysis metric is the required squeezing force, computed using an approximation of the rigid body equilibrium conditions. The resulting analysis conditions are formulated as a linear program and solved using the simplex method. A Zebra-ZERO robot arm with a parallel-jaw gripper is used to execute the chosen grasp. In a simple test of the system, it successfully grasped and lifted 10 of 14 test objects.>

The results of designing, training, and testing a neural network for the voiced/unvoiced (V/UV) speech classification problem are described. A feedforward multilayer backpropagation network was used with six input, ten internal, and two output nodes-for a binary decision. The six features are common and easily computed. Training was done with 72 frames from two speakers. Testing was done with 479 frames from four speakers and resulted in a total of two errors (0.4%). Thus, a small neural network performs well on the V/UV problem.>