PureVoice™ VR is a speech recognition system characterized by noise robustness performance, small footprint, and limited computing requirement. The system was embedded into Qualcomm's Code Division Multiple Access (CDMA) Mobile Station Modem (MSM) chipsets. The PureVoice™ VR engine enabled many manufactures of CDMA cellular phones to add highly accurate voice-activated dialing features to their handsets and hands-free car kits without significant increase in hardware cost. Users of these cellular phones could make calls by simply speaking names or phone numbers. The current embedded system has the capacity to recognize 100 speaker-dependent (SD) nametags and user defined commands, or 30 speaker-independent (SI) digits and commands in realtime. It also works in a hybrid mode, in which an input utterance can be either SD namtags or SI commands. PureVoice™ VR has successfully supported the voice-activated dialing function for millions of CDMA handsets sold in the world market.

In this paper, a statistical error analysis is developed to show how the performance of a stereo ranging system relies on system parameters. Formulas for expected absolute depth error, expected relative depth error, and lower and upper bounds of depth error are derived. We show that for a given convergence angle, a simple, but important, parameter called generalized depth resolvability completely determines the performance of the system. These results are useful for the design of fixed ranging systems in constrained environments, or for the active selection of system parameters in "purposive" stereo systems.>

In this work, the correspondence problem in stereo vision is handled by matching two sets of dense feature vectors. Inspired by biological evidence, these feature vectors are generated by a correlation between a bank of Gabor sensors and the intensity image. The sensors consist of two-dimensional Gabor filters at various scales (spatial frequencies) and orientations, which bear close resemblance to the receptive field profiles of simple V1 cells in visual cortex. A hierarchical, stochastic relaxation method is then used to obtain the dense stereo disparities. Unlike traditional hierarchical methods for stereo, feature based hierarchical processing yields consistent disparities. To avoid false matchings due to static occlusion, a dual matching, based on the imaging geometry, is used.

The authors consider the problem of occlusion in computing stereo disparity from a pair of images. Usual approaches to stereo matching, e.g. area-based, feature-based, etc., can perform poorly in the neighborhood of occluding boundaries if no surface models are assumed. Qualitative improvements are possible based on conditions derived from the geometry of occlusion. A pair of correspondence processes, using information near an occlusion boundary to localize the boundary, can determine the sign of associated depth discontinuity unambiguously. The proposed method is able to identify the presence and extent of occlusion regions and assign disparities in a consistent way near the occlusion regions.>

A Bayesian approach is proposed for stereo matching to derive the maximum a posteriori estimation of depth. How a pyramid data structure can be combined with simulated annealing to speed up convergence in stereo matching is described. Using the invariant property of image intensity and modeling the disparity as a Markov random field (MRF), the pyramid structure is followed from high (coarse) level to low (fine) level to derive the maximum a posteriori estimates. Simulation results on both random dot diagrams and synthesized images show the promise of this multiresolution stereo approach.>

Maximum a posteriori (MAP) estimation of depth based on the Bayesian approach is described. The authors investigate simulated annealing in a hierarchical pyramid to accelerate the convergence. They also utilize an unobservable line process to recover the piecewise continuous depth map over the image plane. Simulation results are provided using random dot diagrams as well as synthesized images

The interpretation is presented of image flow (optical flow) and its relationship with object structure. Depth extraction is described from stereo image flow when the stereo cameras have parallel optical axes or nonparallel optical axes. Using epipolar geometry, the relationship between the optical flow fields on the image planes (stereo) is derived and applied for matching the images to extract the depth information. Three methods are considered, of which the first two assume parallel optical axes: (1) spatial approach which is the same as the traditional stereo method except that binocular difference flow is used as primitives for matching; (2) spatial-temporal approach which extends previous formulation to incorporate temporal variations of image flow; and (3) under nonparallel optical axes assumption from which the model of the stereo cameras is generalized so that they are arbitrarily separated in space. Then two measures are used to evaluate how well the epipolar constraint is satisfied.>