We introduce FOVEA: a Foveated Vergent Active stereo vision system. FOVEA actively directs a pair of vergent stereo cameras to fixate on surfaces in a scene, performing multiresolution surface depth recovery at each fixation point, and accumulating and integrating a multiresolution map of surface depth over multiple successive fixations. Several features of the system are novel: a foveated image sampling and processing strategy is shown to greatly simplify the problem of establishing; a probabilistic fixation strategy is developed that is driven by the scene structure; the system uses the fixation strategy to recover local depth maps at a high resolution at multiple fixation points, eventually mapping the entire scene; and finally, the local maps are integrated as they are acquired into a global depth map.

We introduce FOVEA: a foveated vergent active stereo vision system. FOVEA actively directs a pair of vergent stereo cameras to fixate on surfaces in a scene, performing multiresolution surface depth recovery at each fixation point, and accumulating and integrating a multiresolution map of surface depth over multiple successive fixations. Several features of the system are novel: an active foveated image sampling and processing strategy is shown to greatly simplify the problem of establishing correspondence; a probabilistic fixation strategy is developed that is driven by the scene structure; the system uses the fixation strategy to recover local depth maps at a high resolution at multiple fixation points, eventually mapping the entire scene; and finally, the local maps are integrated as they are acquired into a global depth map.

Active control of camera positioning is an important tool in the recovery of scene information. In this paper an important innovation is introduced which uses the local spatial frequency content of the acquired images to adaptively control stereo baseline, improving both the reliability and speed of the depth recovery process. This technique was designed to support the operation of an active vergent stereo imaging system by providing a coarse map of depth for camera fixation, providing an initial map of surface depth for accurate vergence.

This paper presents a one-stage stereo algorithm that yields 3D planar surface patches directly from matching image patch intensity information. The method allows an arbitrary rotation and translation between the cameras; it is not limited to parallel-axis, narrow-baseline, or vergent geometries. The key to the approach is to match image patches that have positions, shapes, sizes, orientations, and samplings consistent with a hypothesized surface patch and with each other. The match error then reflects only the mismatch of patch contents and not the mismatch of patch geometries or samplings. The algorithm is quantitatively evaluated against ground truth on real images with difficult viewing geometries, and demonstrates an average accuracy of about 1% in estimating surface depths and 10/spl deg/ in estimating surface normals.

A new method for actively recovering depth information using image defocus is demonstrated and shown to support active stereo vision depth recovery by providing monocular depth estimates to guide the positioning of cameras for stereo processing. This active depth-from-defocus approach employs a spatial frequency model for image defocus which incorporates the optical transfer function of the image acquisition system and a maximum likelihood estimator to determine the amount of defocus present in a sequence of two or more images taken from the same pose. This defocus estimate is translated into a measurement of depth and associated uncertainty that is used to control the positioning of a variable baseline stereo camera system. This cooperative arrangement significantly reduces the matching uncertainty of the stereo correspondence process and increases the depth resolution obtainable with an active stereo vision platform.