L.H, Matthies and T. Kanade, "The cycle of uncertainty and constraint in robot perception," Proc. Intern. Syrup. Robotics Research, 1987. 17, L.H Matthies and S.A. Shafer, "Error modeling in stereo navigation, " IEEE J. Robotics and Automation, pp. 239-248, 1987, Home/Search Document Not in Database Summary Related Articles Check |
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Model Acquisition Using Stochastic Projective Geometry - Robert T. Collins (1993) (9 citations) (Correct)
....of the world guides the interpretation of sensed data, which then is used to constrain the estimated location of the observer, which 38 in turn is then used to further refine the world model. This cycle of uncertainty and constraint lies at the core of many current robotic navigation systems [Matthies87a]. A notable example of the cycle of uncertainty and constraint is the work of Ayache and Faugeras [Ayache91] who describe a successful model based stereo navigation system. The strength of this system is its unified, homogeneous treatment of uncertainty in all stages of model construction, image ....
.... Although on the surface they appear different, solution techniques for implicit and explicit nonlinear models are essentially equivalent [Dolby76, Benichou89] 78 A notable feature of current geometric reasoning systems in vision and robotics is the cyclic nature of the processing involved [Ayache91, Matthies87a]. This cyclic style of processing lends itself well to sequential (recursive) parameter estimation techniques. When uncertainty in geometric features is represented using probability density functions, the natural choice for a sequential inference engine is Bayesian parameter estimation. The heart ....
Matthies, L. and Kanade, T. "The Cycle of Uncertainty and Constraint in Robot Perception," Proceedings of the International Symposium on Robotics Research, MIT Press, Cambridge, MA, 1987.
Shape from Rotation - Szeliski (1990) (10 citations) (Correct)
....the system calibration automatically. Because we also intend our system to eventually run in real time, finding efficient parallelizable algorithms will be important. 1. 1 Previous work Some of the early work in object motion estimation [Hallam, 1983; Broida and Chellappa, 1986; Rives et al. 1986; Matthies and Kanade, 1987] identified Kalman filtering as a viable framework for incremental estimation, because it incorporates representations of uncertainty and provides a mechanism for incrementally reducing uncertainty over time. Applied to depth from motion, this framework was at first restricted to estimating the ....
L. H. Matthies and T. Kanade. The cycle of uncertainty and constraint in robot perception. In International Symposium on Robotics Research, MIT Press, August 1987.
Kalman Filter-based Algorithms for Estimating Depth from.. - Matthies, Kanade (1989) (112 citations) Self-citation (Matthies Kanade) (Correct)
....require a depth estimation algorithm that operates in an on line, incremental fashion. To develop such an algorithm, we require a depth representation that includes not only the current depth estimate, but also an estimate of the uncertainty in the current depth estimate. Previous work [3, 5, 9, 10, 16, 17, 25] has identified Kalman filtering as a viable framework for this problem, because it incorporates representations of uncertainty and provides a mechanism for incrementally reducing uncertainty over time. To date, applications of this framework have largely been restricted to estimating the ....
.... or have used batch processing of the image sequence, for example via spatiotemporal filtering [11] In this paper we introduce a new, pixed based (iconic) approach to incremental depth estimation and compare it mathematically and experimentally to a feature based approach we developed previously [16]. The new approach represents depth and depth variance at every pixel and uses Kalman filtering to extrapolate and update the pixel based depth representation. The algorithm uses correlation to measure the optical flow and to estimate the variance in the flow, then uses the known camera motion to ....
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L.H, Matthies and T. Kanade, "The cycle of uncertainty and constraint in robot perception," Proc. Intern. Syrup. Robotics Research, 1987. 17, L.H Matthies and S.A. Shafer, "Error modeling in stereo navigation, " IEEE J. Robotics and Automation, pp. 239-248, 1987,
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