Dynamic stereo with self-calibration

Tirumalai, Arun P.; Schunck, Brian G.; Jain, Ramesh

doi:10.1109/iccv.1990.139573

Cited by 8 publications

(3 citation statements)

References 12 publications

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“…Each experiment captures a pair of image sequences containing frames as shown in Figure 1. When using (12)- (15) to calculate the optimal estimation, two parameters, the measurement noise covarianceand the processing noise covariance must be prespecified. In this work, the measurement covariance noise was evaluated from the measurement data ∂ 2…”

Section: Methodsmentioning

confidence: 99%

“…These researches did not consider object grouping issues, and therefore are difficult to apply in real scenarios. Tirumalai et al [15] presented an approach for incremental refinement of disparity maps for a mobile robot. They utilized the motion vectors calculated from a least median of squares (LMS)based algorithm for recursive disparity prediction and refinement.…”

Section: Introductionmentioning

confidence: 99%

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Binocular Image Sequence Analysis: Integration of Stereo Disparity and Optic Flow for Improved Obstacle Detection and Tracking

Young

2008

EURASIP J. Adv. Signal Process.

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Binocular vision systems have been widely used for detecting obstacles in advanced driver assistant systems (ADASs). These systems normally utilise disparity information extracted from left and right image pairs, but ignore the optic flows able to be extracted from the two image sequences. In fact, integration of these two methods may generate some distinct benefits. This paper proposes two algorithms for integrating stereovision and motion analysis for improving object detection and tracking. The basic idea is to fully make use of information extracted from stereo image sequence pairs captured from a stereovision rig. The first algorithm is to impose the optic flows as extra constraints for stereo matching. The second algorithm is to use a Kalman filter as a mixer to combine the distance measurement and the motion displacement measurement for object tracking. The experimental results demonstrate that the proposed methods are effective for improving the quality of stereo matching and three-dimensional object tracking.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Binocular Image Sequence Analysis: Integration of Stereo Disparity and Optic Flow for Improved Obstacle Detection and Tracking

Young

2008

EURASIP J. Adv. Signal Process.

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“…Simultaneous use of both cues can impose constraints on where the match of a feature may be, thus restricting the search space, and simplifying the correspondence problem (e.g., Huang and Blostein [32]). It is also possible for the system to calibrate itself using redundant constraints (e.g., Tirumalai et al [26]). Finally, the use of stereo cues can eliminate the well-known scale factor ambiguity in motion vision, where only the direction of motion and the relative depth of the scene points can be recovered from perspective visual cues.…”

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confidence: 99%

Direct motion stereo for passive navigation

Negahdaripour

Hayashi²,

Aloimonos

1995

IEEE Trans. Robot. Automat.

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We address the problem of motion recovery for a head-eye system from stereo image sequences. Two types of motions, the translation of the vehicle and the panning motion of the head, are considered. We show how these motions and the depth map of the scene can be estimated directly from the measurements of image gradients and time derivatives in a sequence of stereo images. There is no need to estimate image motion, track a scene feature over time, or establish point correspondences in a stereo image pair. We present the results of various experiments with real scenes.

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Recovering a boundary-level structural description from dynamic stereo

Tirumalai

Schunck

Jain

1992

Image and Vision Computing

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We present a stereo algorithm to recursively compute a boundary-level structural description of a static scene, from a sequence of dynamic stereo images. This algorithm is based on connected line segments as the basic match primitive, which yields a description composed primarily of boundaries of objects in the scene. The algorithm is integrated into a dynamic stereo vision system to compute and incrementally refine such a structural description recursively, using belief measures.The approach is illustrated with a real dynamic stereo sequence.

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Dynamic stereo with self-calibration

Cited by 8 publications

References 12 publications

Binocular Image Sequence Analysis: Integration of Stereo Disparity and Optic Flow for Improved Obstacle Detection and Tracking

Binocular Image Sequence Analysis: Integration of Stereo Disparity and Optic Flow for Improved Obstacle Detection and Tracking

Direct motion stereo for passive navigation

Recovering a boundary-level structural description from dynamic stereo

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