Cooperative integration of stereopsis and optic flow computation

Sudhir, G.; Banerjee, Subhashis; Biswas, Koushik; Bahl, Rajendar

doi:10.1364/josaa.12.002564

Cited by 12 publications

(6 citation statements)

References 21 publications

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“…The constraints given by (12) and (13) measure the dissimilarity between the left proximity map and the (warped) central and right proximity map, respectively. However, these proximity maps are discrete and possibly highly discontinuous, which makes them impractical to work with in an optimisation scheme.…”

Section: Numerical Implementationmentioning

confidence: 99%

“…Sudhir et al [13] model the visual processes as a sequence of coupled Markov random fields (MRFs). The MRF formulation allows us to define appropriate interactions between the stereo and motion processes and outlines a solution in terms of an appropriate energy function.…”

Section: Introductionmentioning

confidence: 99%

“…These local energy functions express constraints helping the stereo disambiguation by significantly reducing the search space. The integration algorithm as proposed by Sudhir et al [13] makes the visual processes tightly constrained and reduces the possibility of an error. Moreover, it is able to detect stereo‐occlusions and sharp object boundaries in both the disparity and the motion field.…”

Section: Introductionmentioning

confidence: 99%

“…The advantage of this concurrent optimisation methodology is that it provides a symmetric processing cue. This makes it easer to handle the uncertainties induced by the unknown displacements between the different cameras, in comparison with other approaches [13] who consider only one reference image and warp all other images to this reference image for matching and depth estimation. Other researchers have noted this too and have used even more depth or proximity maps.…”

Section: Introductionmentioning

confidence: 99%

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Augmented Lagrangian‐based approach for dense three‐dimensional structure and motion estimation from binocular image sequences

Cubber

Sahli

2014

IET Computer Vision

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In this study, the authors propose a framework for stereo-motion integration for dense depth estimation. They formulate the stereo-motion depth reconstruction problem into a constrained minimisation one. A sequential unconstrained minimisation technique, namely, the augmented Lagrange multiplier (ALM) method has been implemented to address the resulting constrained optimisation problem. ALM has been chosen because of its relative insensitivity to whether the initial design points for a pseudo-objective function are feasible or not. The development of the method and results from solving the stereo-motion integration problem are presented. Although the authors work is not the only one adopting the ALMs framework in the computer vision context, to thier knowledge the presented algorithm is the first to use this mathematical framework in a context of stereo-motion integration. This study describes how the stereo-motion integration problem was cast in a mathematical context and solved using the presented ALM method. Results on benchmark and real visual input data show the validity of the approach.

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Section: Numerical Implementationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Augmented Lagrangian‐based approach for dense three‐dimensional structure and motion estimation from binocular image sequences

Cubber

Sahli

2014

IET Computer Vision

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“…Stereo constraints under a multiview camera setup were investigated and utilized in model-based algorithms and extended gradient-based algorithms. Sudhir et al [13] and Clifford and Nasrabadi [14] used Markov random fields to combine the stereo and motion. In Sudhir's method, the discontinuities are used to predict and enforce the preservation of stereo correspondences through the motion estimates.…”

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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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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Motion — Stereo Integration for Depth Estimation

Strecha

Gool

2002

Computer Vision — ECCV 2002

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Cooperative integration of stereopsis and optic flow computation

Cited by 12 publications

References 21 publications

Augmented Lagrangian‐based approach for dense three‐dimensional structure and motion estimation from binocular image sequences

Augmented Lagrangian‐based approach for dense three‐dimensional structure and motion estimation from binocular image sequences

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

Motion — Stereo Integration for Depth Estimation

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