Automated Camera Calibration and 3D Egomotion Estimation for Augmented Reality Applications
CAIP '97 Proceedings of the 7th International Conference on Computer Analysis of Images and Patterns
Evaluation of Visual Attention Models for Robots
ICVS '06 Proceedings of the Fourth IEEE International Conference on Computer Vision Systems
Joint optical flow estimation, segmentation, and 3D interpretation with level sets
Computer Vision and Image Understanding
Incorporating non-motion cues into 3D motion segmentation
Computer Vision and Image Understanding
An attentional approach for perceptual grouping of spatially distributed patterns
Proceedings of the 29th DAGM conference on Pattern recognition
Ego-motion computing for vehicle velocity estimation
EUROCAST'07 Proceedings of the 11th international conference on Computer aided systems theory
Robust 3D segmentation of multiple moving objects under weak perspective
WDV'05/WDV'06/ICCV'05/ECCV'06 Proceedings of the 2005/2006 international conference on Dynamical vision
Global motion estimation from coarsely sampled motion vector field and the applications
IEEE Transactions on Circuits and Systems for Video Technology
Validating vision and robotic algorithms for dynamic real world environments
SIMPAR'10 Proceedings of the Second international conference on Simulation, modeling, and programming for autonomous robots
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In this work, we propose a saliency-based approach for estimating and segmenting 3D motions of multiple moving objects represented by 2D motion vector fields (MVF). In order to overcome typical problems in autonomous mobile robotic vision such as noise, occlusions, and inhibition of the ego-motion defects of a moving camera head, a classification module has been implemented to define the global motion of the mounted camera. The proposed method achieves valuable reduction in computational time by applying a guided control module which limits the segmentation output to a flexible predefined threshold value. The computational enhancement is very important since the output of the motion segmentation approach is implemented in an active vision system.