Fast Saliency-Based Motion Segmentation Algorithm for an Active Vision System

Shafik, M. Salah E.-N.; Mertsching, Bärbel

doi:10.1007/978-3-540-88458-3_52

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…Fig. 8 shows the progression of the root mean square error E k (c(p m )) over the total iteration steps k of the previously represented synthetic MVF for the proposed algorithm compared to the segmentation approach in [20] .…”

Section: Resultsmentioning

confidence: 99%

Real time stereo-based biologically inspired 3D motion classifier cells

Shafik

Mertsching

2011

2011 6th IEEE Conference on Industrial Electronics and Applications

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In this paper, we present a real time biologically motivated 3D motion classifier cells integrating the depth information generated from a stereo input implemented in an active vision system. The proposed approach is accurately able to detect and estimate multiple interfered 3D complex motions under the absence of predefined spatial coherence. Moreover, the system has ability to examine the response of input 3D motion vector fields to a certain 3D motion patterns (3D motion classifier cells) such as motion in the Z direction representing movements towards the system, which is very important to overcome typical problem in autonomous mobile robotic vision such as collision detection and inhibition of the ego-motion defects of a moving camera head. The output of the algorithm is part in a multi-object segmentation approach implemented in an active vision system.

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Section: Resultsmentioning

confidence: 99%

Real time stereo-based biologically inspired 3D motion classifier cells

Shafik

Mertsching

2011

2011 6th IEEE Conference on Industrial Electronics and Applications

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“…7 shows the progression of the root mean square error E k (c(p m )) over the total iteration Figure 6: Detection of 3D motion patterns, yellow MVs represent the translation in the z direction which represents a possible collision with the robot. Figure 7: Progression of the root mean square error E k (c(p m )) over the total iteration steps k of the previously represented synthetic MVF for the proposed depthintegrated algorithm compared to the segmentation approach in (Shafik and Mertsching, 2008). steps k of the previously represented synthetic MVF for the proposed algorithm compared to the segmentation approach in (Shafik and Mertsching, 2008).…”

Section: Resultsmentioning

confidence: 99%

“…In this paper, a new algorithm is proposed to enhance the computational speed of the motion segmentation approach presented in (Shafik and Mertsching, 2008) by integrating the depth information in the 3D motion parameters estimation process. Hence, the search space has been reduced to be five dimensions which represent the rotation around the x, y, and z axes and translation in the direction of the x− and y−axis.…”

Section: Introductionmentioning

confidence: 99%

Fast Depth-Integrated 3d Motion Estimation and Visualization for an Active Vision System

Shafik

Mertsching

2011

Proceedings of the International Conference on Computer Vision Theory and Applications

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In this paper, we present a fast 3D motion parameter estimation approach integrating the depth information acquired by a stereo camera head mounted on a mobile robot. Afterwards, the resulting 3D motion parameters are used to generate and accurately position motion vectors of the generated depth sequence in the 3D space using the geometrical information of the stereo camera head. The proposed approach has successfully detected and estimated predefined motion patterns such as motion in the Z direction and motion vectors pointing to the robot which is very important to overcome typical problems in autonomous mobile robotic vision such as collision detection and inhibition of the ego-motion defects of a moving camera head. The output of the algorithm is part of a multi-object segmentation approach implemented in an active vision system.

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Validating Vision and Robotic Algorithms for Dynamic Real World Environments

Kotthäuser

Mertsching

2010

Simulation, Modeling, and Programming for Autonomous Robots

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Fast Saliency-Based Motion Segmentation Algorithm for an Active Vision System

Cited by 3 publications

References 12 publications

Real time stereo-based biologically inspired 3D motion classifier cells

Real time stereo-based biologically inspired 3D motion classifier cells

Fast Depth-Integrated 3d Motion Estimation and Visualization for an Active Vision System

Validating Vision and Robotic Algorithms for Dynamic Real World Environments

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