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Object segmentation from motion discontinuities and temporal occlusions--a biologically inspired model.

Beck C, Ognibeni T, Neumann H - PLoS ONE (2008)

Bottom Line: We propose a model derived from mechanisms found in visual areas V1, MT, and MSTl of human and primate cortex that achieves robust detection along motion boundaries.In addition, we discuss how this model is related to neurophysiological findings.The model was successfully tested both with artificial and real sequences including self and object motion.

View Article: PubMed Central - PubMed

Affiliation: Institute for Neural Information Processing, University of Ulm, Ulm, Germany. cornelia.beck@uni-ulm.de

ABSTRACT

Background: Optic flow is an important cue for object detection. Humans are able to perceive objects in a scene using only kinetic boundaries, and can perform the task even when other shape cues are not provided. These kinetic boundaries are characterized by the presence of motion discontinuities in a local neighbourhood. In addition, temporal occlusions appear along the boundaries as the object in front covers the background and the objects that are spatially behind it.

Methodology/principal findings: From a technical point of view, the detection of motion boundaries for segmentation based on optic flow is a difficult task. This is due to the problem that flow detected along such boundaries is generally not reliable. We propose a model derived from mechanisms found in visual areas V1, MT, and MSTl of human and primate cortex that achieves robust detection along motion boundaries. It includes two separate mechanisms for both the detection of motion discontinuities and of occlusion regions based on how neurons respond to spatial and temporal contrast, respectively. The mechanisms are embedded in a biologically inspired architecture that integrates information of different model components of the visual processing due to feedback connections. In particular, mutual interactions between the detection of motion discontinuities and temporal occlusions allow a considerable improvement of the kinetic boundary detection.

Conclusions/significance: A new model is proposed that uses optic flow cues to detect motion discontinuities and object occlusion. We suggest that by combining these results for motion discontinuities and object occlusion, object segmentation within the model can be improved. This idea could also be applied in other models for object segmentation. In addition, we discuss how this model is related to neurophysiological findings. The model was successfully tested both with artificial and real sequences including self and object motion.

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Related in: MedlinePlus

Optic flow estimation at occlusions.Occlusions lead to problems for motion estimation algorithms                                    based on the correlation between only two frames: Parts of the                                    image are only visible in one of the frames, thus no                                    corresponding image positions can be found at these locations.                                    This problem can be solved using only one additional temporally                                    forward-looking step (future step).
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pone-0003807-g003: Optic flow estimation at occlusions.Occlusions lead to problems for motion estimation algorithms based on the correlation between only two frames: Parts of the image are only visible in one of the frames, thus no corresponding image positions can be found at these locations. This problem can be solved using only one additional temporally forward-looking step (future step).

Mentions: In our model, MSTlModel is primarily concerned with object motion, i.e. the detection of spatial motion contrast through center-surround processing of motion fields with different directions (Fig. 3). These neurons receive input from MTModel. They are highly activated if the movement presented in the central part is different from the movement in the surround and are thus tuned to motion discontinuities, i.e. positions where two or more movements meet.


Object segmentation from motion discontinuities and temporal occlusions--a biologically inspired model.

Beck C, Ognibeni T, Neumann H - PLoS ONE (2008)

Optic flow estimation at occlusions.Occlusions lead to problems for motion estimation algorithms                                    based on the correlation between only two frames: Parts of the                                    image are only visible in one of the frames, thus no                                    corresponding image positions can be found at these locations.                                    This problem can be solved using only one additional temporally                                    forward-looking step (future step).
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2586919&req=5

pone-0003807-g003: Optic flow estimation at occlusions.Occlusions lead to problems for motion estimation algorithms based on the correlation between only two frames: Parts of the image are only visible in one of the frames, thus no corresponding image positions can be found at these locations. This problem can be solved using only one additional temporally forward-looking step (future step).
Mentions: In our model, MSTlModel is primarily concerned with object motion, i.e. the detection of spatial motion contrast through center-surround processing of motion fields with different directions (Fig. 3). These neurons receive input from MTModel. They are highly activated if the movement presented in the central part is different from the movement in the surround and are thus tuned to motion discontinuities, i.e. positions where two or more movements meet.

Bottom Line: We propose a model derived from mechanisms found in visual areas V1, MT, and MSTl of human and primate cortex that achieves robust detection along motion boundaries.In addition, we discuss how this model is related to neurophysiological findings.The model was successfully tested both with artificial and real sequences including self and object motion.

View Article: PubMed Central - PubMed

Affiliation: Institute for Neural Information Processing, University of Ulm, Ulm, Germany. cornelia.beck@uni-ulm.de

ABSTRACT

Background: Optic flow is an important cue for object detection. Humans are able to perceive objects in a scene using only kinetic boundaries, and can perform the task even when other shape cues are not provided. These kinetic boundaries are characterized by the presence of motion discontinuities in a local neighbourhood. In addition, temporal occlusions appear along the boundaries as the object in front covers the background and the objects that are spatially behind it.

Methodology/principal findings: From a technical point of view, the detection of motion boundaries for segmentation based on optic flow is a difficult task. This is due to the problem that flow detected along such boundaries is generally not reliable. We propose a model derived from mechanisms found in visual areas V1, MT, and MSTl of human and primate cortex that achieves robust detection along motion boundaries. It includes two separate mechanisms for both the detection of motion discontinuities and of occlusion regions based on how neurons respond to spatial and temporal contrast, respectively. The mechanisms are embedded in a biologically inspired architecture that integrates information of different model components of the visual processing due to feedback connections. In particular, mutual interactions between the detection of motion discontinuities and temporal occlusions allow a considerable improvement of the kinetic boundary detection.

Conclusions/significance: A new model is proposed that uses optic flow cues to detect motion discontinuities and object occlusion. We suggest that by combining these results for motion discontinuities and object occlusion, object segmentation within the model can be improved. This idea could also be applied in other models for object segmentation. In addition, we discuss how this model is related to neurophysiological findings. The model was successfully tested both with artificial and real sequences including self and object motion.

Show MeSH
Related in: MedlinePlus