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Extraction of surface-related features in a recurrent model of V1-V2 interactions.

Weidenbacher U, Neumann H - PLoS ONE (2009)

Bottom Line: The approach is based on feedforward and feedback mechanisms found in visual cortical areas V1 and V2.Unlike previous proposals which treat localized junction configurations as 2D image features, we link them to mechanisms of apparent surface segregation.As a consequence, we demonstrate how junctions can change their perceptual representation depending on the scene context and the spatial configuration of boundary fragments.

View Article: PubMed Central - PubMed

Affiliation: Institute of Neural Information Processing, University of Ulm, Ulm, Germany. ulrich.weidenbacher@uni-ulm.de

ABSTRACT

Background: Humans can effortlessly segment surfaces and objects from two-dimensional (2D) images that are projections of the 3D world. The projection from 3D to 2D leads partially to occlusions of surfaces depending on their position in depth and on viewpoint. One way for the human visual system to infer monocular depth cues could be to extract and interpret occlusions. It has been suggested that the perception of contour junctions, in particular T-junctions, may be used as cue for occlusion of opaque surfaces. Furthermore, X-junctions could be used to signal occlusion of transparent surfaces.

Methodology/principal findings: In this contribution, we propose a neural model that suggests how surface-related cues for occlusion can be extracted from a 2D luminance image. The approach is based on feedforward and feedback mechanisms found in visual cortical areas V1 and V2. In a first step, contours are completed over time by generating groupings of like-oriented contrasts. Few iterations of feedforward and feedback processing lead to a stable representation of completed contours and at the same time to a suppression of image noise. In a second step, contour junctions are localized and read out from the distributed representation of boundary groupings. Moreover, surface-related junctions are made explicit such that they are evaluated to interact as to generate surface-segmentations in static images. In addition, we compare our extracted junction signals with a standard computer vision approach for junction detection to demonstrate that our approach outperforms simple feedforward computation-based approaches.

Conclusions/significance: A model is proposed that uses feedforward and feedback mechanisms to combine contextually relevant features in order to generate consistent boundary groupings of surfaces. Perceptually important junction configurations are robustly extracted from neural representations to signal cues for occlusion and transparency. Unlike previous proposals which treat localized junction configurations as 2D image features, we link them to mechanisms of apparent surface segregation. As a consequence, we demonstrate how junctions can change their perceptual representation depending on the scene context and the spatial configuration of boundary fragments.

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Related in: MedlinePlus

Dynamic stimulus where two occluding bars move in opposite direction.Tracking of L-junctions (green) leads to correct motion estimates of the two bars while tracking of T-junctions leads to erronous motion estimates. Thus, the visual system might use form information, e.g., surface-based occlusion cues to selectively discount local motion estimates for moving T-junctions.
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pone-0005909-g016: Dynamic stimulus where two occluding bars move in opposite direction.Tracking of L-junctions (green) leads to correct motion estimates of the two bars while tracking of T-junctions leads to erronous motion estimates. Thus, the visual system might use form information, e.g., surface-based occlusion cues to selectively discount local motion estimates for moving T-junctions.

Mentions: In a study by Pack et al. [75] it is suggested that end-stopped V1 neurons could provide local measures of two-dimensional feature correspondences in motion by responding preferentially to moving line endings. However, the results of Gue et al. [76] contrast with the suggestion that end-stop neurons can determine global motion directions. They propose that lateral and feedback connections play a critical role in V1 motion information integration. But still, it remains unclear whether cortical neurons represent object motion by selectively responding to two-dimensional features such as junctions and corners. On the other hand, motion of specific junction configurations, in particular T- and X-junctions generates erroneous motion trajectories. As shown in Figure 16, if edge motion from two bars moving in opposite horizontal directions is combined, the resulting intersection of constraints is in an incorrect vertical direction [77]. Thus, static form cues such as detected T-junctions could be selectively discounted in the process of motion interpretation [78].


Extraction of surface-related features in a recurrent model of V1-V2 interactions.

Weidenbacher U, Neumann H - PLoS ONE (2009)

Dynamic stimulus where two occluding bars move in opposite direction.Tracking of L-junctions (green) leads to correct motion estimates of the two bars while tracking of T-junctions leads to erronous motion estimates. Thus, the visual system might use form information, e.g., surface-based occlusion cues to selectively discount local motion estimates for moving T-junctions.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2691604&req=5

pone-0005909-g016: Dynamic stimulus where two occluding bars move in opposite direction.Tracking of L-junctions (green) leads to correct motion estimates of the two bars while tracking of T-junctions leads to erronous motion estimates. Thus, the visual system might use form information, e.g., surface-based occlusion cues to selectively discount local motion estimates for moving T-junctions.
Mentions: In a study by Pack et al. [75] it is suggested that end-stopped V1 neurons could provide local measures of two-dimensional feature correspondences in motion by responding preferentially to moving line endings. However, the results of Gue et al. [76] contrast with the suggestion that end-stop neurons can determine global motion directions. They propose that lateral and feedback connections play a critical role in V1 motion information integration. But still, it remains unclear whether cortical neurons represent object motion by selectively responding to two-dimensional features such as junctions and corners. On the other hand, motion of specific junction configurations, in particular T- and X-junctions generates erroneous motion trajectories. As shown in Figure 16, if edge motion from two bars moving in opposite horizontal directions is combined, the resulting intersection of constraints is in an incorrect vertical direction [77]. Thus, static form cues such as detected T-junctions could be selectively discounted in the process of motion interpretation [78].

Bottom Line: The approach is based on feedforward and feedback mechanisms found in visual cortical areas V1 and V2.Unlike previous proposals which treat localized junction configurations as 2D image features, we link them to mechanisms of apparent surface segregation.As a consequence, we demonstrate how junctions can change their perceptual representation depending on the scene context and the spatial configuration of boundary fragments.

View Article: PubMed Central - PubMed

Affiliation: Institute of Neural Information Processing, University of Ulm, Ulm, Germany. ulrich.weidenbacher@uni-ulm.de

ABSTRACT

Background: Humans can effortlessly segment surfaces and objects from two-dimensional (2D) images that are projections of the 3D world. The projection from 3D to 2D leads partially to occlusions of surfaces depending on their position in depth and on viewpoint. One way for the human visual system to infer monocular depth cues could be to extract and interpret occlusions. It has been suggested that the perception of contour junctions, in particular T-junctions, may be used as cue for occlusion of opaque surfaces. Furthermore, X-junctions could be used to signal occlusion of transparent surfaces.

Methodology/principal findings: In this contribution, we propose a neural model that suggests how surface-related cues for occlusion can be extracted from a 2D luminance image. The approach is based on feedforward and feedback mechanisms found in visual cortical areas V1 and V2. In a first step, contours are completed over time by generating groupings of like-oriented contrasts. Few iterations of feedforward and feedback processing lead to a stable representation of completed contours and at the same time to a suppression of image noise. In a second step, contour junctions are localized and read out from the distributed representation of boundary groupings. Moreover, surface-related junctions are made explicit such that they are evaluated to interact as to generate surface-segmentations in static images. In addition, we compare our extracted junction signals with a standard computer vision approach for junction detection to demonstrate that our approach outperforms simple feedforward computation-based approaches.

Conclusions/significance: A model is proposed that uses feedforward and feedback mechanisms to combine contextually relevant features in order to generate consistent boundary groupings of surfaces. Perceptually important junction configurations are robustly extracted from neural representations to signal cues for occlusion and transparency. Unlike previous proposals which treat localized junction configurations as 2D image features, we link them to mechanisms of apparent surface segregation. As a consequence, we demonstrate how junctions can change their perceptual representation depending on the scene context and the spatial configuration of boundary fragments.

Show MeSH
Related in: MedlinePlus