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Hierarchical representation of shapes in visual cortex-from localized features to figural shape segregation.

Tschechne S, Neumann H - Front Comput Neurosci (2014)

Bottom Line: Based on this, global configurational as well as local information is made available to distinguish changes in the object's contour.Our model is probed with a selection of stimuli to illustrate processing results at different processing stages.We especially highlight how modulatory feedback connections contribute to the processing of visual input at various stages in the processing hierarchy.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Engineering and Computer Science (with Psychology and Education), Institute of Neural Information Processing, Ulm University Ulm, Germany.

ABSTRACT
Visual structures in the environment are segmented into image regions and those combined to a representation of surfaces and prototypical objects. Such a perceptual organization is performed by complex neural mechanisms in the visual cortex of primates. Multiple mutually connected areas in the ventral cortical pathway receive visual input and extract local form features that are subsequently grouped into increasingly complex, more meaningful image elements. Such a distributed network of processing must be capable to make accessible highly articulated changes in shape boundary as well as very subtle curvature changes that contribute to the perception of an object. We propose a recurrent computational network architecture that utilizes hierarchical distributed representations of shape features to encode surface and object boundary over different scales of resolution. Our model makes use of neural mechanisms that model the processing capabilities of early and intermediate stages in visual cortex, namely areas V1-V4 and IT. We suggest that multiple specialized component representations interact by feedforward hierarchical processing that is combined with feedback signals driven by representations generated at higher stages. Based on this, global configurational as well as local information is made available to distinguish changes in the object's contour. Once the outline of a shape has been established, contextual contour configurations are used to assign border ownership directions and thus achieve segregation of figure and ground. The model, thus, proposes how separate mechanisms contribute to distributed hierarchical cortical shape representation and combine with processes of figure-ground segregation. Our model is probed with a selection of stimuli to illustrate processing results at different processing stages. We especially highlight how modulatory feedback connections contribute to the processing of visual input at various stages in the processing hierarchy.

No MeSH data available.


Related in: MedlinePlus

Corner representation in model V2/V3. For each group of three pictures: Initially, responses of model V1 did not yet benefit from contextual feedback of model V2 neurons. Corner representation is thus distorted by noise (second row, middle). After a few iterations, when V1 responses have been modulated V2 feedback, the corner representation is much clearer.
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Figure 6: Corner representation in model V2/V3. For each group of three pictures: Initially, responses of model V1 did not yet benefit from contextual feedback of model V2 neurons. Corner representation is thus distorted by noise (second row, middle). After a few iterations, when V1 responses have been modulated V2 feedback, the corner representation is much clearer.

Mentions: Also in Figure 5, the representation of illusory contours at V2 stage is depicted. This is illustrated using an input depicting a Kanisza square (last row). A complete square is highly salient for human observers despite the fact that only a series of circles with cut-out corners are depicted. This is reflected in the grouping responses of V2 neurons, they also show activity in the gap between the real contour fragments. Figure 5 shows V2 responses for the same parameter set and for a parameter set with changed receptive field sizes, to illustrate the effect even stronger (framed part). Figure 6 shows a result of the corner representation in the model.


Hierarchical representation of shapes in visual cortex-from localized features to figural shape segregation.

Tschechne S, Neumann H - Front Comput Neurosci (2014)

Corner representation in model V2/V3. For each group of three pictures: Initially, responses of model V1 did not yet benefit from contextual feedback of model V2 neurons. Corner representation is thus distorted by noise (second row, middle). After a few iterations, when V1 responses have been modulated V2 feedback, the corner representation is much clearer.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4127482&req=5

Figure 6: Corner representation in model V2/V3. For each group of three pictures: Initially, responses of model V1 did not yet benefit from contextual feedback of model V2 neurons. Corner representation is thus distorted by noise (second row, middle). After a few iterations, when V1 responses have been modulated V2 feedback, the corner representation is much clearer.
Mentions: Also in Figure 5, the representation of illusory contours at V2 stage is depicted. This is illustrated using an input depicting a Kanisza square (last row). A complete square is highly salient for human observers despite the fact that only a series of circles with cut-out corners are depicted. This is reflected in the grouping responses of V2 neurons, they also show activity in the gap between the real contour fragments. Figure 5 shows V2 responses for the same parameter set and for a parameter set with changed receptive field sizes, to illustrate the effect even stronger (framed part). Figure 6 shows a result of the corner representation in the model.

Bottom Line: Based on this, global configurational as well as local information is made available to distinguish changes in the object's contour.Our model is probed with a selection of stimuli to illustrate processing results at different processing stages.We especially highlight how modulatory feedback connections contribute to the processing of visual input at various stages in the processing hierarchy.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Engineering and Computer Science (with Psychology and Education), Institute of Neural Information Processing, Ulm University Ulm, Germany.

ABSTRACT
Visual structures in the environment are segmented into image regions and those combined to a representation of surfaces and prototypical objects. Such a perceptual organization is performed by complex neural mechanisms in the visual cortex of primates. Multiple mutually connected areas in the ventral cortical pathway receive visual input and extract local form features that are subsequently grouped into increasingly complex, more meaningful image elements. Such a distributed network of processing must be capable to make accessible highly articulated changes in shape boundary as well as very subtle curvature changes that contribute to the perception of an object. We propose a recurrent computational network architecture that utilizes hierarchical distributed representations of shape features to encode surface and object boundary over different scales of resolution. Our model makes use of neural mechanisms that model the processing capabilities of early and intermediate stages in visual cortex, namely areas V1-V4 and IT. We suggest that multiple specialized component representations interact by feedforward hierarchical processing that is combined with feedback signals driven by representations generated at higher stages. Based on this, global configurational as well as local information is made available to distinguish changes in the object's contour. Once the outline of a shape has been established, contextual contour configurations are used to assign border ownership directions and thus achieve segregation of figure and ground. The model, thus, proposes how separate mechanisms contribute to distributed hierarchical cortical shape representation and combine with processes of figure-ground segregation. Our model is probed with a selection of stimuli to illustrate processing results at different processing stages. We especially highlight how modulatory feedback connections contribute to the processing of visual input at various stages in the processing hierarchy.

No MeSH data available.


Related in: MedlinePlus