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Bilge Sayim

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Publications associées (23)

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Capsule networks as recurrent models of grouping and segmentation

Michael Herzog, Adrien Christophe Doerig, Bilge Sayim, Mauro Manassi, Lynn Schmittwilken

Classically, visual processing is described as a cascade of local feedforward computations. Feedforward Convolutional Neural Networks (ffCNNs) have shown how powerful such models can be. However, using visual crowding as a well-controlled challenge, we pre ...

2020

Crowding, grouping, and object recognition: A matter of appearance

Michael Herzog, Bilge Sayim, Mauro Manassi, Vitaly Chicherov

In crowding, the perception of a target strongly deteriorates when neighboring elements are presented. Crowding is usually assumed to have the following characteristics. (a) Crowding is determined only by nearby elements within a restricted region around t ...

Association for Research in Vision and Ophthalmology2015

How color, regularity, and good Gestalt determine backward masking

Michael Herzog, Bilge Sayim, Mauro Manassi

The strength of visual backward masking depends on the stimulus onset asynchrony (SOA) between target and mask. Recently, it was shown that the conjoint spatial layout of target and mask is as crucial as SOA. Particularly, masking strength depends on wheth ...

Association for Research in Vision and Ophthalmology2014

Crowding of crowding leads to uncrowding

Michael Herzog, Bilge Sayim, Mauro Manassi

In crowding, target discrimination is impaired by flanking elements. What happens if a target is neighbored by flankers A and in addition by further flankers B? According to pooling models, crowding is the consequence of averaging features of target and fl ...

2013

When crowding of crowding leads to uncrowding

Michael Herzog, Bilge Sayim, Mauro Manassi

In object recognition, features are thought to be processed in a hierarchical fashion from low-level analysis (edges and lines) to complex figural processing (shapes and objects). Here, we show that figural processing determines low-level processing. Verni ...

Association for Research in Vision and Ophthalmology2013

Grouping, pooling, and when bigger is better in visual crowding

Michael Herzog, Bilge Sayim, Mauro Manassi

In crowding, perception of a target is strongly deteriorated by nearby elements. Crowding is often explained by pooling models predicting that adding flankers increases crowding. In contrast, the centroid hypothesis proposes that adding flankers decreases ...

Association for Research in Vision and Ophthalmology2012

Quantifying target conspicuity in contextual modulation by visual search

Michael Herzog, Bilge Sayim

Contextual elements can strongly modulate visual performance. For example, performance deteriorates when a vernier is flanked by neighboring lines. On a neural level, such contextual modulation is often explained by local spatial interactions such as later ...

Association for Research in Vision and Ophthalmology2011

When bigger is better

Michael Herzog, Bilge Sayim, Mauro Manassi

One of the main factors of crowding is the spacing between target and flankers. The closer the flankers are to the target, the stronger is crowding. Recently, it was proposed that crowding strength is determined by the distance between target and flanker c ...

2011

Grouping trumps pooling and centroids in crowding

Michael Herzog, Bilge Sayim, Mauro Manassi

In the periphery, discrimination of a target is usually impaired by flanking elements. This crowding effect is often explained in terms of pooling, ie target and flanker signals are averaged. The pooling hypothesis predicts stronger crowding when more flan ...

2011

How grouping determines crowding

Michael Herzog, Bilge Sayim, Mauro Manassi

The spacing between target and flankers is one of the main factors of crowding. The closer the flankers are to the target, the stronger is crowding. Recently, it was proposed that crowding strength is determined by the distance between target and flanker c ...

2011