English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  A model for multi-stable dynamics in action recognition modulated by integration of silhouette and shading cues

Fedorov, L., & Giese, M. (2016). A model for multi-stable dynamics in action recognition modulated by integration of silhouette and shading cues. Poster presented at Twenty-Fifth Annual Computational Neuroscience Meeting (CNS*2016), Jeju, South Korea.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/21.11116/0000-0000-7B46-8 Version Permalink: http://hdl.handle.net/21.11116/0000-0004-DBE6-2
Genre: Poster

Files

show Files

Locators

show
hide
Locator:
Link (Any fulltext)
Description:
-

Creators

show
hide
 Creators:
Fedorov, LA1, Author              
Giese, MA1, Author              
Affiliations:
1International Max Planck Research School, University of Tübingen,, ou_persistent22              

Content

show
hide
Free keywords: -
 Abstract: The visual perception of body motion can show interesting multi-stability. For example, a walking body silhouette (bottom inset Fig. 83A) is seen alternately as walking in two different directions [1]. For stimuli with minimal texture information, such as shading, this multi-stability disappears. Existing neural models for body motion perception [2–4] do not reproduce perceptual switching. Extending the model [2], we developed a neurodynamic model that accounts for this multi-stability (Fig. 83A). The core of the model is a two-dimensional neural field that consists of recurrently coupled neurons with selectivity for instantaneous body postures (‘snapshots’). The dimensions of the field encode the keyframe number θ and the view of the walker ϕ. The lateral connectivity of the field stabilizes two competing traveling pulse solutions that encode the perceived temporally changing action patterns (walking in the directions ±45°). The input activity of the field is generated by two visual pathways that recognize body postures from gray-level input movies. One pathway (‘silhouette pathway’) was adapted from [2] and recognizes shapes, mainly based on the contrast edges between the moving figure and the background. The second pathway is specialized for the analysis of luminance gradients inside the moving figure. Both pathways are hierarchical (deep) architectures, built from detectors that reproduce known properties of cortical neurons. Higher levels of the hierarchies extract more complex features with higher degree of position/scale invariance. The field activity is read out by two Motion Pattern (MP) neurons, which encode the two possible perceived walking directions. Testing the model with an unshaded silhouette stimulus, it produces randomly switching percepts that alternate between the walking directions (±45°) (Fig. 83B, C). Addition of shading cues disambiguates the percept and removes the bistability (Fig. 83D). The developed architecture accounts for the disambiguation by shape-from shading.

Details

show
hide
Language(s):
 Dates: 2016-08
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: DOI: 10.1186/s12868-016-0283-6
BibTex Citekey: FedorovG2016_2
 Degree: -

Event

show
hide
Title: Twenty-Fifth Annual Computational Neuroscience Meeting (CNS*2016)
Place of Event: Jeju, South Korea
Start-/End Date: 2016-07-02 - 2016-07-07

Legal Case

show

Project information

show

Source 1

show
hide
Title: BMC Neuroscience
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: BioMed Central
Pages: - Volume / Issue: 16 (Supplement 1) Sequence Number: P156 Start / End Page: 89 Identifier: ISSN: 1471-2202
CoNE: https://pure.mpg.de/cone/journals/resource/111000136905018