Extraction of Surface-Related Features in a Recurrent Model of V1-V2 Interactions Ulrich Weidenbacher*, Heiko Neumann Institute of Neural Information Processing, University of Ulm, Ulm, Germany 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. Citation: Weidenbacher U, Neumann H (2009) Extraction of Surface-Related Features in a Recurrent Model of V1-V2 Interactions. PLoS ONE 4(6): e5909. doi:10.1371/journal.pone.0005909 Editor: Eshel Ben-Jacob, Tel Aviv University, Israel Received February 12, 2009; Accepted May 8, 2009; Published June 15, 2009 Copyright: 2009 Weidenbacher, Neumann. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: Part of this research has been supported by grants from the European Union, ICT-project no. 215866-SEARISE, and the Transregional Collaborative Research Centre SFB/TRR 62 ‘‘Companion-Technology for Cognitive Technical Systems’’ funded by the German Research Foundation (DFG). Details on the SEARISE project can be found at www.searise.eu. Details on the SFB/TRR 62 can be found at http://www.informatik.uni-ulm.de/ki/sfb-trr-62/. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. * E-mail: ulrich.weidenbacher@uni-ulm.de Introduction Our visual system structures the visual world into surfaces that, if required, we recognize as familiar objects. A fundamental task of vision therefore is to find the boundary contours separating the regions corresponding to surfaces or objects. As our retina captures only a 2D projection of the 3D world, mutual occlusions are a natural consequence which can be interpreted by the visual system as a cue to relative depth. A vivid demonstration of surface-based depth perception is given by a painting of a professional artist who tries to depict a scene where the visual system generates surface segmentations in the presence of multiple occlusions (Figure 1). However, it remains unclear what particular features are used by the visual system to detect occlusions and whether this information is derived locally or from more global criteria. Some recent evidence [1,2,3] suggests that the human visual system might use surface-related features that are specific contour junctions that have a surface-based relevance in scene interpretation. In this contribution, we propose a neural model that suggests how surface-related features can be extracted from a 2D luminance image. The approach is based on contour grouping mechanisms found in visual cortical areas V1 and V2. Our computational model comprises the extraction of oriented contrasts which are subsequently integrated by short- and long-range grouping mechanisms to generate disambiguated and stabilized boundary representations. We argue that the mutual interactions realized by lateral interactions and recurrent feedback between the cortical areas considered stabilize the representation of fragments of outlines and group them together. Moreover, we demonstrate that the model is able to signal and complete illusory contours over a few time-steps. Illusory contours are a form of visual illusion where contours are perceived without a luminance or color change across the contour. Such illusory contours can be induced by partially occluded surfaces where the contour of the occluded object is PLoS ONE | www.plosone.org 1 June 2009 | Volume 4 | Issue 6 | e5909