Surface-Related Features Figure 1. (A) Painting of a professional artist [Marrara, M., 2002, reproduction with permission from the artist] that leads to the perception of different depths induced by occlusion and colour cues. Notice how hidden surface parts are perceptually completed by the human visual system in order to segregate surfaces apart from each other. Surfaces can also be associated with (parts of) objects in scenes depicted by trees and clouds in (B). A human observer could use local cues such as T-junctions (red) formed by the boundary contour of surface parts to detect surface occlusions and hence to infer depth from monocular scenes. doi:10.1371/journal.pone.0005909.g001 perceptually completed (amodal completion) or where the occluding object has the same luminance than parts of the occluded background (modal completion). Illusory contours play a significant role in the perceptual interpretation of junction features. For instance, it was suggested by Rubin [1] that the perception of occlusion-based junctions (T-junctions) can be induced by L-junctions in combination with the presence of illusory contours. Consistently, in our model junction signals are read out from completed boundary groupings which are interpreted as intermediate-level representations that allow for the correct perceptual interpretation of junctions, namely L- junctions features can be perceptually interpreted as T-junctions. This is unlike previous approaches which are based on purely feature-based junction detection schemes [4,5]. Taken together, our proposed model suggests how surface-based features could be extracted and perceptually interpreted by the visual system. At the same time, this leads to improved robustness and clearness of surface-based feature representations and hence to an improved performance of extracted junction signals compared to standard computer vision corner detection schemes. Based on these perceptual representations, surface-related junctions are made explicit such that they could be interpreted to interact as to generate surface-segmentations in static or temporally varying images. Methods In this section we give a short overview of the proposed model and its components. Our model focuses on the early processing stages of form processing in primate visual cortex, namely cortical areas V1 and V2, and incorporates hierarchical feedforward processing as well as top-down feedback connections to consider the signal flow along the reverse hierarchy processing [6]. An overview of the model architecture is depicted in Figure 2. Overview of the model architecture The model has been structured into three main components. The first component comprises initial feedforward processing. The monochromatic input image is processed by a cascade of different pools of model cells in V1, specifically simple, complex, end-stop, and bipole cells. Each cell population consists of cells that are tuned to different orientation selectivity. This is consistent with the representation of orientation selective cells found in V1 which are arranged in hypercolumns [7]. Our model cell types are responsive for specific local image structures, i.e., simple and complex cells are sensitive to oriented contrast represented by edges or bar elements, end-stop cells respond best to contour terminations that occur, e.g., at line ends or corners, and bipole cells are sensitive for collinear arrangements of contour fragments with similar orientation. Model area V2 receives forward projections from V1 bipole cells and V1 end-stop cells. These signals are then integrated by long-range V2 bipole cells which have a larger extent than bipole cells in model V1. Bipole cells in V2 respond to luminance contrasts as well as to illusory contours, thus resembling functional properties of contour neurons in V2 [8,9,10]. This finding suggests that orientation selective mechanisms for contour integration in area V2 do not simply represent a scaled version of V1 mechanisms for lateral contrast integration. Their capability to integrate activities to bridge gaps and generate illusory contours makes an important step towards surface boundary segregation while V1 contrast integration is selective to stimulus feature processing. The second model component comprises recurrent feedback processing between model areas V1 and V2. Neurons in V1 are also responsive to more global arrangements of the scene [11]. These response properties possibly arise from recurrent processing and lateral connections from pyramidal neurons [12]. Whereas feedforward connections have mainly driving character, feedback connections are predominantly modulatory in their effects [13]. There is evidence that feedback originating in higher level visual areas such as V2, V4, IT or MT, from cells with bigger receptive fields and more complex response properties can manipulate and shape V1 responses, accounting for contextual or extra-classical receptive field effects [14,15,16]. We account for these findings by incorporating a recurrent interaction mechanism between model areas V1 and V2. In our model, activity in V2 serves as top-down feedback signal to iteratively improve initial feedforward activity in V1. The feedback signals that are delivered by descending cortical pathways multiplicatively enhance initial activities at earlier processing stages. Importantly, this type of feedback is not PLoS ONE | www.plosone.org 2 June 2009 | Volume 4 | Issue 6 | e5909