Surface-Related Features Figure 10. Saliency maps for different junction types. The junction maps (A), (B), and (D) were extracted from model activities presented in Figure 9. Bright regions indicate positions where the ensemble of model activities suggests for the presence of the respective junction type (L-, T-, X- junction). A combined feature likelihood map(C) incorporates all features, colour coded. Blue signals the presence of X-junctions, red signals T- junctions and green signals the presence of L-junctions. Moreover, position and type of detected features is visualized in (E) (based on maximum likelihood selection). doi:10.1371/journal.pone.0005909.g010 along the illusory contour turn into T-junctions. The emergence of T-junctions in turn supports the perceptual interpretation of occlusions. A similar effect can be observed in Figure 12 where line endings initially produce no junction signals. After a few feedforward- feedback cycles, however, the emerging illusory contour responses of V2 bipole cells lead to T-junction signals along the contour. Processing of real-world data In order to examine how the model performs for real-world camera images we used an image taken from a desk scene where several papers and a transparent foil are arranged such that they partly occlude each other (Figure 13). Model activities and the extracted feature map demonstrate that the model is also capable of dealing with real-world images. Note, that one of the papers has a very low contrast ratio with respect to the background. Nevertheless, the model performs excellent in finding the contour and the respective junctions. This also underlines that the model is invariant to contrast changes and thus also stable against changes of illumination conditions. Quantitative evaluation and comparison In this section, we evaluate our model by comparing our recurrent junction detection scheme with results obtained by simply switching the recurrent feedback cycle off, which reduces the model to an ordinary feedforward model. Moreover, we compare our results to a standard computer vision corner detection scheme based on the Harris corner detector [4]. In a comparative study of different corner detection schemes, the Harris corner detector provides the best results among five corner detectors [42]. As input for our comparison, we use corner test image adapted from Smith and Brady [5] that poses several challenges such as, e.g., low contrast regions, smooth luminance gradients, or obtuse and acute angles. Moreover, all types of junctions (L, T and X) considered are represented in the test image together with information about their exact position (ground truth PLoS ONE | www.plosone.org 12 June 2009 | Volume 4 | Issue 6 | e5909