Surface-Related Features Figure 14. Evaluation of extracted junction signals on synthetic test image. A synthetic test image (A) reproduced from Smith and Brady (1997) was used to evaluate and compare extracted junction signals against a computational scheme (structure tensor) for corner detection proposed by Harris and Stephens (1988). The extracted junction saliency is visualized in a feature likelihood map (B) and detected junction positions/types are superimposed on input image (C). ROC curves are computed from structure tensor results (dashed), initial model responses (dotted) and from converged model responses after 4 recurrent cycles (solid) (D). Abscissa denotes the false alarm rate, and the ordinate denotes the hit rate. Note, that for better visibility the abscissa has been scaled to [0, 0.1]. doi:10.1371/journal.pone.0005909.g014 Discussion In this section we begin by summarizing our main findings. Then, we compare our model with other proposed models that are related to our work. Moreover, we show that all core mechanisms employed in our model are biological plausible. We also discuss how junction signals that are extracted from our neural representation could be used by other cortical areas to solve visual tasks such as depth ordering, figure-ground segregation or motion correspondence finding. Finally, we briefly discuss some examples where our model fails to produce accurate results. Summary of findings We presented a recurrent model of V1-V2 contour processing utilizing long-range interactions in combination with short-range lateral inhibition which as been adapted from [33]. We have shown quantitatively and qualitatively that recurrent combination of contextual features substantially enhances the initial estimates of local contrast. We have also shown that the model V2 cells are capable of generating illusory contour groupings which strongly influence the interpretation of different junction type estimates of local contrast. In addition, we demonstrated that cell activities represented in both model areas can be combined and extracted to robustly signal three different sorts of junctions (L-,T-, and X-junctions). These junctions provide important cues for fundamental visual processes such as surface completion and figure-ground segrega- tion [1,39,43]. Furthermore, our model predicts a hysteresis effect between opaque-transparent and transparent-opaque transitions which could be experimentally validated in a psychophysical experiment. Finally, we have demonstrated in a quantitative analysis that our model responses outperform a state-of-the-art computer vision corner detection scheme. Related work A number of different models have been proposed for contour integration. A comprehensive review can be found in [44]. Our contour integration model which utilizes interaction of feedfor- ward and feedback, in particular modulatory feedback, has been applied successfully to a number of different tasks of visual processing such as optical flow estimation [45], texture processing [46], selective attention [47], cortico-thalamic enhancement [48], and linking synchronization [49]. PLoS ONE | www.plosone.org 16 June 2009 | Volume 4 | Issue 6 | e5909