Contour Completion without Region Segmentation

Abstract of Contour completion without Region On MAT LAB System

Contour Completion without Region Segmentation,Contour completion plays an important role in visual perception, where the goal is to group fragmented low-level edge elements into perceptually coherent and salient contours.

Most existing methods for contour completion have focused on pixelwise detection accuracy.

In contrast, fewer methods have addressed the global contour closure effect, despite psychological evidences for its importance.

This paper proposes a purely contour-based higher order CRF model to achieve contour closure, through local connectedness approximation.

This leads to a simplified problem structure, where our higher order inference problem can be transformed into an integer linear program and be solved efficiently.

Compared with the methods based on the same bottom–up edge detector, our method achieves a superior contour grouping ability (measured by Rand index), a comparable precision–recall performance, and more visually pleasing results.

 Conclusion

Given local detection results, our model seeks the maximum a posteriori solution of the conditional probabilistic distribution of contours.

we describe how to construct our new CRF model, and how to use it to represent multiple image contours.

Understanding the mechanism for contour-completionholds the promise to develop better-performing image understanding systems.

We hope this Contour Completion without Region Segmentation work will provide useful ideas for perceptual grouping research.