PointFlow: A model for automatically tracing object boundaries and inferring illusory contours

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Abstract

In this paper, we propose a novel method for tracing object boundaries automatically based on a method called “PointFlow” in image induced vector fields. The PointFlow method comprises two steps: edge detection and edge integration. Basically, it uses an ordinary differential equation for describing the movement of points under the action of an image-induced vector field and generates induced trajectories. The trajectories of the flows allow to find and integrate edges and determine object boundaries. We also extend the original PointFlow method to make it adaptable to images with complicated scenes. In addition, the PointFlow method can be applied to infer certain illusory contours. We test our method on real image dataset. Compared with the other classical edge detection and integration models, our PointFlow method is better at providing precise and continuous curves. The experimental results clearly exhibit the robustness and effectiveness of the proposed method.

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Yang, F., Bruckstein, A. M., & Cohen, L. D. (2018). PointFlow: A model for automatically tracing object boundaries and inferring illusory contours. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10746 LNCS, pp. 485–498). Springer Verlag. https://doi.org/10.1007/978-3-319-78199-0_32

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