Field of Junctions

Abstract: We introduce a bottom-up model for jointly finding many boundary elements in an image, including edges, curves, corners and junctions. The model explains boundary shape in each small patch using a junction with M angles and a freely-moving vertex. Images are analyzed by solving a non-convex optimization problem using purposefully-designed algorithms, cooperatively finding M+2 junction values at every pixel. The resulting field of junctions is simultaneously an edge detector, a corner/junction detector, and a boundary-aware smoothing of regional appearance. We demonstrate how it behaves at different scales, and for both single-channel and multi-channel input. Notably, we find it has unprecedented resilience to noise: It succeeds at high noise levels where previous methods for segmentation and for edge, corner and junction detection fail.

Publication

Dor Verbin and Todd Zickler, "Field of Junctions: Extracting Boundary Structure at Low SNR", International Conference on Computer Vision (ICCV), 2021.

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Citation

            @InProceedings{verbin2021foj,
                author    = {Verbin, Dor and Zickler, Todd},
                title     = {Field of Junctions: Extracting Boundary Structure at Low {SNR}},
                booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
                month     = {October},
                year      = {2021},
                pages     = {6869-6878}
            }