Boundary Attention: Learning to Find Faint Boundaries at Any Resolution
We present a differentiable model that explicitly models boundaries --
including contours, corners and junctions -- using a new mechanism that we call
boundary attention. We show that our model provides accurate results even when
the boundary signal is very weak or is swamped by noise. Compared to previous
classical methods for finding faint boundaries, our model has the advantages of
being differentiable; being scalable to larger images; and automatically
adapting to an appropriate level of geometric detail in each part of an image.
Compared to previous deep methods for finding boundaries via end-to-end
training, it has the advantages of providing sub-pixel precision, being more
resilient to noise, and being able to process any image at its native
resolution and aspect ratio.
6594ccf589f1ff04632ec971
Mia Gaia Polansky
6594ccf589f1ff04632ec972
Charles Herrmann
6594ccf589f1ff04632ec973
Junhwa Hur
6594ccf589f1ff04632ec974
Deqing Sun
6594ccf589f1ff04632ec975
Dor Verbin
6594ccf589f1ff04632ec976
Todd Zickler
Boundary Attention: Learning to Find Faint Boundaries at Any Resolution
We present a differentiable model that explicitly models boundaries --
including contours, corners and junctions -- using a new mechanism that we call
boundary attention. We show that our model provides accurate results even when
the boundary signal is very weak or is swamped by noise. Compared to previous
classical methods for finding faint boundaries, our model has the advantages of
being differentiable; being scalable to larger images; and automatically
adapting to an appropriate level of geometric detail in each part of an image.
Compared to previous deep methods for finding boundaries via end-to-end
training, it has the advantages of providing sub-pixel precision, being more
resilient to noise, and being able to process any image at its native
resolution and aspect ratio.
A differentiable model using boundary attention accurately detects weak and noisy boundaries in images, offering scalable, adaptive, and sub-pixel precise performance.
differentiable model boundary attention contours corners junctions sub-pixel precision end-to-end training