  Ellipse estimation is an important topic in food image processing because it
can be leveraged to parameterize plates and bowls, which in turn can be used to
estimate camera view angles and food portion sizes. Automatically detecting the
elliptical rim of plates and bowls and estimating their ellipse parameters for
data "in-the-wild" is challenging: diverse camera angles and plate shapes could
have been used for capture, noisy background, multiple non-uniform plates and
bowls in the image could be present. Recent advancements in foundational models
offer promising capabilities for zero-shot semantic understanding and object
segmentation. However, the output mask boundaries for plates and bowls
generated by these models often lack consistency and precision compared to
traditional ellipse fitting methods. In this paper, we combine ellipse fitting
with semantic information extracted by zero-shot foundational models and
propose WildEllipseFit, a method to detect and estimate the elliptical rim for
plate and bowl. Evaluation on the proposed Yummly-ellipse dataset demonstrates
its efficacy and zero-shot capability in real-world scenarios.
