In The Wild Ellipse Parameter Estimation for Circular Dining Plates and Bowls

TL;DR

This paper introduces WildEllipseFit, a method combining ellipse fitting with zero-shot foundational models to accurately detect and estimate elliptical rims of plates and bowls in diverse, real-world food images.

Contribution

It presents a novel approach that integrates semantic understanding from foundational models with traditional ellipse fitting for improved in-the-wild food plate and bowl analysis.

Findings

Effective in diverse real-world scenarios

Outperforms traditional methods in accuracy

Demonstrates zero-shot capability

Abstract

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.