# Image-Based Method For Measuring And Classification Of Iron Ore Pellets Using Star-Convex Polygons

arXiv: https://arxiv.org/abs/2506.11126 · HTML: https://mlsanity.com/paper/2506.11126/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 17 references that are in the map.
- First posted on arXiv 10 Jun 2025.

## Builds on (most cited first, 12 of 12)

- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [EfficientNetV2: Smaller Models and Faster Training](https://mlsanity.com/paper/2104.00298/) (2021)
- [Panoptic Segmentation](https://mlsanity.com/paper/1801.00868/) (2018)
- [FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows](https://mlsanity.com/paper/2111.07677/) (2021)
- [Statistical Aspects of Wasserstein Distances](https://mlsanity.com/paper/1806.05500/) (2018)
- [Cell Detection with Star-convex Polygons](https://mlsanity.com/paper/1806.03535/) (2018)
- [InstanceCut: from Edges to Instances with MultiCut](https://mlsanity.com/paper/1611.08272/) (2016)
- [Star-convex Polyhedra for 3D Object Detection and Segmentation in Microscopy](https://mlsanity.com/paper/1908.03636/) (2019)
- [CoNIC: Colon Nuclei Identification and Counting Challenge 2022](https://mlsanity.com/paper/2111.14485/) (2021)
- [Nuclei instance segmentation and classification in histopathology images with StarDist](https://mlsanity.com/paper/2203.02284/) (2022)
- [Complete Scanning Application Using OpenCv](https://mlsanity.com/paper/2107.03700/) (2021)

## Abstract (arXiv)

We would like to present a comprehensive study on the classification of iron ore pellets, aimed at identifying quality violations in the final product, alongside the development of an innovative imagebased measurement method utilizing the StarDist algorithm, which is primarily employed in the medical field. This initiative is motivated by the necessity to accurately identify and analyze objects within densely packed and unstable environments. The process involves segmenting these objects, determining their contours, classifying them, and measuring their physical dimensions. This is crucial because the size distribution and classification of pellets such as distinguishing between nice (quality) and joint (caused by the presence of moisture or indicating a process of production failure) types are among the most significant characteristics that define the quality of the final product. Traditional algorithms, including image classification techniques using Vision Transformer (ViT), instance segmentation methods like Mask R-CNN, and various anomaly segmentation algorithms, have not yielded satisfactory results in this context. Consequently, we explored methodologies from related fields to enhance our approach. The outcome of our research is a novel method designed to detect objects with smoothed boundaries. This advancement significantly improves the accuracy of physical dimension measurements and facilitates a more precise analysis of size distribution among the iron ore pellets. By leveraging the strengths of the StarDist algorithm, we aim to provide a robust solution that addresses the challenges posed by the complex nature of pellet classification and measurement.