mlsanityData to 14 Oct 2026

A comprehensive study of clustering a class of 2D shapes

arXiv 2111.06662 ↗First posted 12 Nov 2021cs.CVcited by 1

Not on the map. Why?

Abstract (arXiv)

The paper concerns clustering with respect to the shape and size of 2D contours that are boundaries of cross-sections of 3D objects of revolution. We propose a number of similarity measures based on combined disparate Procrustes analysis (PA) and Dynamic Time Warping (DTW) distances. Motivation and the main application for this study comes from archaeology. The performed computational experiments refer to the clustering of archaeological pottery.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 3 of its 54 references that are in the map. First posted on arXiv 12 Nov 2021.

Builds on

3 of 54 references.

References · 3

  1. 2019Evaluation Metrics for Unsupervised Learning Algorithmscited by 16
  2. 2015Fr'echet Distance for Curves, Revisitedcited by 4
  3. 2019Procrustes registration of two-dimensional statistical shape models without correspondencescited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026On the application of the Wasserstein metric to 2D curves classificationcited by 0