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A Computational Framework for Assembling Pottery Vessels

This research presents a novel computational framework designed to automate the assembly of pottery vessels. Conducted by Stuart Andrews with advisor David H. Laidlaw and members of the SHAPE Lab in the Department of Computer Science, this study addresses the labor-intensive and time-consuming process of reconstructing pots, which can take days of on-site work. Utilizing a virtual artifact database and advanced algorithms for sherd feature analysis, the framework aims to enhance efficiency, allowing archaeologists to focus on more critical aspects of their work. ###

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A Computational Framework for Assembling Pottery Vessels

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  1. A Computational Framework for Assembling Pottery Vessels Presented by: Stuart Andrews Advisor: David H. Laidlaw Members of the SHAPE Lab and the Department of Computer Science The study of 3D shape with applications in archaeology NSF/KDI grant #BCS-9980091

  2. Why should we try to automate pottery vessel assembly? • Reconstructing pots is important • Tedious and time consuming hours  days per pot, 50% of “on-site” time • Virtual artifact database A Computational Framework for Assembling Pottery Vessels

  3. Statement of Problem A Computational Framework for Assembling Pottery Vessels

  4. Statement of Problem A Computational Framework for Assembling Pottery Vessels

  5. Goal To assemble pottery vessels automatically • A computational framework for sherd feature analysis • An assembly strategy A Computational Framework for Assembling Pottery Vessels

  6. Challenges • Integration of evidence • Efficient search • Modular and extensible system design A Computational Framework for Assembling Pottery Vessels

  7. Virtual Sherd Data • Scan physical sherds • Extract iso-surface • Segment break curves • Identify corners • Specify axis 16 sherds 120 pairs ! 560 triples !! A Computational Framework for Assembling Pottery Vessels

  8. A Greedy Bottom-Up Assembly Strategy Single sherds A Computational Framework for Assembling Pottery Vessels

  9. A Greedy Bottom-Up Assembly Strategy Single sherds Pairs A Computational Framework for Assembling Pottery Vessels

  10. A Greedy Bottom-Up Assembly Strategy Single sherds Pairs A Computational Framework for Assembling Pottery Vessels

  11. A Greedy Bottom-Up Assembly Strategy Single sherds Pairs Triples A Computational Framework for Assembling Pottery Vessels

  12. A Greedy Bottom-Up Assembly Strategy Single sherds Pairs Triples A Computational Framework for Assembling Pottery Vessels

  13. A Greedy Bottom-Up Assembly Strategy Etc. Single sherds Pairs Triples A Computational Framework for Assembling Pottery Vessels

  14. Overview Generate Likely Pair-wise Matches Generate Likely 3-Way Matches … etc. A Computational Framework for Assembling Pottery Vessels

  15. Likely Pairs Generate Likely Pair-wise Matches • Proposals • Likelihood Evaluations A Computational Framework for Assembling Pottery Vessels

  16. A Match • A pair of sherds • A relative placement of the sherds A Computational Framework for Assembling Pottery Vessels

  17. Corner Alignment Match Proposals A Computational Framework for Assembling Pottery Vessels

  18. Example Corner Alignments A Computational Framework for Assembling Pottery Vessels

  19. Likely Pairs Generate Likely Pair-wise Matches • Proposals • Likelihood Evaluations A Computational Framework for Assembling Pottery Vessels

  20. Match Likelihood Evaluations • An evaluation returns the likelihood of a feature alignment • Based on the notion of a residual A Computational Framework for Assembling Pottery Vessels

  21. Axis Divergence Feature: Axis of rotation Residual: Angle between axes Match Likelihood Evaluations A Computational Framework for Assembling Pottery Vessels

  22. Axis Separation Feature: Axis of rotation Residual: Distance between axes Match Likelihood Evaluations A Computational Framework for Assembling Pottery Vessels

  23. Break-Curve Separation Feature: Break-curve Residuals: Distance between closest point pairs Match Likelihood Evaluations A Computational Framework for Assembling Pottery Vessels

  24. Break-Curve Divergence Feature: Break-curve Residuals: Angle between tangents at closest point pairs Match Likelihood Evaluations A Computational Framework for Assembling Pottery Vessels

  25. Match Likelihood Evaluations How likely are the measured residuals? • Fact: Assuming the residuals ~ N(0,1) i.i.d., then we can form a Chi-square: ²observed • Note: Typically, residuals are ~ N(0, 2) i.i.d. A Computational Framework for Assembling Pottery Vessels

  26. Match Likelihood Evaluations How likely are the measured residuals? • We define the likelihood of the match using the probability of observing a larger ²random Pr{ ²random > ²observed } = Q • Individual or ensemble of features • Pair-wise, 3-Way or larger matches A Computational Framework for Assembling Pottery Vessels

  27. Example Match Likelihood Evaluation (1) A Computational Framework for Assembling Pottery Vessels

  28. Example Match Likelihood Evaluation (2) A Computational Framework for Assembling Pottery Vessels

  29. Local Improvement of Match Likelihood before after A Computational Framework for Assembling Pottery Vessels

  30. Pair-wise Match Results Summary ?? A Computational Framework for Assembling Pottery Vessels

  31. Pair-wise Match Results Summary Correct Matches Incorrect Matches A Computational Framework for Assembling Pottery Vessels

  32. Pair-wise Match Results Summary # of pairs with correct match identified: Proposed matches Correct match True Pair … Q=1  decreasing likelihood  Q=0 There is no correct match for the remaining 94 pairs!! A Computational Framework for Assembling Pottery Vessels

  33. Overview Generate Likely Pair-wise Matches Generate Likely 3-Way Matches … etc. A Computational Framework for Assembling Pottery Vessels

  34. Likely Triples Generate Likely 3-Way Matches • 3-Way Proposals • 3-Way Likelihood Evaluations A Computational Framework for Assembling Pottery Vessels

  35. 3-Way Match Proposals • Merge pairs with common sherd + = A Computational Framework for Assembling Pottery Vessels

  36. 3-Way Match Likelihood Evaluation • Feature alignments are measured 3-way A Computational Framework for Assembling Pottery Vessels

  37. 3-Way Match Results Summary A Computational Framework for Assembling Pottery Vessels

  38. 3-Way Match Results Summary # of 3-way matches with correct match identified: A Computational Framework for Assembling Pottery Vessels

  39. Overview Generate Likely Pair-wise Matches Generate Likely 3-Way Matches … etc. Future work A Computational Framework for Assembling Pottery Vessels

  40. Related Work • Assembly systems that rely on single features [U. Fedral Fluminense / Middle East Technical U. / U. of Athens] • Multiple features and parametric shape models [The SHAPE Lab – Brown U.] • Distributed systems for solving AI problems [Toronto / Michigan State / Duke U.] A Computational Framework for Assembling Pottery Vessels

  41. Contributions • A computational framework based on match proposal and likelihood evaluation • A method for combining multiple features into one match likelihood • An example (greedy) assembly strategy A Computational Framework for Assembling Pottery Vessels

  42. Where to go from here? • Improve accuracy of features • Add new features and feature comparisons • Learn how to classify true and false pairs • Design specialized search strategies A Computational Framework for Assembling Pottery Vessels

  43. Conclusions • Encouraging progress on a difficult task • We are close to a working system • We can get closer by following this approach • A uniform statistical analysis of features defines the basis for a complete working system A Computational Framework for Assembling Pottery Vessels

  44. References • D. Cooper et al. VAST 2001. • da Gama Leito et al. Universidade Fedral Fluminense 1998. • A.D. Jepson et al. ICCV 1999. • G.A. Keim et al. AAAI / IAAI, 1999. • S. Pankanti et al. Michigan State, 1994. • G. Papaioannou et al. IEEE Computer Graphics and Applications, 2001. • G. Ucoluk et al. Computers & Graphics, 1999. A Computational Framework for Assembling Pottery Vessels

  45. Results For Discussion count Q count Q A Computational Framework for Assembling Pottery Vessels

  46. Results For Discussion A Computational Framework for Assembling Pottery Vessels

  47. Results For Discussion A Computational Framework for Assembling Pottery Vessels

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