C4.5: programs for machine learning
C4.5: programs for machine learning
A Multiscale Method for the Reassembly of Two-Dimensional Fragmented Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stochastic 3d geometric models for classification, deformation, and estimation
Stochastic 3d geometric models for classification, deformation, and estimation
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
Reassembling fractured objects by geometric matching
ACM SIGGRAPH 2006 Papers
A Texture Based Matching Approach for Automated Assembly of Puzzles
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Contour-shape based reconstruction of fragmented, 1600 BC wallpaintings
IEEE Transactions on Signal Processing
Learning how to match fresco fragments
Journal on Computing and Cultural Heritage (JOCCH)
Computer Graphics Forum
Field-guided registration for feature-conforming shape composition
ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH Asia 2012
Transformed polynomials for global registration of point clouds
Proceedings of the 27th Spring Conference on Computer Graphics
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We present a multiple-feature approach for determining matches between small fragments of archaeological artifacts such as Bronze-Age and Roman frescoes. In contrast with traditional 2D and 3D shape matching approaches, we introduce a set of feature descriptors that are based on not only color and shape, but also normal maps. These are easy to acquire and combine high data quality with discriminability and robustness to some types of deterioration. Our feature descriptors range from general-purpose to domain-specific, and are quick to compute and match. We have tested our system on three datasets of fresco fragments, demonstrating that multi-cue matching using different subsets of features leads to different tradeoffs between efficiency and effectiveness. In particular, we show that normal-based features are more effective than color-based ones at similar computational complexity, and that 3D features are more discriminative than ones based on 2D or normals, but at higher computational cost. We also demonstrate how machine learning techniques can be used to effectively combine our new features with traditional ones. Our results show good retrieval performance, significantly improving upon the match prediction rate of state-of-the-art 3D matching algorithms, and are expected to extend to general matching problems in applications such as texture synthesis and forensics.