On Aligning Curves

By Thomas B. Sebastian, Philip N. Klein, Benjamin B. Kimia. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003

411 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/87313317e8bf7f8dc9912f25772e1784c55caead

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In Computational Mechanics

In Computer Vision and Pattern Recognition

Citations in the corpus

Analysis of planar shapes using geodesic paths on shape spaces (2004, methodology): “) There are other approaches to interpolating between closed curves (see, for example, [24]).”
Time-series data mining (2012, methodology): “Several parametric temporal models may be considered, including statistical modeling by feature extraction [Nanopoulos et al. 2001], ARMA models [Kalpakis et al. 2001], Markov Chains (MCs) [Sebastiani et al. 1999], or HMM [Panuccio et al. 2002].”
An Efficient Earth Mover's Distance Algorithm for Robust Histogram Comparison (2007, methodology): “Algorithm CSS [33] Visual Parts [23] SC+TPS ( χ(2)) [3] Curve Edit [43] Distance Set [9] Score 75.”
Recognition of shapes by editing their shock graphs (2004, methodology): “…representations, the matching is done either by aligning ordered point sets using an optimal similarity transformation [1], or by finding a mapping from one curve to another that minimizes an elastic performance functional consisting of stretching and bending terms [2], [10], [36], [33].”
Shape Classification Using the Inner-Distance (2007, background): “shape contexts [5], curve edit distance [38], and generative”
Shape Analysis of Elastic Curves in Euclidean Spaces (2011, methodology): “To illustrate the strengths and limitations of a gradientbased approach with respect to a common DP algorithm [7], [26], we present a comparison of computational costs (using Matlab on a 2.”
A Similarity-Based Aspect-Graph Approach to 3D Object Recognition (2004, positive): “The curve matching metric is obtained by finding the least action path in deforming one curve to another (Sebastian et al., 2001a; Sebastian et al., 2003), while the shock match metric is computed by finding the least action path in deforming the shape represented by its shock graph (Sebastian et al”
Learning Context-Sensitive Shape Similarity by Graph Transduction (2010, neutral): “Context Model CSS [38] [4] [1] [39] [40] [41] [42] [43] [2] [44] Score 75.”
Shape retrieval using triangle-area representation and dynamic space warping (2007, methodology): “For shape matching, a DP algorithm, which is called dynamic space warping (DSW), is employed to find the best alignment between two shape representations.”
Hierarchical Matching of Deformable Shapes (2007, methodology): “However, as described in [2, 23] these methods can”
Robust point matching for nonrigid shapes by preserving local neighborhood structures (2006, background): “Sebastian et al. [5] demonstrated the effectiveness of point ordering in matching curves, but for general shapes other than curves, local point ordering is hard to describe, and is ignored in many point matching algorithms [4].”
Shape representation and classification using the Poisson equation (2004, methodology): “Local properties are used for registration and recognition, as well as similarity judgement [1, 9, 21].”
Integral Invariants for Shape Matching (2006, methodology): “Many researchers have addressed the comparison of objects under small deformations in a way that is invariant with respect to various transformation groups (see Section 2); fewer have addressed the sensitivity to high-frequency noise and yet fewer have addressed localized changes [76], [65].”