Significantly Improving Scan-Based Shape Representations Using Rotational Key Feature Points

Ebrahim, Y.; Ahmed, Maher; Chau, Siu-Cheung; Abdelsalam, W.

doi:10.1007/978-3-642-13772-3_29

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2014

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“…A portion of the onedimensional representation with the lowest variance is extracted to produce a representation of reduced length and high similarity across the class. An extension to the key feature point [7] denoted rotational key feature points involves concatenating representations from rotated instances of the same shape and identifying key feature points.…”

Section: Shape Matchingmentioning

confidence: 99%

Exploiting Novel Properties of Space-Filling Curves for Data Analysis

Weston

2014

Advances in Intelligent Data Analysis XIII

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Abstract. Using space-filling curves to order multidimensional data has been found to be useful in a variety of application domains. This paper examines the space-filling curve induced ordering of multidimensional data that has been transformed using shape preserving transformations. It is demonstrated that, although the orderings are not invariant under these transformations, the probability of an ordering is dependent on the geometrical configuration of the multidimensional data. This novel property extends the potential applicability of space-filling curves and is demonstrated by constructing novel features for shape matching.

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Section: Shape Matchingmentioning

confidence: 99%

Exploiting Novel Properties of Space-Filling Curves for Data Analysis

Weston

2014

Advances in Intelligent Data Analysis XIII

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show abstract

Significantly Improving Scan-Based Shape Representations Using Rotational Key Feature Points

Cited by 1 publication

References 6 publications

Exploiting Novel Properties of Space-Filling Curves for Data Analysis

Exploiting Novel Properties of Space-Filling Curves for Data Analysis

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