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Shape recognition by using Scale Invariant Feature Transform for contour
Author(s)
Rojanamontien, Mathara
Watchareeruetai, Ukrit
Date Issued
September 5, 2017
Type
Conference Paper
Abstract
This paper proposes a novel shape feature extractor named Contour-SIFT along with a matching method that computes the similarity between two set of proposed descriptors. It allows a shape to be recognized based on automatically located outstanding local features on its contour, which are extracted from 1-D signal representations of different smoothing scales. The algorithm describes each local feature as a list of frequencies from curvature histogram, which is created from curve segment around each local position. The descriptors will give high similarity compared with a model descriptors of a similar shape. The algorithm has properties of image scaling-, translation-, and rotation-invariants. An experiment were conducted with 200 images from Flavia dataset for verification. The result of using the proposed algorithm is compared with the result of using CSS.
Citation
Proceedings of the 2017 14th International Joint Conference on Computer Science and Software Engineering Jcsse 2017, 2017