article

Shape matching and classification using height functions

Authors:

Longin Jan LateckiAuthors Info & Claims

Pattern Recognition Letters, Volume 33, Issue 2

Pages 134 - 143

https://doi.org/10.1016/j.patrec.2011.09.042

Published: 01 January 2012 Publication History

Abstract

We propose a novel shape descriptor for matching and recognizing 2D object silhouettes. The contour of each object is represented by a fixed number of sample points. For each sample point, a height function is defined based on the distances of the other sample points to its tangent line. One compact and robust shape descriptor is obtained by smoothing the height functions. The proposed descriptor is not only invariant to geometric transformations such as translation, rotation and scaling but also insensitive to nonlinear deformations due to noise and occlusion. In the matching stage, the Dynamic Programming (DP) algorithm is employed to find out the optimal correspondence between sample points of every two shapes. The height function provides an excellent discriminative power, which is demonstrated by excellent retrieval performances on several popular shape benchmarks, including MPEG-7 data set, Kimia's data set and ETH-80 data set.

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Published In

cover image Pattern Recognition Letters

Pattern Recognition Letters Volume 33, Issue 2

January, 2012

125 pages

ISSN:0167-8655

Issue’s Table of Contents

Copyright © Elsevier B.V. © 2011.

Publisher

Elsevier Science Inc.

United States

Publication History

Published: 01 January 2012

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Cited By

Kanimozhi MSudhakar M(2024)Octagonal lattice-based triangulated shape descriptor engaging second-order derivatives supplementing image retrievalJournal of Visual Communication and Image Representation10.1016/j.jvcir.2023.10400598:COnline publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1016/j.jvcir.2023.104005
Sakrani KElghoul SGhorbel F(2024)Optimized multi-scale affine shape registration based on an unsupervised Bayesian classificationMultimedia Tools and Applications10.1007/s11042-023-14890-483:3(7057-7083)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s11042-023-14890-4
Jia QChen XWang YFan XLing HLatecki L(2024)A rotation robust shape transformer for cartoon character recognitionThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-023-03123-240:8(5575-5588)Online publication date: 1-Aug-2024
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Abu ADiamant R(2023)Underwater object classification combining SAS and transferred optical-to-SAS ImageryPattern Recognition10.1016/j.patcog.2023.109868144:COnline publication date: 1-Dec-2023
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Yu XGao YBennamoun MXiong S(2023)A Lie algebra representation for efficient 2D shape classificationPattern Recognition10.1016/j.patcog.2022.109078134:COnline publication date: 1-Feb-2023
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Yang CFang LFei BYu QWei H(2023)Multi-level contour combination features for shape recognitionComputer Vision and Image Understanding10.1016/j.cviu.2023.103650229:COnline publication date: 1-Mar-2023
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Mziou-Sallami MKhalsi RSmati IMhiri SGhorbel F(2023)DeepGCSS: a robust and explainable contour classifier providing generalized curvature scale space featuresNeural Computing and Applications10.1007/s00521-023-08639-135:24(17689-17700)Online publication date: 18-May-2023
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Yang LWang LZhang ZZhuang J(2022)Bag of feature with discriminative module for non-rigid shape retrievalDigital Signal Processing10.1016/j.dsp.2021.103240120:COnline publication date: 3-Jan-2022
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Kumar RMali K(2022)Local Binary Patterns of Segments of a Binary Object for Shape AnalysisJournal of Mathematical Imaging and Vision10.1007/s10851-022-01130-x65:4(618-630)Online publication date: 24-Nov-2022
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