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Journal ArticleDOI

A novel method for 2D nonrigid partial shape matching

Chengzhuan Yang, +2 more
- 31 Jan 2018 - 
- Vol. 275, pp 1160-1176
TLDR
A novel shape descriptor, triangular centroid distances (TCDs) is proposed, for shape representation; the TCDs shape descriptor is invariant to translation, rotation, scaling, and considerable shape deformations and outperforms existing methods in 2D nonrigid partial shape matching.
About
This article is published in Neurocomputing.The article was published on 2018-01-31. It has received 39 citations till now. The article focuses on the topics: Heat kernel signature & Shape analysis (digital geometry).

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Citations
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Journal ArticleDOI

Multiscale Fourier descriptor based on triangular features for shape retrieval

TL;DR: This work introduces a novel multiscale Fourier descriptor based on triangular features which is used to identify shapes and is far superior to the complex shape description methods in terms of retrieval efficiency and computational complexity.
Journal ArticleDOI

Graph convolutional autoencoder model for the shape coding and cognition of buildings in maps

TL;DR: This study develops a learning strategy to combine multiple features extracted from its boundary and obtain a reasonable shape representation, and shows that the GCAE model can produce a cognitively compliant shape coding, with the ability to distinguish different shapes.
Journal ArticleDOI

Fourier Transform to Group Feature on Generated Coarser Contours for Fast 2D Shape Matching

TL;DR: This work presents the first Fourier descriptor that surpasses the Shape Tree method in terms of both accuracy and speed on this dataset, and shows that MSFDGF-SH is an effective and efficient global shape descriptor.
Journal ArticleDOI

Robust template matching with large angle localization

TL;DR: A robust method for template matching with large angle localization to iteratively search the corresponding patch-features and updating the template location with rotation transformation and a robust objective function is presented to register features based on the Maximum Correntropy Criterion and optimized for the transformation with translation and rotation parameters.
Journal ArticleDOI

Towards Automatic Skeleton Extraction With Skeleton Grafting

TL;DR: An approach is proposed here that generates backbone and dense skeletons by shape input, and then extends the backbone branches via skeleton grafting from the dense skeleton to ensure a well-integrated output.
References
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Journal ArticleDOI

Shape matching and object recognition using shape contexts

TL;DR: This paper presents work on computing shape models that are computationally fast and invariant basic transformations like translation, scaling and rotation, and proposes shape detection using a feature called shape context, which is descriptive of the shape of the object.
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Review of shape representation and description techniques

TL;DR: This paper identifies some promising techniques for image retrieval according to standard principles and examines implementation procedures for each technique and discusses its advantages and disadvantages.
Journal ArticleDOI

Shape Classification Using the Inner-Distance

TL;DR: It is suggested that the inner-distance can be used as a replacement for the Euclidean distance to build more accurate descriptors for complex shapes, especially for those with articulated parts.
Proceedings ArticleDOI

Shape descriptors for non-rigid shapes with a single closed contour

TL;DR: This paper reports on the MPEG-7 Core Experiment CE-Shape, which gave a unique opportunity to compare various shape descriptors for non-rigid shapes with a single closed contour and found that a more theoretical comparison of these descriptors seems to be extremely hard.
Journal ArticleDOI

Recognition of shapes by editing their shock graphs

TL;DR: An edit-distance algorithm for shock graphs that finds the optimal deformation path in polynomial time is employed and gives intuitive correspondences for a variety of shapes and is robust in the presence of a wide range of visual transformations.
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