On the public citation graph

Is Rotation a Nuisance in Shape Recognition?

https://doi.org/10.1109/cvpr.2014.528

0 signed · 45 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.

Claim with ORCID

Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.

See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-642-33709-3_36openalex-snapshot

Leafsnap: A Computer Vision System for Automatic Plant Species Identification

inferredcites →https://doi.org/10.1007/s00778-008-0111-4openalex-snapshot

Supporting exact indexing of arbitrarily rotated shapes and periodic time series under Euclidean and warping distance measures

inferredcites →https://doi.org/10.1016/j.patcog.2003.07.008openalex-snapshot

Review of shape representation and description techniques

inferredcites →https://doi.org/10.1016/s0925-2312(97)00036-2openalex-snapshot

The handbook of brain theory and neural networks

inferredcites →https://doi.org/10.1109/34.817410openalex-snapshot

Flexible syntactic matching of curves and its application to automatic hierarchical classification of silhouettes

inferredcites →https://doi.org/10.1109/34.9107openalex-snapshot

Hierarchical chamfer matching: a parametric edge matching algorithm

inferredcites →https://doi.org/10.1109/34.993558openalex-snapshot

Shape matching and object recognition using shape contexts

inferredcites →https://doi.org/10.1109/cvpr.2003.1211497openalex-snapshot

Analyzing appearance and contour based methods for object categorization

inferredcites →https://doi.org/10.1109/iccv.2013.158openalex-snapshot

A Framework for Shape Analysis via Hilbert Space Embedding

inferredcites →https://doi.org/10.1109/tpami.2006.208openalex-snapshot

Integral Invariants for Shape Matching

inferredcites →https://doi.org/10.1109/tpami.2007.41openalex-snapshot

Shape Classification Using the Inner-Distance

inferredcites →https://doi.org/10.1109/tpami.2009.85openalex-snapshot

Learning Context-Sensitive Shape Similarity by Graph Transduction

inferredcites →https://doi.org/10.1115/1.1577356openalex-snapshot

A Survey of Shape Similarity Assessment Algorithms for Product Design and Manufacturing Applications

inferredcites →https://doi.org/10.1515/9780691183978-018openalex-snapshot

On Growth and Form, 1917

inferredcites →https://doi.org/10.5860/choice.33-4484openalex-snapshot

The Handbook of brain theory and neural networks

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/978-3-319-46448-0_30openalex-snapshot

Localizing and Orienting Street Views Using Overhead Imagery

inferred← citeshttps://doi.org/10.1007/s11042-023-14890-4openalex-snapshot

Optimized multi-scale affine shape registration based on an unsupervised Bayesian classification

inferred← citeshttps://doi.org/10.1016/j.amc.2021.126096openalex-snapshot

Invariant multiscale triangle feature for shape recognition

inferred← citeshttps://doi.org/10.1016/j.cmpb.2015.12.014openalex-snapshot

Representation learning for mammography mass lesion classification with convolutional neural networks

inferred← citeshttps://doi.org/10.1016/j.compag.2024.109700openalex-snapshot

Learning a discriminative region descriptor for fine-grained cultivar identification

inferred← citeshttps://doi.org/10.1016/j.cviu.2023.103650openalex-snapshot

Multi-level contour combination features for shape recognition

inferred← citeshttps://doi.org/10.1016/j.engappai.2024.108291openalex-snapshot

Composite descriptor based on contour and appearance for plant species identification

inferred← citeshttps://doi.org/10.1016/j.image.2018.11.004openalex-snapshot

Multiscale Fourier descriptor based on triangular features for shape retrieval

inferred← citeshttps://doi.org/10.1016/j.knosys.2023.110688openalex-snapshot

Deep convolutional feature aggregation for fine-grained cultivar recognition

inferred← citeshttps://doi.org/10.1016/j.patrec.2015.06.022openalex-snapshot

Neural shape codes for 3D model retrieval

inferred← citeshttps://doi.org/10.1109/access.2020.3019800openalex-snapshot

Learning Contour-Based Mid-Level Representation for Shape Classification

inferred← citeshttps://doi.org/10.1109/cvpr.2016.611openalex-snapshot

Learning Weight Uncertainty with Stochastic Gradient MCMC for Shape Classification

inferred← citeshttps://doi.org/10.1109/cvpr.2017.486openalex-snapshot

A New Representation of Skeleton Sequences for 3D Action Recognition

inferred← citeshttps://doi.org/10.1109/cvpr.2019.00083openalex-snapshot

Re-Ranking via Metric Fusion for Object Retrieval and Person Re-Identification

inferred← citeshttps://doi.org/10.1109/embc.2015.7318482openalex-snapshot

Convolutional neural networks for mammography mass lesion classification

inferred← citeshttps://doi.org/10.1109/iccv.2017.90openalex-snapshot

Ensemble Diffusion for Retrieval

inferred← citeshttps://doi.org/10.1109/icip.2017.8296412openalex-snapshot

Multi-glimpse LSTM with color-depth feature fusion for human detection

inferred← citeshttps://doi.org/10.1109/ijcnn.2018.8489318openalex-snapshot

Deep CNNs with Rotational Filters for Rotation Invariant Character Recognition

inferred← citeshttps://doi.org/10.1109/tcbb.2022.3148463openalex-snapshot

A Learning Robust and Discriminative Shape Descriptor for Plant Species Identification

inferred← citeshttps://doi.org/10.1109/tip.2018.2812099openalex-snapshot

Learning Clip Representations for Skeleton-Based 3D Action Recognition

inferred← citeshttps://doi.org/10.1109/tip.2018.2863028openalex-snapshot

Automatic Ensemble Diffusion for 3D Shape and Image Retrieval

inferred← citeshttps://doi.org/10.1109/tmm.2017.2778559openalex-snapshot

Leveraging Structural Context Models and Ranking Score Fusion for Human Interaction Prediction

inferred← citeshttps://doi.org/10.1109/wacv.2019.00038openalex-snapshot

Euclidean Invariant Recognition of 2D Shapes Using Histograms of Magnitudes of Local Fourier-Mellin Descriptors

inferred← citeshttps://doi.org/10.3390/electronics10212708openalex-snapshot

Human Action Recognition of Spatiotemporal Parameters for Skeleton Sequences Using MTLN Feature Learning Framework

inferred← citeshttps://doi.org/10.37126/aige.v2.i3.70openalex-snapshot

Application of convolutional neural network in detecting and classifying gastric cancer

inferred← citeshttps://doi.org/10.37126/aige.v2.i3.71openalex-snapshot

Application of convolutional neural network in detecting and classifying gastric cancer

inferred← citeshttps://doi.org/10.48550/arxiv.1608.00161openalex-snapshot

Localizing and Orienting Street Views Using Overhead Imagery

inferred← citeshttps://doi.org/10.48550/arxiv.2004.13371openalex-snapshot

3D Solid Spherical Bispectrum CNNs for Biomedical Texture Analysis

inferred← citeshttps://doi.org/10.5220/0009161701960204openalex-snapshot

Geometric Deep Learning on Skeleton Sequences for 2D/3D Action Recognition

inferred← citeshttps://doi.org/10.5281/zenodo.21566234openalex-snapshot

A Neoteric Approach Based on Multi Task Learning Network for Skeletal 3D Action Recognition

Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.