Basic Shape Classification Using Spatially Normalised Fourier Shape Signature

2013 ◽  
pp. 435-445 ◽  
Author(s):  
Chin Yeow Wong ◽  
Stephen Ching-Feng Lin ◽  
Guannan Jiang ◽  
Ngai Ming Kwok
Keyword(s):  
Shape Classification ◽  
Shape Signature

Human face shape classification method based on active shape model

2009 ◽  
Vol 29 (10) ◽  
pp. 2710-2712 ◽  
Author(s):  
Li-qiang DU ◽  
Peng JIA ◽  
Zong-tan ZHOU ◽  
De-wen HU
Keyword(s):  
Classification Method ◽  
Active Shape Model ◽  
Shape Classification ◽  
Human Face ◽  
Shape Model ◽  
Face Shape ◽  
Active Shape

scholarly journals The shape classification of Torus-like and (n-sphere)-like continua

1974 ◽  
Vol 4 (1) ◽  
pp. 85-94 ◽  
Author(s):  
Carl Eberhart ◽  
G.R. Gordh ◽  
John Mack
Keyword(s):  
Shape Classification

scholarly journals Automated Three-Dimensional Detection and Shape Classification of Dendritic Spines from Fluorescence Microscopy Images

PLoS ONE ◽  
2008 ◽  
Vol 3 (4) ◽  
pp. e1997 ◽  
Author(s):  
Alfredo Rodriguez ◽  
Douglas B. Ehlenberger ◽  
Dara L. Dickstein ◽  
Patrick R. Hof ◽  
Susan L. Wearne
Keyword(s):  
Fluorescence Microscopy ◽  
Dendritic Spines ◽  
Three Dimensional ◽  
Shape Classification ◽  
Microscopy Images

Point Transformer for Shape Classification and Retrieval of Urban Roof Point Clouds

2021 ◽  
pp. 1-5
Author(s):  
Dimple A. Shajahan ◽  
Mukund Varma T. ◽  
Ramanathan Muthuganapathy
Keyword(s):  
Point Clouds ◽  
Shape Classification

Survey and Evaluation of Neural 3D Shape Classification Approaches

2021 ◽  
pp. 1-1
Author(s):  
Martin Mirbauer ◽  
Miroslav Krabec ◽  
Jaroslav Krivanek ◽  
Elena Sikudova
Keyword(s):  
Shape Classification ◽  
3D Shape ◽  
Survey And Evaluation

3D-MetaConNet: Meta-learning for 3D Shape Classification and Segmentation

2021 ◽  
Author(s):  
Hao Huang ◽  
Xiang Li ◽  
Lingjing Wang ◽  
Yi Fang
Keyword(s):  
Shape Classification ◽  
3D Shape ◽  
Meta Learning

Image processing and machine learning for telehealth craniosynostosis screening in newborns

2021 ◽  
pp. 1-8
Author(s):  
Markus J. Bookland ◽  
Edward S. Ahn ◽  
Petronella Stoltz ◽  
Jonathan E. Martin
Keyword(s):  
Image Processing ◽  
Intraclass Correlation ◽  
Correlation Coefficients ◽  
Asymmetry Index ◽  
Optical Measurements ◽  
Shape Classification ◽  
Data Set ◽  
Intraclass Correlation Coefficients ◽  
Optical Scanner ◽  
Cranial Shape

OBJECTIVE The authors sought to evaluate the accuracy of a novel telehealth-compatible diagnostic software system for identifying craniosynostosis within a newborn (< 1 year old) population. Agreement with gold standard craniometric diagnostics was also assessed. METHODS Cranial shape classification software accuracy was compared to that of blinded craniofacial specialists using a data set of open-source (n = 40) and retrospectively collected newborn orthogonal top-down cranial images, with or without additional facial views (n = 339), culled between April 1, 2008, and February 29, 2020. Based on image quality, midface visibility, and visibility of the cranial equator, 351 image sets were deemed acceptable. Accuracy, sensitivity, and specificity were calculated for the software versus specialist classification. Software agreement with optical craniometrics was assessed with intraclass correlation coefficients. RESULTS The cranial shape classification software had an accuracy of 93.3% (95% CI 86.8–98.8; p < 0.001), with a sensitivity of 92.0% and specificity of 94.3%. Intraclass correlation coefficients for measurements of the cephalic index and cranial vault asymmetry index compared to optical measurements were 0.95 (95% CI 0.84–0.98; p < 0.001) and 0.67 (95% CI 0.24–0.88; p = 0.003), respectively. CONCLUSIONS These results support the use of image processing–based neonatal cranial deformity classification software for remote screening of nonsyndromic craniosynostosis in a newborn population and as a substitute for optical scanner– or CT-based craniometrics. This work has implications that suggest the potential for the development of software for a mobile platform that would allow for screening by telemedicine or in a primary care setting.

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