Literature DB >> 21659124

A generic geometric transformation that unifies a wide range of natural and abstract shapes.

Johan Gielis1.   

Abstract

To study forms in plants and other living organisms, several mathematical tools are available, most of which are general tools that do not take into account valuable biological information. In this report I present a new geometrical approach for modeling and understanding various abstract, natural, and man-made shapes. Starting from the concept of the circle, I show that a large variety of shapes can be described by a single and simple geometrical equation, the Superformula. Modification of the parameters permits the generation of various natural polygons. For example, applying the equation to logarithmic or trigonometric functions modifies the metrics of these functions and all associated graphs. As a unifying framework, all these shapes are proven to be circles in their internal metrics, and the Superformula provides the precise mathematical relation between Euclidean measurements and the internal non-Euclidean metrics of shapes. Looking beyond Euclidean circles and Pythagorean measures reveals a novel and powerful way to study natural forms and phenomena.

Entities:  

Year:  2003        PMID: 21659124     DOI: 10.3732/ajb.90.3.333

Source DB:  PubMed          Journal:  Am J Bot        ISSN: 0002-9122            Impact factor:   3.844


  31 in total

1.  OPTIMAL PARAMETER MAP ESTIMATION FOR SHAPE REPRESENTATION: A GENERATIVE APPROACH.

Authors:  Shireen Y Elhabian; Praful Agrawal; Ross T Whitaker
Journal:  Proc IEEE Int Symp Biomed Imaging       Date:  2016-06-16

2.  ShapeCut: Bayesian surface estimation using shape-driven graph.

Authors:  Gopalkrishna Veni; Shireen Y Elhabian; Ross T Whitaker
Journal:  Med Image Anal       Date:  2017-04-29       Impact factor: 8.545

3.  Volumetric analysis of MRI data monitoring the treatment of polycystic kidney disease in a mouse model.

Authors:  Stathis Hadjidemetriou; Wilfried Reichardt; Juergen Hennig; Martin Buechert; Dominik von Elverfeldt
Journal:  MAGMA       Date:  2011-01-07       Impact factor: 2.310

4.  Uncertain-DeepSSM: From Images to Probabilistic Shape Models.

Authors:  Jadie Adams; Riddhish Bhalodia; Shireen Elhabian
Journal:  Shape Med Imaging (2020)       Date:  2020-10-03

5.  Investigating the Shape of the Shoot Apical Meristem in Bamboo Using a Superellipse Equation.

Authors:  Qiang Wei; Peijian Shi
Journal:  Bio Protoc       Date:  2017-12-05

6.  Analysis of a deep learning-based method for generation of SPECT projections based on a large Monte Carlo simulated dataset.

Authors:  Julian Leube; Johan Gustafsson; Michael Lassmann; Maikol Salas-Ramirez; Johannes Tran-Gia
Journal:  EJNMMI Phys       Date:  2022-07-19

7.  Complex Shapes Are Bluish, Darker, and More Saturated; Shape-Color Correspondence in 3D Object Perception.

Authors:  Jiwon Song; Haeji Shin; Minsun Park; Seungmin Nam; Chai-Youn Kim
Journal:  Front Psychol       Date:  2022-05-04

8.  Aesthetic preference recognition of 3D shapes using EEG.

Authors:  Lin Hou Chew; Jason Teo; James Mountstephens
Journal:  Cogn Neurodyn       Date:  2015-11-04       Impact factor: 5.082

9.  Object Representations in Human Visual Cortex Formed Through Temporal Integration of Dynamic Partial Shape Views.

Authors:  Tanya Orlov; Ehud Zohary
Journal:  J Neurosci       Date:  2017-12-01       Impact factor: 6.167

10.  Universal natural shapes: from unifying shape description to simple methods for shape analysis and boundary value problems.

Authors:  Johan Gielis; Diego Caratelli; Yohan Fougerolle; Paolo Emilio Ricci; Ilia Tavkelidze; Tom Gerats
Journal:  PLoS One       Date:  2012-09-27       Impact factor: 3.240

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.