On Qualitative Shape Inferences: a journey from geometry to topology

08/19/2020
by   Steven W. Zucker, et al.
6

Shape inference is classically ill-posed, because it involves a map from the (2D) image domain to the (3D) world. Standard approaches regularize this problem by either assuming a prior on lighting and rendering or restricting the domain, and develop differential equations or optimization solutions. While elegant, the solutions that emerge in these situations are remarkably fragile. We exploit the observation that people infer shape qualitatively; that there are quantitative differences between individuals. The consequence is a topological approach based on critical contours and the Morse-Smale complex. This paper provides a developmental review of that theory, emphasizing the motivation at different stages of the research.

READ FULL TEXT

page 1

page 4

page 8

page 14

page 19

page 20

page 21

page 22

research
05/20/2017

Critical Contours: An Invariant Linking Image Flow with Salient Surface Organization

We exploit a key result from visual psychophysics -- that individuals pe...
research
08/19/2023

A Theory of Topological Derivatives for Inverse Rendering of Geometry

We introduce a theoretical framework for differentiable surface evolutio...
research
05/16/2020

From Boundaries to Bumps: when closed (extremal) contours are critical

Invariants underlying shape inference are elusive: a variety of shapes c...
research
10/15/2022

Min max method, shape, topological derivatives, averaged Lagrangian, homogenization, two scale convergence, Helmholtz equation

In this paper, we perform a rigourous version of shape and topological d...
research
06/23/2023

Shape optimization of optical microscale inclusions

This paper describes a class of shape optimization problems for optical ...
research
07/12/2023

Machine learning and Topological data analysis identify unique features of human papillae in 3D scans

The tongue surface houses a range of papillae that are integral to the m...
research
10/13/2020

Motif Learning in Knowledge Graphs Using Trajectories Of Differential Equations

Knowledge Graph Embeddings (KGEs) have shown promising performance on li...

Please sign up or login with your details

Forgot password? Click here to reset