Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
Curvilinear Motion: Rectangular Components01:23

Curvilinear Motion: Rectangular Components

Curvilinear motion characterizes the movement of a particle or object along a curved path, notably evident when envisioning a car navigating a winding road. If the car starts at point A, its position vector is established within a fixed frame of reference, where the ratio of the position vector to its magnitude signifies the unit vector pointing in the position vector's direction.
As the car advances, its position evolves over time. Quantifying the car's velocity involves computing the time...
Linearization and Approximation01:26

Linearization and Approximation

Linearization is a mathematical technique used to approximate complex, nonlinear functions with simpler linear models in the vicinity of a chosen reference point. The method is based on the idea that, although a function may be difficult to evaluate exactly, its behavior near a specific input value can often be closely approximated by the tangent line at that point. This approach is particularly useful when small deviations from a known value are involved.Consider the square root function, for...
Boundary Conditions: Lossless Lines01:21

Boundary Conditions: Lossless Lines

Consider a single-phase, two-wire, lossless transmission line terminated by an impedance at the receiving end and a source with Thevenin voltage and impedance at the sending end. The line, with length, has a surge impedance and wave velocity determined by the line's inductance and capacitance.
At the receiving end, the boundary condition states that the voltage equals the product of the receiving-end impedance and current. This relationship is expressed as a function of the incident and...
Deformations in a Transverse Cross Section01:21

Deformations in a Transverse Cross Section

When a material is subjected to uniaxial stress, it elongates or contracts in the direction of the applied force, and also undergoes changes in the perpendicular directions. This behavior is crucial for understanding how materials behave under stress and is governed by mechanical properties such as Poisson's ratio v, which measures the ratio of transverse strain to axial strain.
As the material stretches, it expands or contracts in orthogonal directions to the load. This phenomenon varies...
Calibration Curves: Correlation Coefficient01:10

Calibration Curves: Correlation Coefficient

In a linear calibration curve, there is a value called the calibration coefficient, denoted by 'r,' which measures the strength and the direction of association between two variables. The correlation coefficient value ranges from −1 to +1. A value of +1 indicates a perfect positive linear correlation, −1 denotes a perfect negative correlation, and 0 implies no correlation between the two variables. A positive correlation value establishes that as one variable increases, the other increases, and...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Plasma Melanin-Concentrating Hormone in Childhood Obesity and Its Association with Metabolic Parameters: A Clinical Observational Study.

Diabetes, metabolic syndrome and obesity : targets and therapy·2026
Same author

Trophic Interactions Are Key to Understanding the Effects of Global Change on the Distribution and Functional Role of the Brown Bear.

Global change biology·2025
Same author

Human Footprint and Forest Disturbance Reduce Space Use of Brown Bears (Ursus arctos) Across Europe.

Global change biology·2025
Same author

Oral Bioavailability Enhancement of Melanin Concentrating Hormone, Development and In Vitro Pharmaceutical Assessment of Novel Delivery Systems.

Pharmaceutics·2022
Same author

Changes of pituitary adenylate cyclase activating polypeptide (PACAP) level in polytrauma patients in the early post-traumatic period.

Peptides·2021
Same author

Investigation of pituitary adenylate cyclase activating polypeptide (PACAP) in human amniotic fluid samples.

Reproductive biology·2020

Related Experiment Video

Updated: May 23, 2026

Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities
07:13

Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities

Published on: October 27, 2023

Nonlinear shape registration without correspondences.

Csaba Domokos1, Jozsef Nemeth, Zoltan Kato

  • 1Department of Image Processing and Computer Graphics, University of Szeged, PO Box 652, H-6701 Szeged, Hungary. dcs@inf.u-szeged.hu

IEEE Transactions on Pattern Analysis and Machine Intelligence
|March 24, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a new framework for shape registration, directly calculating transformation parameters without needing landmark correspondences. This method accurately estimates diffeomorphic deformations, proving robust and easy to implement for various applications.

More Related Videos

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
02:09

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
05:05

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Published on: November 23, 2019

Related Experiment Videos

Last Updated: May 23, 2026

Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities
07:13

Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities

Published on: October 27, 2023

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
02:09

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
05:05

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Published on: November 23, 2019

Area of Science:

  • Medical Image Analysis
  • Computer Vision
  • Computational Geometry

Background:

  • Shape registration is crucial for comparing and analyzing shapes in various scientific fields.
  • Traditional methods rely on establishing point correspondences, which can be challenging and error-prone.
  • Diffeomorphic transformations are essential for accurately modeling large, non-linear deformations.

Purpose of the Study:

  • To propose a novel framework for estimating diffeomorphic transformation parameters.
  • To overcome limitations of classical registration methods that require established correspondences.
  • To provide a robust and generalizable approach for shape alignment.

Main Methods:

  • The framework formulates shape registration as solving a system of nonlinear equations.
  • It directly estimates the parameters of the aligning diffeomorphism.
  • The method is applicable to various transformation models without explicit landmark identification.

Main Results:

  • The proposed method successfully recovers diffeomorphic deformations without requiring established correspondences.
  • It demonstrates robustness against segmentation errors and is insensitive to deformation strength.
  • Performance validated on large synthetic datasets and diverse real-world applications.

Conclusions:

  • The novel framework offers an efficient and robust alternative for shape registration.
  • It simplifies the process by directly computing transformation parameters.
  • The method's versatility and accuracy make it suitable for a wide range of applications.