Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Shape and Texture of Coarse Aggregate

Shape and Texture of Coarse Aggregate

Aggregate shape is classified based on the relative sharpness or roundness of the edges and corners. This classification includes categories like rounded, angular, elongated, and flaky, each with specific characteristics. Rounded aggregates, fully shaped by attrition, are typical of river or seashore gravel, while angular aggregates, such as crushed rock, have well-defined edges. Aggregates that are elongated and flaky are less desirable, as they can reduce the workability and strength of...

Second Derivatives and the Shape of a Graph

Second Derivatives and the Shape of a Graph

The second derivative of a function provides essential information about a graph's curvature and how it changes over an interval. It helps determine whether a function is concave upward or concave downward and identifies points where the curvature changes. These properties are fundamental in analyzing real-world scenarios, such as changes in road elevation, population growth, and economic trends.A function f(x) is considered concave upward on an interval if its graph lies above all its tangent...

You might also read

Related Articles

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

Sort by

Same author

FGO-SLAM++: Real-time Geometry-Aware Gaussian SLAM with Continuous Opacity Field.

IEEE transactions on visualization and computer graphics·2026

Same author

Total-Body Dynamic PET/CT Imaging of Proton-Induced Activity and Biologic Washout After Proton Therapy.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine·2026

Same author

Unlocking the Quality Potential of Liberoid Coffee: Advances in Composition, Processing, and Microbial Fermentation.

Comprehensive reviews in food science and food safety·2026

Same author

Prognostic value of the triglyceride-glucose (TyG) index for renal function progression in patients with CKD stages 3-4.

Frontiers in nutrition·2026

Same author

Non-Arrhenius threshold switching by field-driven dipolar ordering.

Nature communications·2026

Same author

Vapochromism and Enhanced Yellow Emission of CuI Under Ammonia Vapor.

Luminescence : the journal of biological and chemical luminescence·2026

Search research articles

Related Experiment Video

Updated: Mar 16, 2026

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

DeepShape: Deep-Learned Shape Descriptor for 3D Shape Retrieval.

Jin Xie, Guoxian Dai, Fan Zhu

IEEE Transactions on Pattern Analysis and Machine Intelligence

|August 3, 2016

Summary

This summary is machine-generated.

This study introduces a new deep learning method for 3D shape retrieval. It effectively extracts deformation-invariant features, improving 3D shape matching accuracy for complex models.

More Related Videos

Author Spotlight: Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans

Author Spotlight: Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans

Published on: September 8, 2023

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Related Experiment Videos

Last Updated: Mar 16, 2026

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

Author Spotlight: Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans

Author Spotlight: Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans

Published on: September 8, 2023

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Area of Science:

Computer Vision
Machine Learning
3D Geometry

Background:

3D shape matching and retrieval face challenges due to complex geometric variations.
Existing methods struggle with deformation-invariant feature extraction.

Purpose of the Study:

To develop a novel 3D shape feature learning method.
To achieve high-level shape features that are insensitive to geometric deformations.
To enhance the accuracy of 3D shape retrieval.

Main Methods:

Utilized a discriminative deep auto-encoder for learning deformation-invariant features.
Input features derived from multiscale shape distributions.
Incorporated the Fisher discrimination criterion into the auto-encoder's hidden layer.
Concatenated features from multiple scales to form the final shape descriptor.

Main Results:

The proposed method demonstrated effectiveness on benchmark datasets with significant geometric variations.
Achieved improved performance in 3D shape retrieval tasks.
Successfully extracted robust, high-level shape features.

Conclusions:

The deep discriminative auto-encoder approach is effective for learning deformation-invariant 3D shape features.
The method significantly enhances 3D shape retrieval capabilities.
Validated through extensive experiments on diverse 3D model datasets.