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

Convolution Properties II

Convolution Properties II

The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...

Convolution Properties I

Convolution Properties I

Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:

Convolution: Math, Graphics, and Discrete Signals

Convolution: Math, Graphics, and Discrete Signals

In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...

Deconvolution

Deconvolution

Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...

Three-Dimensional Force System:Problem Solving

Three-Dimensional Force System:Problem Solving

A three-dimensional force system refers to a scenario in which three forces act simultaneously in three different directions. This type of problem is commonly encountered in physics and engineering, where it is necessary to calculate the resultant force on the system, which can then be used to predict or analyze the behavior of the object or structure under consideration.
To solve a three-dimensional force system, first resolve each force into its respective scalar components. Do this using...

Depth Perception and Spatial Vision

Depth Perception and Spatial Vision

Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.

You might also read

Related Articles

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

Sort by

Same author

AttSec: protein secondary structure prediction by capturing local patterns from attention map.

BMC bioinformatics·2023

Same author

Self-Parameter Distillation Dehazing.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2023

Same author

Pixel-Wise Wasserstein Autoencoder for Highly Generative Dehazing.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2021

Same author

SphereGAN: Sphere Generative Adversarial Network Based on Geometric Moment Matching and its Applications.

IEEE transactions on pattern analysis and machine intelligence·2020

Same author

Adaptive Visual Tracking with Minimum Uncertainty Gap Estimation.

IEEE transactions on pattern analysis and machine intelligence·2016

Same author

A Unified Framework for Event Summarization and Rare Event Detection from Multiple Views.

IEEE transactions on pattern analysis and machine intelligence·2015

Search research articles

Related Experiment Video

Updated: Aug 4, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Hierarchical Bidirected Graph Convolutions for Large-Scale 3-D Point Cloud Place Recognition.

Dong Wook Shu, Junseok Kwon

IEEE Transactions on Neural Networks and Learning Systems

|April 6, 2023

Summary

This study introduces a novel hierarchical bidirected graph convolution network (HiBi-GCN) for 3-D point cloud place recognition. The method effectively extracts discriminative features from 3-D scenes, improving robustness in real-world environments.

More Related Videos

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

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells

Published on: September 28, 2019

Related Experiment Videos

Last Updated: Aug 4, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 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

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells

Published on: September 28, 2019

Area of Science:

Computer Vision
Robotics
Artificial Intelligence

Background:

3-D point cloud data offers robustness for place recognition compared to 2-D images.
Extracting informative features from 3-D point clouds presents challenges due to difficulties in defining convolution operations.

Purpose of the Study:

To propose a novel hierarchical bidirected graph convolution network (HiBi-GCN) for large-scale 3-D point cloud place recognition.
To address the challenge of feature extraction in 3-D point cloud data.

Main Methods:

Introduced a new hierarchical kernel defined as a hierarchical graph structure via unsupervised clustering.
Employed pooling edges to aggregate hierarchical graphs from fine to coarse.
Utilized fusing edges to combine pooled graphs from coarse to fine.

Main Results:

The HiBi-GCN method learns representative features hierarchically and probabilistically.
The approach extracts discriminative and informative global descriptors for place recognition.
Experimental results confirm the suitability of the hierarchical graph structure for 3-D scene representation.

Conclusions:

The proposed hierarchical graph structure is well-suited for point cloud-based place recognition.
HiBi-GCN enhances the ability to represent and recognize real-world 3-D scenes effectively.