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

Deconvolution01:20

Deconvolution

186
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...
186
Structural Classification of Joints01:20

Structural Classification of Joints

3.5K
Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
3.5K
Downsampling01:20

Downsampling

183
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
183

You might also read

Related Articles

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

Sort by
Same author

OmniCharacter++: Towards Comprehensive Benchmark for Realistic Role-Playing Agents.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Sharpness-Aware Fine-Tuning for OOD Detection.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Stable-Hair V2: Real-World Hair Transfer via Multiple-View Diffusion Model.

IEEE transactions on visualization and computer graphics·2026
Same author

Vocabulary-Free Image Classification and Semantic Segmentation.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

High-Resolution Open-Vocabulary Object 6D Pose Estimation.

IEEE transactions on pattern analysis and machine intelligence·2025
Same author

A Unified Masked Jigsaw Puzzle Framework for Vision and Language Models.

IEEE transactions on pattern analysis and machine intelligence·2025
Same journal

Relation DETR+: Exploring Explicit Position Relation Prior for Dense Prediction.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

RBF++: Quantifying and Optimizing Reasoning Boundaries across Measurable and Unmeasurable Capabilities for Chain-of-Thought Reasoning.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

CAFE: Cross-View Adaptive Fusion and Cluster Center Enhancement for Robust Multi-View Clustering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

DIVER: Reinforced Diffusion Breaks Imitation Bottlenecks in End-to-End Autonomous Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Ethics-Aware Safe Reinforcement Learning for Rare-Event Risk Control in Interactive Urban Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Learning Shape Anchors for Holistic Indoor Scene Understanding.

IEEE transactions on pattern analysis and machine intelligence·2026
See all related articles

Related Experiment Video

Updated: Jul 17, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

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

Published on: November 30, 2022

2.8K

Compositional Semantic Mix for Domain Adaptation in Point Cloud Segmentation.

Cristiano Saltori, Fabio Galasso, Giuseppe Fiameni

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |August 30, 2023
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces compositional semantic mixing, a novel unsupervised domain adaptation technique for 3D point cloud semantic segmentation. It enhances model generalization across different sensors and environments by mixing data samples.

    More Related Videos

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
    08:25

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

    Published on: May 7, 2019

    9.0K
    From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
    12:08

    From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

    Published on: August 13, 2014

    24.6K

    Related Experiment Videos

    Last Updated: Jul 17, 2025

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

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

    Published on: November 30, 2022

    2.8K
    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
    08:25

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

    Published on: May 7, 2019

    9.0K
    From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
    12:08

    From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

    Published on: August 13, 2014

    24.6K

    Area of Science:

    • Computer Vision
    • Machine Learning
    • 3D Data Processing

    Background:

    • Deep learning models for 3D point cloud semantic segmentation struggle with generalization due to domain shift (differences in sensors or environments).
    • Existing domain adaptation methods often require specific data formats (e.g., range view maps) or multi-modal inputs, limiting their applicability.
    • Image domain adaptation techniques using sample mixing offer a more flexible approach by manipulating input data.

    Purpose of the Study:

    • To introduce the first unsupervised domain adaptation technique for 3D point cloud semantic segmentation based on semantic and geometric sample mixing.
    • To develop a method that improves the generalization capabilities of deep learning models for point cloud segmentation across different domains.
    • To evaluate the effectiveness of the proposed method in both unsupervised and semi-supervised settings.

    Main Methods:

    • Proposed 'compositional semantic mixing' for unsupervised domain adaptation in 3D point cloud semantic segmentation.
    • Introduced a two-branch symmetric network architecture to process source and target domain point clouds concurrently.
    • Integrated data fragments and semantic information (source labels, target pseudo-labels) from both domains within each network branch.
    • Incorporated optional semi-supervised learning using limited human point-level annotations.

    Main Results:

    • The proposed compositional semantic mixing significantly outperforms state-of-the-art methods in unsupervised domain adaptation for point cloud segmentation.
    • The method also demonstrates superior performance in semi-supervised settings, further enhancing accuracy with limited annotations.
    • Evaluations were conducted on LiDAR datasets in both synthetic-to-real and real-to-real domain adaptation scenarios.

    Conclusions:

    • Compositional semantic mixing is an effective unsupervised domain adaptation technique for 3D point cloud semantic segmentation.
    • The two-branch network architecture successfully leverages cross-domain information for improved generalization.
    • The method offers a flexible and powerful solution for adapting models to diverse real-world conditions.