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

Survival Tree01:19

Survival Tree

453
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
453
Structural Classification of Joints01:20

Structural Classification of Joints

8.1K
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...
8.1K

You might also read

Related Articles

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

Sort by
Same author

Learning Compact Semantic Information and Reliable Pseudo-Labels for Incomplete Multi-View Multi-Label Classification.

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

Adaptive Fine-Grained Fusion Network for Multimodal UAV Object Detection.

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

High-Confident Block Diagonal Analysis for Multi-View Palmprint Recognition in Unrestrained Environment.

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

A Cosine Network for Image Super-Resolution.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2025
Same author

ALNet: towards real-time and accurate maize row detection via anchor-line network.

Frontiers in plant science·2025
Same author

Exploiting adversarial style for generalized and robust weed segmentation in rice paddy field.

Frontiers in plant science·2025
Same journal

Through the Looking Glass: A Dual Perspective on Weakly-Supervised Few-Shot Segmentation.

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

Mask-guided Asymmetric Contrastive and Semantic Alignment for Unsupervised Person Re-Identification.

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

Hyperbolic Cycle Alignment for Infrared-Visible Image Fusion.

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

Learning Gaze Synthesizer via 3D-eye Controlled Diffusion and Cross-domain Feature Alignment.

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

Underlying Semantic Diffusion for Effective and Efficient In-Context Learning.

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

DiffRES: Unleashing Text-to-Image Diffusion Models for Generative Referring Expression Segmentation without Information Leakage.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
See all related articles

Related Experiment Video

Updated: Mar 8, 2026

Using Computer Vision Libraries to Streamline Nuclei Quantification
06:25

Using Computer Vision Libraries to Streamline Nuclei Quantification

Published on: June 6, 2025

734

Tree-Structured Nuclear Norm Approximation with Applications to Robust Face Recognition.

Lei Luo, Liang Chen, Jian Yang

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |January 24, 2017
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel tree-structured nuclear norm approximation (TSNA) model for computer vision tasks. TSNA effectively combines structured sparsity and nuclear norm properties, outperforming existing methods in face reconstruction and recognition.

    More Related Videos

    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
    12:27

    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

    Published on: February 15, 2017

    7.4K
    Deep Neural Networks for Image-Based Dietary Assessment
    13:19

    Deep Neural Networks for Image-Based Dietary Assessment

    Published on: March 13, 2021

    10.1K

    Related Experiment Videos

    Last Updated: Mar 8, 2026

    Using Computer Vision Libraries to Streamline Nuclei Quantification
    06:25

    Using Computer Vision Libraries to Streamline Nuclei Quantification

    Published on: June 6, 2025

    734
    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
    12:27

    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

    Published on: February 15, 2017

    7.4K
    Deep Neural Networks for Image-Based Dietary Assessment
    13:19

    Deep Neural Networks for Image-Based Dietary Assessment

    Published on: March 13, 2021

    10.1K

    Area of Science:

    • Computer Vision
    • Pattern Recognition
    • Machine Learning

    Background:

    • Structured sparsity excels with correlated variables but traditional group norms struggle with internal group structure.
    • Nuclear norm captures global matrix structure but neglects local details.
    • Existing methods like sparse representation based classifier (SRC) have limitations.

    Purpose of the Study:

    • To develop a novel model, tree-structured nuclear norm approximation (TSNA), that integrates structured sparsity and nuclear norm benefits.
    • To address the limitations of existing methods in characterizing group structures and local/global matrix properties.
    • To provide a robust framework for representation learning with complex data structures.

    Main Methods:

    • Proposed a tree-structured nuclear norm approximation (TSNA) model.
    • Assumed representation residual with tree-structured prior follows a dependent matrix distribution.
    • Employed the Extended Alternating Direction Method of Multipliers (EADMM) for model optimization.
    • Derived an efficient bound condition using extended restricted isometry constants for theoretical guarantees.

    Main Results:

    • Demonstrated the exact recovery capability of the TSNA model under noisy conditions.
    • Showcased TSNA's ability to characterize both local and global structures effectively.
    • Established TSNA as a unifying framework encompassing methods like SRC, NL1R, and NMR.

    Conclusions:

    • The proposed TSNA model offers superior performance in tasks involving structured data compared to existing methods.
    • TSNA provides a powerful and flexible approach for representation learning, particularly in computer vision.
    • The theoretical guarantees and experimental results validate the effectiveness of the TSNA model.