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

Kendall's Tau Test

Kendall's Tau Test

Kendall's tau test, also known as the Kendall rank coefficient test, is a nonparametric method for assessing association between two variables. This test is particularly useful for identifying significant correlations when the distributions of the sample and population are unknown. Developed in 1938 by the British statistician Sir Maurice George Kendall, the tau coefficient (denoted as τ) serves as a rank correlation coefficient, with values ranging from -1 to +1.
A τ value of +1 indicates...

You might also read

Related Articles

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

Sort by

Same author

A Robust Multi-Branch CNN-LSTM Architecture for Cross-Subject Motor Imagery Classification.

Sensors (Basel, Switzerland)·2026

Same author

Multiscale RGB-Guided Fusion for Hyperspectral Image Super-Resolution.

Journal of imaging·2026

Same author

Enhancing Direction-of-Arrival Estimation with Multi-Task Learning.

Sensors (Basel, Switzerland)·2024

Same author

DALib: A Curated Repository of Libraries for Data Augmentation in Computer Vision.

Journal of imaging·2023

Same author

Estimation of Soil Characteristics from Multispectral Sentinel-3 Imagery and DEM Derivatives Using Machine Learning.

Sensors (Basel, Switzerland)·2023

Same author

Quasi Real-Time Apple Defect Segmentation Using Deep Learning.

Sensors (Basel, Switzerland)·2023

Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026

Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026

Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026

Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026

Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026

Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Nov 18, 2025

Tomato Analyzer: A Useful Software Application to Collect Accurate and Detailed Morphological and Colorimetric Data from Two-dimensional Objects

Tomato Analyzer: A Useful Software Application to Collect Accurate and Detailed Morphological and Colorimetric Data from Two-dimensional Objects

Published on: March 16, 2010

T1K+: A Database for Benchmarking Color Texture Classification and Retrieval Methods.

Claudio Cusano¹, Paolo Napoletano², Raimondo Schettini²

¹Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Via Ferrata 1, 27100 Pavia, Italy.

Sensors (Basel, Switzerland)

|February 5, 2021

Summary

This summary is machine-generated.

We introduce T1K+, a large texture image database with 1129 classes for texture classification research. This resource aids in understanding texture retrieval and classification challenges.

Keywords:

color and Texture color texture databases texture descriptors texture features texture recognition texture retrieval

More Related Videos

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Published on: June 18, 2021

Multimodal Optical Imaging Platform for Studying Cellular Metabolism

Multimodal Optical Imaging Platform for Studying Cellular Metabolism

Published on: June 6, 2025

Related Experiment Videos

Last Updated: Nov 18, 2025

Tomato Analyzer: A Useful Software Application to Collect Accurate and Detailed Morphological and Colorimetric Data from Two-dimensional Objects

Tomato Analyzer: A Useful Software Application to Collect Accurate and Detailed Morphological and Colorimetric Data from Two-dimensional Objects

Published on: March 16, 2010

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Published on: June 18, 2021

Multimodal Optical Imaging Platform for Studying Cellular Metabolism

Multimodal Optical Imaging Platform for Studying Cellular Metabolism

Published on: June 6, 2025

Area of Science:

Computer Vision
Image Analysis
Machine Learning

Background:

Texture classification and retrieval are crucial in image analysis.
Existing datasets may lack the scale and heterogeneity required for robust model training.
Developing comprehensive resources is essential for advancing texture recognition technologies.

Purpose of the Study:

To introduce T1K+, a large-scale, heterogeneous database of high-quality texture images.
To provide a structured resource for studying texture classification and retrieval.
To facilitate research on visual descriptors for texture analysis.

Main Methods:

Compilation of a large, diverse texture image dataset (T1K+) with 1129 classes.
Hierarchical organization of texture classes into 5 thematic and 266 sub-categories.
Evaluation of hand-crafted and learned visual descriptors on supervised texture classification tasks.

Main Results:

T1K+ offers a rich resource for diverse texture analysis experiments.
The hierarchical structure facilitates targeted research and database exploration.
Comparative analysis of descriptor performance provides insights for texture classification.

Conclusions:

T1K+ is a valuable asset for advancing texture classification and retrieval research.
The database supports the development and evaluation of novel texture analysis methods.
Understanding descriptor performance is key to improving texture recognition accuracy.