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

Color Vision01:24

Color Vision

Color perception begins in the retina, the light-sensitive layer at the back of the eye. Two main theories explain how colors are seen: the trichromatic theory and the opponent-process theory. The trichromatic theory, proposed by Thomas Young in 1802 and extended by Hermann von Helmholtz in 1852, suggests that color vision is based on three types of cone receptors in the retina. These cones are sensitive to different but overlapping ranges of wavelengths corresponding to red, blue, and green.
Association Areas of the Cortex01:21

Association Areas of the Cortex

Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
Prefrontal Association Area: This area is located in the frontal lobe and is involved in planning, decision-making, and moderating social behavior. It connects with primary motor areas,...
IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the C=O, C=N, and C=C occur between 1600–1850 cm−1.
The...
Force Classification01:22

Force Classification

Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...

You might also read

Related Articles

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

Sort by
Same author

Transplantation of NEP1-40 and NT-3 Gene-Co-Transduced Neural Stem Cells Improves Function and Neurogenesis after Spinal Cord Injury in a Rat Model.

Neurology India·2022
Same author

[Symptomatic disc pseudocyst after percutaneous endoscopic discectomy of lumbar disc herniation:5 cases report and literature progress].

Zhongguo gu shang = China journal of orthopaedics and traumatology·2022
Same author

Clinical algorithm for preventing missed diagnoses of occult cervical spine instability after acute trauma: A case report.

World journal of clinical cases·2021
Same author

Predictive ability of pharyngeal inlet angle for the occurrence of postoperative dysphagia after occipitocervical fusion.

BMC musculoskeletal disorders·2021
Same author

Loss of Correction After Removal of Spinal Implants in Congenital Scoliosis.

World neurosurgery·2020
Same author

Predictive abilities of O-C2a and O-EAa for the development of postoperative dysphagia in patients undergoing occipitocervical fusion.

The spine journal : official journal of the North American Spine Society·2019
Same journal

Style-Aware Contrastive Test-Time Adaptation: A Dual-Cache Model for Robust Vision-Language Alignment.

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

Semantic Frame Interpolation.

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

Physics-Guided Cross-Modal Decoupling with Test-Time Adaptation for Hyperspectral Image Restoration.

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

Change-Prior-Guided Unsupervised Change Detection of Heterogeneous Remote Sensing Images.

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

AgonicDreamer: Enhancing Multi-View Consistency in Text-to-3D Generation via Rectified Score Distillation.

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

BiCM-Prompt: Bidirectional Cross-Modal Prompt Tuning for Class-Incremental Learning on Multisource Remote Sensing Images.

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

Related Experiment Videos

Tensor discriminant color space for face recognition.

Su-Jing Wang1, Jian Yang, Na Zhang

  • 1College of Computer Science and Technology, Jilin University, Changchun 130012, China. wangsj08@mails.jlu.edu.cn

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|March 2, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a new Tensor Discriminant Color Space (TDCS) model for improved face recognition. TDCS enhances performance and efficiency by preserving spatial color image structures.

Related Experiment Videos

Area of Science:

  • Computer Vision
  • Machine Learning
  • Image Processing

Background:

  • Color information is crucial for effective face recognition.
  • Selecting an optimal color space is vital for diverse visual tasks.
  • Existing methods lack efficiency in complex face recognition scenarios.

Purpose of the Study:

  • To develop a novel color space model for face recognition.
  • To address the challenge of finding a suitable color space for this specific task.
  • To improve both the accuracy and computational efficiency of face recognition systems.

Main Methods:

  • Representing color images as third-order tensors.
  • Introducing the Tensor Discriminant Color Space (TDCS) model.
  • Utilizing n-mode scatter matrices for iterative optimization.
  • Deriving color space transformation and discriminant projection matrices.

Main Results:

  • The TDCS model effectively preserves the spatial structure of color images.
  • Experimental results demonstrate superior performance and efficiency compared to existing models.
  • The proposed method shows significant advantages on complex face databases with pose variations.

Conclusions:

  • The TDCS model offers a robust approach for color-based face recognition.
  • This method provides a significant advancement over current state-of-the-art techniques.
  • TDCS is particularly effective in challenging face recognition applications.