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

In-situ Hybridization02:31

In-situ Hybridization

In situ hybridization (ISH) is a technique used to detect and localize specific DNA or RNA molecules in cells, tissue, or tissue sections using a labeled probe. The technique was first used in 1969 for the investigation of nucleic acids. It is currently an essential tool in scientific research and clinical settings, especially for diagnostic purposes.
Types of probes and labels
A probe is a complementary strand of DNA or RNA that binds to corresponding nucleotide sequences in a cell. Many...
Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been developed.
Photoreceptors and Visual Pathways01:22

Photoreceptors and Visual Pathways

At the molecular level, visual signals trigger transformations in photopigment molecules, resulting in changes in the photoreceptor cell's membrane potential. The photon's energy level is denoted by its wavelength, with each specific wavelength of visible light associated with a distinct color. The spectral range of visible light, classified as electromagnetic radiation, spans from 380 to 720 nm. Electromagnetic radiation wavelengths exceeding 720 nm fall under the infrared category, whereas...
Deconvolution01:20

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...

You might also read

Related Articles

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

Sort by
Same author

Sestrin-2: a new biomarker for type 2 Diabetes Mellitus and Insulin resistance.

Journal of diabetes and metabolic disorders·2026
Same author

Sestrin-2 and metabolic syndrome: Biochemical insights from a cross-sectional study.

Journal of biomedical research·2026
Same author

KDTViT knowledge distillation, transfer learning and transformer based deep learning framework for efficient histopathology image classification.

Scientific reports·2026
Same author

From Compression to Closure: Efficacy and Safety of Vascular Closure Devices (VCD) Versus Manual Compression: A Comparative Analysis of Hemostatic Strategies of Obtura, Angioseal, and ProGlide VCD.

Journal of the Saudi Heart Association·2026
Same author

Classification of epileptic seizure using hybrid deep learning framework with time and time-frequency Hjorth features.

Computer methods in biomechanics and biomedical engineering·2026
Same author

Machine learning approach to gait analysis for Parkinson's disease detection and severity classification.

Frontiers in robotics and AI·2025

Related Experiment Video

Updated: Jun 30, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

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

Published on: December 15, 2023

1.2K

Enhanced content-based image retrieval via hybrid color, texture, and deep learning features.

Surbhi Tyagi1, Praveen Shukla2, Partap Singh1

  • 1Quantum University, Roorkee, Uttarakhand, India.

Scientific Reports
|March 25, 2026
PubMed
Summary

CTD-Net, a novel Content-Based Image Retrieval system, enhances image search by combining color, texture, and deep learning features. This hybrid approach significantly improves retrieval precision across multiple datasets.

Keywords:
CBIRDeep learningDigital inclusionEfficientNetHandcrafted featuresHealth services accessibilitySimilarity measurement

Related Experiment Videos

Last Updated: Jun 30, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

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

Published on: December 15, 2023

1.2K

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Image Processing

Background:

  • Traditional Content-Based Image Retrieval (CBIR) systems face limitations in capturing complex visual and semantic information.
  • Existing methods often rely solely on either handcrafted or deep learning features, hindering comprehensive image analysis.

Purpose of the Study:

  • To introduce CTD-Net (Color, Texture, and Deep Learning- Network), a novel CBIR system.
  • To enhance image retrieval performance by integrating diverse feature types.

Main Methods:

  • CTD-Net fuses handcrafted features (Color Histogram, Color Moments, Local Binary Patterns, Wavelet Transform) with deep features (EfficientNet-B7).
  • This hybrid approach bridges the gap between low-level visual attributes and high-level semantic understanding.

Main Results:

  • CTD-Net achieved high precision rates: 98.85% on Corel-1K, 92.40% on Corel-10K, and 88.94% on Caltech-101.
  • The system significantly outperformed existing CBIR methods in experimental evaluations.

Conclusions:

  • Hybrid feature fusion in CTD-Net is highly effective for CBIR.
  • The proposed approach demonstrates significant potential for advancing the field of Content-Based Image Retrieval.