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

Extraction: Advanced Methods

Extraction: Advanced Methods

Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is formed in...

Imaging Biological Samples with Optical Microscopy

Imaging Biological Samples with Optical Microscopy

Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...

Deconvolution

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

Decoupling Processing-Morphology-Stability Relationships Enables 19.65% Organic Solar Cells With Exceptional Photostability.

Small (Weinheim an der Bergstrasse, Germany)·2026

Same author

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 author

Core-to-Wing Type Hybrid Dimeric Giant Molecule Acceptors With Different-Length Ester-Linked Alkyl Chains Enable 20.25% Efficiency Organic Solar Cells.

Angewandte Chemie (International ed. in English)·2026

Same author

Structural basis and immunogenic efficacy of porcine circovirus type 3 virus-like particle.

Nature communications·2026

Same author

SD<sup>2</sup>-SNN: Self-distillation and structural decomposition framework for SNNs in continual learning.

Neural networks : the official journal of the International Neural Network Society·2026

Same author

Autophagy in plant male reproduction: conserved machinery, divergent functions.

The New phytologist·2026

Search research articles

Related Experiment Video

Updated: May 27, 2026

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

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

Robust CoHOG feature extraction in human-centered image/video management system.

Yanwei Pang¹, He Yan, Yuan Yuan

¹School of Electronic Information Engineering, Tianjin University, Tianjin 300072, China. pyw@tju.edu.cn

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|November 25, 2011

Summary

This summary is machine-generated.

This study enhances human detection by improving co-occurrence histograms of oriented gradients (CoHOGs). The new method utilizes gradient information more effectively and reduces feature vector size for better performance.

More Related Videos

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster (Nephrops norvegicus)

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster (Nephrops norvegicus)

Published on: April 8, 2019

Related Experiment Videos

Last Updated: May 27, 2026

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

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

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster (Nephrops norvegicus)

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster (Nephrops norvegicus)

Published on: April 8, 2019

Area of Science:

Computer Vision
Machine Learning
Image Processing

Background:

Robust human detection is crucial for image and video management systems.
Co-occurrence Histograms of Oriented Gradients (CoHOGs) are used for feature extraction but have limitations.

Purpose of the Study:

To address shortcomings of CoHOGs: discarded gradient magnitudes, aliasing due to unsmoothed gradients, and high dimensionality.
To propose an improved framework for robust human detection feature extraction.

Main Methods:

A novel gradient decomposition and combination strategy to leverage gradient information.
A two-stage gradient smoothing scheme for efficient interpolation.
Incremental Principal Component Analysis (IPCA) for dimensionality reduction.

Main Results:

The proposed framework effectively utilizes gradient information.
Gradient smoothing mitigates aliasing effects.
IPCA significantly reduces the CoHOG feature vector dimensionality.

Conclusions:

The enhanced CoHOG method improves feature extraction for human detection.
Experimental results on human databases validate the proposed framework's effectiveness.