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

Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

7.9K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
7.9K
Sampling Distribution01:12

Sampling Distribution

16.3K
Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
16.3K
Bandpass Sampling01:17

Bandpass Sampling

407
In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2....
407
Sampling Methods: Overview01:06

Sampling Methods: Overview

1.3K
A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
1.3K
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

578
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
578
Downsampling01:20

Downsampling

508
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
508

You might also read

Related Articles

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

Sort by
Same author

Evaluating the utility of ChatGPT in addressing conceptual and non-conceptual questions related to urodynamic quality control and trace analysis.

Scientific reports·2025
Same author

LDPC Code-Based Distributed Source Coding With an Efficient Message Passing Mechanism for the Compression of Correlated Image Sources.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2024
Same author

Screening for early rheumatoid arthritis using high-frequency ultrasound, serum RANKL, and OPG detection.

Clinical rheumatology·2023
Same author

An iron(III) complex-based supramolecular organic framework (SOF) as a theranostic platform <i>via</i> magnetic resonance imaging-guided chemotherapy.

Journal of materials chemistry. B·2023
Same author

Attention-Guided Collaborative Counting.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2022
Same author

Optimally filtering and matching processing for regional upstrokes to improve ultrasound transit time-based local PWV estimation.

Computer methods and programs in biomedicine·2022

Related Experiment Video

Updated: Dec 12, 2025

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

9.7K

Background Noise Filtering and Distribution Dividing for Crowd Counting.

Hong Mo, Wenqi Ren, Yuan Xiong

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |August 8, 2020
    PubMed
    Summary

    This study introduces a novel head size estimation method for crowd counting, improving accuracy by considering global people distribution and using head masks for background filtering. The approach enhances robustness against scale variations and noise.

    More Related Videos

    Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images
    11:49

    Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

    Published on: February 2, 2019

    9.7K
    High Density Event-related Potential Data Acquisition in Cognitive Neuroscience
    08:33

    High Density Event-related Potential Data Acquisition in Cognitive Neuroscience

    Published on: April 16, 2010

    12.9K

    Related Experiment Videos

    Last Updated: Dec 12, 2025

    Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
    11:54

    Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

    Published on: March 13, 2017

    9.7K
    Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images
    11:49

    Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

    Published on: February 2, 2019

    9.7K
    High Density Event-related Potential Data Acquisition in Cognitive Neuroscience
    08:33

    High Density Event-related Potential Data Acquisition in Cognitive Neuroscience

    Published on: April 16, 2010

    12.9K

    Area of Science:

    • Computer Vision
    • Artificial Intelligence

    Background:

    • Crowd counting faces challenges from varied crowd distributions and background noise.
    • Existing methods often rely solely on local head proximity, limiting performance.

    Purpose of the Study:

    • To develop a robust crowd counting approach by improving head size estimation.
    • To mitigate the impact of diverse crowd scales and background interference.

    Main Methods:

    • Proposes a head size estimation technique incorporating global people distribution.
    • Utilizes dummy head points to ensure uninterrupted head size propagation from far to near regions.
    • Generates high-fidelity head masks and explores three mask usage mechanisms for background filtering.
    • Develops two competitive crowd counting models based on learned masks.

    Main Results:

    • The proposed method demonstrates improved robustness against background noise and diverse crowd scales.
    • Evaluated on ShanghaiTech, UCFQNRF, and UCFCC_50 datasets, achieving favorable results.
    • Outperforms existing state-of-the-art crowd counting approaches.

    Conclusions:

    • The novel head size estimation and mask-based filtering significantly enhance crowd counting performance.
    • The approach offers a more reliable solution for crowd density analysis in complex scenarios.