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Related Concept Videos

Histogram01:05

Histogram

The histogram is a graphical representation in the x-y form of data distribution in a data set. The horizontal x-axis is labeled with what the data represents (for instance, distance from your home to school). The vertical y-axis is labeled either frequency or relative frequency (or percent frequency or probability).
A histogram graph consists of contiguous (adjoining) boxes. The heights of the bars correspond to frequency values. The graph will have the same shape with respective labels. The...
Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an organic...
Relative Frequency Histogram01:14

Relative Frequency Histogram

The relative frequency depicts the proportion of data points that have each value. The frequency tells the number of data points that have each value. Like the histogram, a relative frequency histogram also has the same shape with a horizontal scale (the x-axis), but the vertical scale (the y-axis) is marked with relative frequencies (percentages of the whole) instead of actual frequencies. A relative frequency histogram is a graphical representation of a frequency distribution where the...
Probability Histograms01:17

Probability Histograms

A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
Distance Measurements by Taping01:18

Distance Measurements by Taping

Tapes are essential in surveying for accurate, durable, and short-distance measurements. Made from lightweight, nylon-coated steel, they offer flexibility and strength for rugged outdoor use. The nylon coating protects against rust and wear, extending the tape's life. Standard lengths, around 30 meters, are marked in meters and millimeters for precision.Surveyors select tapes based on site conditions and accuracy needs. Lightweight, nylon-coated tapes are commonly used for ease of handling and...
Cluster Sampling Method01:20

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...

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Related Experiment Video

Updated: May 8, 2026

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery
05:12

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery

Published on: August 12, 2021

Joint histogram-based cost aggregation for stereo matching.

Dongbo Min1, Jiangbo Lu, Minh N Do

  • 1Advanced Digital Science Center, 1 Fusionopolis Way, Singapore. dongbo@adsc.com.sg

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 24, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a novel histogram-based method for efficient cost aggregation in stereo matching. The approach significantly reduces computational complexity while maintaining high-quality disparity map generation.

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A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
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A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

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Related Experiment Videos

Last Updated: May 8, 2026

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery
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Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery

Published on: August 12, 2021

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
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A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

Area of Science:

  • Computer Vision
  • Image Processing
  • Artificial Intelligence

Background:

  • Stereo matching is crucial for 3D reconstruction.
  • Efficient cost aggregation is a key challenge in stereo matching algorithms.
  • Existing methods often struggle with computational complexity.

Purpose of the Study:

  • To develop a novel and efficient cost aggregation method for stereo matching.
  • To significantly reduce the computational complexity of stereo matching.
  • To investigate the trade-off between accuracy and complexity.

Main Methods:

  • Reformulating the cost aggregation problem using a histogram perspective.
  • Reducing computational redundancy across the search range by avoiding repeated filtering.
  • Implementing an efficient sampling scheme within the matching window for complexity reduction.

Main Results:

  • The proposed method achieves significant reductions in computational complexity.
  • High-quality disparity maps are generated with the novel approach.
  • The method outperforms existing local stereo matching techniques in terms of efficiency and quality.

Conclusions:

  • The histogram-based approach offers a promising direction for complexity-constrained stereo matching.
  • The method provides a good balance between accuracy and computational cost.
  • New insights into designing efficient stereo matching algorithms are presented.