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Related Concept Videos

Outliers and Influential Points01:08

Outliers and Influential Points

An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the vertical...
What Are Outliers?01:12

What Are Outliers?

Outliers are observed data points that are far from the least squares line. They have unusual values and need to be examined carefully. Though an outlier may result from erroneous data, at other times, it may hold valuable information about the population under study and should be included in the data. Hence, it is crucial to examine what causes a data point to be an outlier.
The z score is used to find outliers or unusual values. It should be noted that any values beyond -2 and +2 are...
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This number is...
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
Modified Boxplots00:57

Modified Boxplots

A standard box and whisker plot informs us about the spread of the data in a given sample. One can identify the minimum value, maximum value, first quartile value, second quartile or median value, and third quartile.
However, the box plot does not tell the reader about outliers - values that lie far from the center of the data. We can modify the standard box and whisker plot to identify the outliers and visualize the actual spread of the data in a sample.
Initially, we calculate the adjusted...
Structural Classification of Joints01:20

Structural Classification of Joints

Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...

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Related Experiment Video

Updated: May 27, 2026

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
12:08

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

Simultaneously fitting and segmenting multiple-structure data with outliers.

Hanzi Wang1, Tat-Jun Chin, David Suter

  • 1School of Information Science and Technology, Xiamen University, Fujian, 361005, China. hanzi.wang@ieee.org

IEEE Transactions on Pattern Analysis and Machine Intelligence
|November 9, 2011
PubMed
Summary
This summary is machine-generated.

We introduce Adaptive Kernel-Scale Weighted Hypotheses (AKSWH), a robust framework for segmenting data with many outliers. It features a novel Iterative Kth Ordered Scale Estimator (IKOSE) for accurate inlier scale estimation.

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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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Related Experiment Videos

Last Updated: May 27, 2026

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
12:08

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Area of Science:

  • Computer Vision
  • Machine Learning
  • Data Analysis

Background:

  • Robust fitting is crucial for data segmentation, especially with outliers.
  • Existing methods struggle with heavily corrupted multiple-structure data.
  • Accurate scale estimation is vital for reliable model fitting.

Purpose of the Study:

  • To propose a robust fitting framework, Adaptive Kernel-Scale Weighted Hypotheses (AKSWH), for segmenting multiple-structure data.
  • To introduce a novel scale estimator, Iterative Kth Ordered Scale Estimator (IKOSE), for accurate inlier scale estimation in corrupted data.
  • To enhance hypothesis weighting, clustering, and fusing for improved segmentation.

Main Methods:

  • Developed the Adaptive Kernel-Scale Weighted Hypotheses (AKSWH) framework.
  • Introduced the Iterative Kth Ordered Scale Estimator (IKOSE) for robust scale estimation.
  • Integrated novel techniques for weighting, clustering, and fusing hypotheses.

Main Results:

  • AKSWH accurately estimates the number of model instances, parameters, and scale simultaneously.
  • Demonstrated strong performance in line fitting, circle fitting, and range image segmentation.
  • Showcased effectiveness in homography estimation and two-view motion segmentation with synthetic and real data.

Conclusions:

  • AKSWH provides a robust and accurate solution for segmenting multiple-structure data with outliers.
  • The IKOSE scale estimator is a valuable standalone component for other robust estimation tasks.
  • The framework's versatility is proven across various computer vision applications.