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Related Concept Videos

Cumulative Frequency Distribution01:04

Cumulative Frequency Distribution

A cumulative frequency distribution is another type of frequency distribution. Instead of reporting how many data values fall in some classes, it reports how many data values are contained in either that class or any class to its left. Technically, it means the sum of frequencies of the class and all the classes below it in a frequency distribution. A cumulative frequency is calculated by adding the frequency of each class lower than the corresponding class interval or category. In general, a...
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...
Relative Frequency Distribution00:55

Relative Frequency Distribution

A relative frequency distribution is the proportion or fraction of times a value occurs in a data set. To find the relative frequencies, one can divide each frequency by the total number of data points in the sample. It is very similar to a regular frequency distribution, except that instead of reporting how many data values fall in a class, a relative frequency distribution reports the fraction of data values that fall in a class. These fractions or proportions are called relative frequencies...
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear.
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal assumptions,...

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Linear models of cumulative distribution function for content-based medical image retrieval.

K N Manjunath1, A Renuka, U C Niranjan

  • 1Department of Computer Science & Engineering, Manipal Institute of Technology, Manipal University, Manipal, India. Knmanjunath08@rediffmail.com

Journal of Medical Systems
|November 29, 2007
PubMed
Summary

This study introduces a novel image matching technique using Cumulative Distribution Functions (CDF) for faster retrieval. The method approximates CDFs with linear models, significantly reducing image feature dimensions and improving matching speed.

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Area of Science:

  • Computer Science
  • Image Processing
  • Pattern Recognition

Background:

  • Efficient image retrieval is crucial in large databases.
  • Existing image matching techniques can be computationally intensive, leading to long retrieval times.

Purpose of the Study:

  • To propose a novel image matching technique for reduced retrieval time.
  • To introduce and evaluate bit plane histogram and hierarchical bit plane histogram approaches.
  • To compare the proposed Cumulative Distribution Function (CDF) based technique with existing methods.

Main Methods:

  • Approximating the CDF of query and database images using piecewise linear models.
  • Utilizing slope and intercept parameters at various grayscale intervals for CDF approximation.
  • Comparing images based on estimated slopes and intercepts of their CDFs.
  • Employing contiguous lines to represent CDFs for efficient comparison.

Main Results:

  • The proposed CDF-based image matching technique offers a considerable reduction in retrieval time.
  • Approximation of CDFs with lines reduces feature dimensions, enhancing matching speed.
  • The method allows for the comparison of images with different sizes due to the dynamic range of CDF (0 to 1).

Conclusions:

  • The CDF-based image matching technique provides a significant improvement in retrieval speed.
  • The use of piecewise linear models for CDF approximation is effective for image matching.
  • This approach offers a scalable and efficient solution for large-scale image retrieval.