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Histogram

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...

Probability Histograms

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.

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Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

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KERNEL-BASED HIGH-DIMENSIONAL HISTOGRAM ESTIMATION FOR VISUAL TRACKING.

Peter Karasev¹, James Malcolm, Allen Tannenbaum

¹School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia.

Proceedings. International Conference on Image Processing

|May 14, 2013

Summary

This summary is machine-generated.

This study introduces a novel non-rigid tracking method using distribution parameters for efficient object representation. The approach offers comparable accuracy to histograms but with reduced complexity and improved robustness against noise and lighting variations.

Keywords:

Object tracking kernel density estimation mean-shift region covariance

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Area of Science:

Computer Vision
Machine Learning
Image Processing

Background:

Object tracking is crucial in computer vision.
Traditional methods like histograms face challenges with high dimensionality and complexity.
Robustness to noise and illumination changes remains a key issue.

Purpose of the Study:

To develop an efficient non-rigid object tracking approach.
To represent objects using a reduced set of distribution parameters.
To enhance tracking performance compared to existing methods.

Main Methods:

Object representation using a set of distribution parameters (e.g., mixed moments).
Comparison with joint histogram representations.
Evaluation of robustness against noise and illumination variations.
Extension to mixture models.

Main Results:

The proposed method achieves a significantly reduced representation size compared to histograms.
Discriminating power is comparable to high-dimensional histograms.
The approach demonstrates robustness to noise and illumination changes.
Outperforms full color mean-shift and global covariance searches in experiments.

Conclusions:

The proposed distribution parameter-based tracking method is efficient and effective.
It offers a favorable trade-off between representation size, computational complexity, and performance.
The method provides a robust solution for non-rigid object tracking.