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Orthogonal Trajectories

Orthogonal Trajectories

Orthogonal trajectories describe the geometric relationship between two families of curves that intersect each other at right angles. One illustrative case involves a family of parabolas that open sideways along the x-axis. These curves share a common shape but differ by a scaling parameter, resulting in a set of curves that all pass through the origin and widen at different rates.Determining Orthogonal TrajectoriesTo identify the orthogonal trajectories for these parabolas, the first step...

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Related Experiment Video

Updated: Mar 1, 2026

A Protocol for Real-time 3D Single Particle Tracking

A Protocol for Real-time 3D Single Particle Tracking

Published on: January 3, 2018

Locator-Checker-Scaler Object Tracking Using Spatially Ordered and Weighted Patch Descriptor.

Han-Ul Kim, Chang-Su Kim

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|May 26, 2017

Summary

This summary is machine-generated.

This study introduces a new object descriptor and tracking algorithm for accurate object tracking. The method uses weighted patches and a locator-checker-scaler system for robust performance on benchmarks.

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Area of Science:

Computer Vision
Machine Learning

Background:

Object tracking is crucial for various applications.
Existing methods struggle with background clutter and scale variations.

Purpose of the Study:

To propose a novel object descriptor and tracking algorithm.
To achieve accurate and robust object tracking.

Main Methods:

Developed a Spatially Ordered and Weighted Patch (SOWP) descriptor using color and gradient histograms with foreground weighting via random walk with restart (RWR).
Introduced a Locator, Checker, and Scaler (LCS) tracking algorithm incorporating the SOWP descriptor.

Main Results:

The SOWP descriptor effectively reduces background interference.
The LCS tracker demonstrated robust performance in estimating target location and scale.
Experimental results show excellent performance on recent benchmarks.

Conclusions:

The proposed SOWP descriptor and LCS tracker offer an effective solution for object tracking.
The method provides accurate and robust tracking capabilities.