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Related Experiment Video

Updated: May 29, 2026

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

A Discrete Version of Green's Theorem.

¹MEMBER, IEEE, Department of Electrical Engineering, State University of New York at Buffalo, Buffalo, NY; Department of Information Engineering, National Taiwan University, Taipei.

IEEE Transactions on Pattern Analysis and Machine Intelligence

|August 27, 2011

Summary

This summary is machine-generated.

We developed a discrete Green theorem for computational efficiency. This new method simplifies calculations in image processing and material science by evaluating area sums using boundary sums.

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Last Updated: May 29, 2026

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

Area of Science:

Computational Mathematics
Image Processing
Materials Science

Background:

Traditional Green's theorem relates line integrals to double integrals.
Discrete methods are crucial for digital computations in science and engineering.

Purpose of the Study:

To formulate a discrete version of Green's theorem.
To demonstrate its computational advantages in image processing and material analysis.

Main Methods:

Developed a discrete Green theorem for summations over discrete regions and boundaries.
Applied the theorem to standard image processing tasks.
Utilized the theorem for shape analysis of iron(III) oxide (Fe2O3) particle aggregates.

Main Results:

The discrete Green theorem allows area summations via boundary summations.
Significant computational gains were observed in several applications.
Effective analysis of Fe2O3 particle aggregate shapes was achieved.

Conclusions:

The discrete Green theorem offers a computationally efficient alternative for specific problems.
This method has practical applications in image analysis and the characterization of particulate materials.