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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

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Euclidean Distance Approximations From Replacement Product Graphs.

T Arthur Terlep, Mark R Bell, Thomas M Talavage

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

|November 22, 2021

Summary

This summary is machine-generated.

We introduce RE-grid graphs, a novel chamfering method that approximates Euclidean distance in images. These graphs maintain accurate distance contours in noisy data, proving useful for path-finding in low-resolution images.

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Area of Science:

Computer Vision
Image Processing
Computational Geometry

Background:

Chamfering algorithms approximate Euclidean distance in digital images.
Existing methods struggle with noisy data and complex image spaces.
Need for robust approximation techniques when exact solutions are impractical.

Purpose of the Study:

Introduce a new chamfering paradigm using RE-grid graphs.
Demonstrate the effectiveness of RE-grid graphs for path-finding in challenging image spaces.
Explore potential applications in various image processing tasks.

Main Methods:

Developing a novel chamfering approach by creating internal pixel networks (RE-grid graphs).
Building local pixel connections to approximate Euclidean space.
Analyzing the resulting modular global architecture and its topological properties.

Main Results:

RE-grid graphs maintain near-Euclidean polygonal distance contours, even with noisy data.
The method offers a viable approximation for path-finding where exact solutions are difficult.
Demonstrated utility in case studies for high-frequency, low-resolution image spaces.

Conclusions:

RE-grid graphs present a promising advancement in chamfering techniques.
The approach offers robustness in approximating Euclidean distances in digital images.
Potential applications span morphology, segmentation, and neural network design, warranting further investigation.