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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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A topological solution to object segmentation and tracking.

Thomas Tsao¹, Doris Y Tsao^2,3

¹OpticArray Technologies, Rockville, MD 20850.

Proceedings of the National Academy of Sciences of the United States of America

|October 6, 2022

Summary

This summary is machine-generated.

Objects can be segmented and tracked without machine learning. This study uses the mathematical structure of light rays to create a persistent surface representation, solving visual perception challenges.

Keywords:

binding problem segmentation surface representation symbolic representation tracking

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Area of Science:

Computer Vision
Computational Neuroscience
Mathematical Modeling

Background:

Visual perception involves segmenting objects and tracking their identities despite appearance changes.
Current advanced methods for object segmentation and tracking rely on machine learning.

Purpose of the Study:

To investigate if object segmentation and tracking can be achieved without machine learning.
To explore the potential of using the mathematical structure of light rays for visual perception tasks.

Main Methods:

Generating a persistent surface representation from continuous visual input based on the mathematical structure of light rays.
Applying this representation to solve object segmentation and tracking problems.

Main Results:

Demonstrated a method to segment visual input into discrete units representing persistent surfaces.
Showcased invariant tracking of objects in cluttered synthetic video with severe appearance changes.
Achieved segmentation and tracking without any learning component.

Conclusions:

The mathematical structure of light rays provides a natural representation for persistent surfaces.
This surface representation offers a viable solution for both object segmentation and tracking without requiring machine learning.
The approach successfully addresses fundamental challenges in visual perception.