An End-to-End Learning Approach for Subpixel Feature Extraction

Xiaojian Xu; Jeffrey A. Fessler; Marc Klasky; G S Sidharth; G S Sidharth; Jennifer L. Schei; Michael Thompson McCann

doi:10.1364/3D.2023.JW2A.1

Optica Imaging Congress (3D, COSI, DH, FLatOptics, IS, pcAOP)
Technical Digest Series (Optica Publishing Group, 2023),
paper JW2A.1
•https://doi.org/10.1364/3D.2023.JW2A.1

An End-to-End Learning Approach for Subpixel Feature Extraction

Xiaojian Xu, Jeffrey A. Fessler, Marc Klasky, G S Sidharth, Jennifer L. Schei, and Michael Thompson McCann

3D Image Acquisition and Display: Technology, Perception and Applications 2023

Boston, Massachusetts United States
14–17 August 2023
ISBN: 978-1-957171-28-9

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Joint Poster Session II (JW2A)

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X. Xu, J. A. Fessler, M. Klasky, G. S. Sidharth, J. L. Schei, and M. T. McCann, "An End-to-End Learning Approach for Subpixel Feature Extraction," in Optica Imaging Congress (3D, COSI, DH, FLatOptics, IS, pcAOP), Technical Digest Series (Optica Publishing Group, 2023), paper JW2A.1.

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Abstract

We propose a feature extraction method based on Fourier image encoding and a multiscale convolutional neural network, training end-to-end. Our experiments show the method can localize subpixel feature locations from density fields to subpixel accuracy.

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