AutoCNet: A Python library for sparse multi-image correspondence identification for planetary data | Zendy

J. Laura | Zendy; Kelvin Rodriguez | Zendy; A. C. Paquette | Zendy; Evin Dunn | Zendy

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AutoCNet: A Python library for sparse multi-image correspondence identification for planetary data

Author(s) -

J. Laura,

Kelvin Rodriguez,

A. C. Paquette,

Evin Dunn

Publication year - 2018

Publication title -

softwarex

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.528

H-Index - 21

ISSN - 2352-7110

DOI - 10.1016/j.softx.2018.02.001

Subject(s) - python (programming language) , computer science , software , pipeline (software) , artificial intelligence , computer graphics (images) , programming language

In this work we describe the AutoCNet library, written in Python, to support the application of computer vision techniques for n -image correspondence identification in remotely sensed planetary images and subsequent bundle adjustment. The library is designed to support exploratory data analysis, algorithm and processing pipeline development, and application at scale in High Performance Computing (HPC) environments for processing large data sets and generating foundational data products. We also present a brief case study illustrating high level usage for the Apollo 15 Metric camera.

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