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Publication

LiftPose3D, a deep learning-based approach for transforming two-dimensional to three-dimensional poses in laboratory animals

Pascal Fua, Pavan P Ramdya, Helge Jochen Rhodin, Semih Günel, Victor Lobato Rios, Adám Gosztolai, Daniel Eduardo Morales Garza
2021
Journal Articles

Abstract

LiftPose3D infers three-dimensional poses from two-dimensional data or from limited three-dimensional data. The approach is illustrated for videos of behaving Drosophila, mice, rats and macaques.

Official source

https://infoscience.epfl.ch/entities/publication/30d59c41-688a-41cd-b993-8cf761764d57

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Pascal Fua, Pavan P Ramdya, Helge Jochen Rhodin, Semih Günel, Victor Lobato Rios, Adám Gosztolai, Daniel Eduardo Morales Garza
2021
Journal Articles

Abstract

LiftPose3D infers three-dimensional poses from two-dimensional data or from limited three-dimensional data. The approach is illustrated for videos of behaving Drosophila, mice, rats and macaques.

Official source

https://infoscience.epfl.ch/entities/publication/30d59c41-688a-41cd-b993-8cf761764d57

About this result

Related concepts (32)

Deep learning

Deep learning is part of a broader family of machine learning methods, which is based on artificial neural networks with representation learning. The adjective "deep" in deep learning refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervised.

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A camera is an optical instrument used to capture and store images or videos, either digitally via an electronic , or chemically via a light-sensitive material such as photographic film. As a pivotal technology in the fields of photography and videography, cameras have played a significant role in the progression of visual arts, media, entertainment, surveillance, and scientific research. The invention of the camera dates back to the 19th century and has since evolved with advancements in technology, leading to a vast array of types and models in the 21st century.

Feedforward neural network

A feedforward neural network (FNN) is one of the two broad types of artificial neural network, characterized by direction of the flow of information between its layers. Its flow is uni-directional, meaning that the information in the model flows in only one direction—forward—from the input nodes, through the hidden nodes (if any) and to the output nodes, without any cycles or loops, in contrast to recurrent neural networks, which have a bi-directional flow.

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