Kim Bjerge

Aarhus University

Denmark

Research Interests

Explore related searches

Professors in Computer Vision

Contact this professor

Get a free first email built on one of their papers, ready for you to edit and send yourself.

Articles (16)

Year:

2025

Year:

2025

Year:

2025

Year:

2024

Year:

2024

Year:

2024

Year:

2023

Year:

2023

Year:

2023

Year:

2022

Year:

2022

Year:

2022

Year:

2022

Year:

2021

Year:

2021

Year:

2021

Collaborators (19)

Maxim Larrivée

Head of section

Insectarium de Montréal

CANADA

View details

Toke T. Hoye

Professor

Aarhus University

DENMARK

View details

Christopher Clements

University of Bristol

UNITED KINGDOM

View details

Claus Melvad

Aarhus University

DENMARK

View details

Christian Fischer Pedersen

Associate Professor

Aarhus University

DENMARK

View details

Quentin Geissmann

Aarhus University

DENMARK

View details

Grum Gebreyesus Teklewold

Aarhus University

DENMARK

View details

Henrik Karstoft

Aarhus University

DENMARK

View details

Jeffrey Kerby

Aarhus University

DENMARK

View details

Lise Lotte Sørensen

Aarhus University

DENMARK

View details

Mikhail Mastepanov

Aarhus University

DENMARK

View details

Mads Dyrmann

Aarhus University

DENMARK

View details

Tenna Riis

Aarhus University

DENMARK

View details

Torben R. Christensen

Aarhus University

DENMARK

View details

Anders Krogh Mortensen

Aarhus University

DENMARK

View details

Marianne Bruus

Aarhus University

DENMARK

View details

Jamie Alison

Aarhus University

DENMARK

View details

Marc Mazerolle

Professor

Laval University

CANADA

View details

Tommaso Jucker

University of Bristol

UNITED KINGDOM

View details

ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts before you send them.

Kim Bjerge

Research Interests

Explore related searches

Contact this professor

Articles (16)

Prediction of black soldier fly larval sex and morphological traits using computer vision and deep learning

Towards edge processing of images from insect camera traps

ScannerVision: Scanner-based image acquisition of medically important arthropods for the development of computer vision and deep learning models

Computer vision and deep learning in insects for food and feed production: A review

Towards a standardized framework for AI-assisted, image-based monitoring of nocturnal insects

A deep learning pipeline for time-lapse camera monitoring of insects and their floral environments

Hierarchical classification of insects with multitask learning and anomaly detection

Accurate detection and identification of insects from camera trap images with deep learning

Object Detection of Small Insects in Time-Lapse Camera Recordings

Towards the fully automated monitoring of ecological communities

A mobile observatory powered by sun and wind for near real time measurements of atmospheric, glacial, terrestrial, limnic and coastal oceanic conditions in remote off-grid areas

Accurate image-based identification of macroinvertebrate specimens using deep learning — How much training data is needed?

Real‐time insect tracking and monitoring with computer vision and deep learning

A scalable and efficient convolutional neural network accelerator using HLS for a system-on-chip design

Camera Assisted Roadside Monitoring for Invasive Alien Plant Species Using Deep Learning

Vibration signatures in ball bearings as a function of lubricant viscosity ratio κ, under alternating lubrication conditions

Collaborators (19)

Maxim Larrivée

Toke T. Hoye

Christopher Clements

Claus Melvad

Christian Fischer Pedersen

Quentin Geissmann

Grum Gebreyesus Teklewold

Henrik Karstoft

Jeffrey Kerby

Lise Lotte Sørensen

Mikhail Mastepanov

Mads Dyrmann

Tenna Riis

Torben R. Christensen

Anders Krogh Mortensen

Marianne Bruus

Jamie Alison

Marc Mazerolle

Tommaso Jucker

Make your next move.