S1.3 Computer Vision

28 Sep 2018
10:30 - 12:15
Lecture Hall 5

S1.3 Computer Vision

S1.3 _ Computer vision in environmental sciences. (Session Chairs: Shinji Fukuda and Jeffrey Tuhtan)

Image-based methods are at the forefront of artificial intelligence applications. This special session provides a forum for researchers and professionals using image-based methods to study species, population, biodiversity, and the abiotic environment.

The topics of this special session include:
-UAV imagery
-GIS/orthoimagery
-Video tracking/motion estimation
-Object recognition and classification
-High speed imaging
-Multispectral remote sensing

Keywords: image analysis, machine learning, artificial intelligence, classification/regression

Talk 01-07

NAIRA a tool to automatic mammals genera identification in Camera Trapping Pictures
Claudia Isaza, Luis Pulido, Angelica Diaz-Pulido

Assessment of permanent grasslands in Latvia using spectral remote sensing techniques
Dainis Jakovels, Agris Brauns, Jevgenijs Filipovs, Juris Taskovs and Ruta Abaja

Exploiting Taxonomic Relations in Image-based Plant Species Classification
Marco Seeland, David Boho, and Patrick Mäder

Computer vision applications using multispectral UAS imagery: comparing pixel and object-based methods for automatic classification of river landscapes
Jeffrey A. Tuhtan, Philipp Thumser, Christian Haas

Tracking swimming Lefua echigonia to assess the impact of crayfish introduction
Shinji Fukuda and Jeffrey Tuhtan

Trends in machine learning for plant species identification
Jana Wäldchen, Michael Rzanny, Marco Seeland and Patrick Mäder

Deep Learning for Cracking the Leaf Code
Dimitri Belousow, Georg Graser, Marco Seeland and Patrick Mäder