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pro vyhledávání: '"Theisen, Nick"'
Semantic segmentation is an essential step for many vision applications in order to understand a scene and the objects within. Recent progress in hyperspectral imaging technology enables the application in driving scenarios and the hope is that the d
Externí odkaz:
http://arxiv.org/abs/2409.11205
One-shot action recognition allows the recognition of human-performed actions with only a single training example. This can influence human-robot-interaction positively by enabling the robot to react to previously unseen behaviour. We formulate the o
Externí odkaz:
http://arxiv.org/abs/2012.13823
Recognizing an activity with a single reference sample using metric learning approaches is a promising research field. The majority of few-shot methods focus on object recognition or face-identification. We propose a metric learning approach to reduc
Externí odkaz:
http://arxiv.org/abs/2004.11085
We present a simple, yet effective and flexible method for action recognition supporting multiple sensor modalities. Multivariate signal sequences are encoded in an image and are then classified using a recently proposed EfficientNet CNN architecture
Externí odkaz:
http://arxiv.org/abs/2003.06156
In this paper we present an approach for learning to imitate human behavior on a semantic level by markerless visual observation. We analyze a set of spatial constraints on human pose data extracted using convolutional pose machines and object inform
Externí odkaz:
http://arxiv.org/abs/1807.11541
Akademický článek
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Autor:
Theisen, Nickolas Dana
Field experiments were conducted in 2017 and 2018 at the NDSU Horticulture Research Farm near Absaraka, ND to evaluate the growth and yield characteristics of twelve commercial hop cultivars in response to varied training densities. Cultivars were tr
Externí odkaz:
https://hdl.handle.net/10365/32264