Zobrazeno 1 - 10
of 14
pro vyhledávání: '"Chris M. Ward"'
Publikováno v:
OENO One, Vol 57, Iss 4 (2023)
Soft scale insects and mealybugs are phloem-feeding Hemipterans that are considered major pests in agriculture and horticulture throughout the world. However, correct taxonomic identification in the field can be difficult, making it hard for growers
Externí odkaz:
https://doaj.org/article/d341afd87e1d44e9ba856cdc02f8b717
Autor:
Mohammad R. Alam, Chris M. Ward
Publikováno v:
2022 IEEE Applied Imagery Pattern Recognition Workshop (AIPR).
Autor:
Josh Harguess, Chris M. Ward
Publikováno v:
2022 IEEE Applied Imagery Pattern Recognition Workshop (AIPR).
Autor:
Chris M. Ward, Ben Cichy
Publikováno v:
Geospatial Informatics XI.
In recent years, deep neural-networks have gained popularity in maritime detection problems. Successes in deep- learning have been due, partially, to the controlled and constrained nature of the training dataset. However, remote sensing data are high
Publikováno v:
Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III.
In this paper we explore the problem of open set object recognition in maritime domains. In recent years, deep neural-networks have gained popularity in ship detection and classification problems, but there is little related work in applying Out-of-D
Publikováno v:
AIPR
In this paper we introduce new video datasets to investigate the gaps between synthetic and real imagery in object detection and depth estimation. Currently, synthetic image datasets with real-world counterparts largely focus on computer vision appli
Publikováno v:
Geospatial Informatics IX.
Capsule networks have shown promise in their ability to perform classification tasks with viewpoint invariance; outperforming the accuracy of other models in some cases. This capability applies to maritime classification tasks where there is a lack o
Autor:
Shibin Parameswaran, Chelsea Mediavilla, Josh Harguess, Marissa Dotter, Jonathan Sato, Chris M. Ward
Publikováno v:
Geospatial Informatics IX.
Autor:
Keith M. Sullivan, Rick Watkins, Josh Harguess, Chelsea Mediavilla, Cameron Hilton, Chris M. Ward
Publikováno v:
Geospatial Informatics IX.
Traditional metrics for evaluating the efficacy of image processing techniques do not lend themselves to under- standing the capabilities and limitations of modern image processing methods - particularly those enabled by deep learning. When applying
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c3b0230ab32e00c11b75d0cabce169e8
http://arxiv.org/abs/1905.05373
http://arxiv.org/abs/1905.05373