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Galloway et al., 2022 - Google Patents

Predicting dreissenid mussel abundance in nearshore waters using underwater imagery and deep learning

Galloway et al., 2022

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Document ID
6225632385938306023
Author
Galloway A
Brunet D
Valipour R
McCusker M
Biberhofer J
Sobol M
Moussa M
Taylor G
Publication year
Publication venue
Limnology and Oceanography: Methods

External Links

Snippet

Accurate and cost‐effective dreissenid mussel abundance maps are vital to assess their ecological roles in aquatic systems. A deep neural network (DNN) modeling framework using semantic segmentation was developed to automatically assess the abundance …
Continue reading at aslopubs.onlinelibrary.wiley.com (PDF) (other versions)

Classifications

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    • G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46—Extraction of features or characteristics of the image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
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    • G06T2207/10032—Satellite or aerial image; Remote sensing
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