Cognitive spectroscopy for wood species identification: near infrared hyperspectral imaging combined with convolutional neural networks

The Analyst ◽  
2019 ◽  
Vol 144 (21) ◽  
pp. 6438-6446
Author(s):  
Hideaki Kanayama ◽  
Te Ma ◽  
Satoru Tsuchikawa ◽  
Tetsuya Inagaki
Keyword(s):  
Neural Networks ◽  
Species Identification ◽  
Hyperspectral Imaging ◽  
Convolutional Neural Networks ◽  
Wood Species ◽  
Near Infrared ◽  
Wood Properties ◽  
Identification System ◽  
Illegal Logging ◽  
System P

From the viewpoint of combating illegal logging and examining wood properties, there is a contemporary demand for a wood species identification system.

Development of a species identification system of Japanese bats from echolocation calls using convolutional neural networks

2021 ◽  
pp. 101253
Author(s):  
Keigo Kobayashi ◽  
Keisuke Masuda ◽  
Chihiro Haga ◽  
Takanori Matsui ◽  
Dai Fukui ◽  
...  
Keyword(s):  
Neural Networks ◽  
Species Identification ◽  
Convolutional Neural Networks ◽  
Identification System ◽  
Echolocation Calls

scholarly journals Fine-Grained Wood Species Identification Using Convolutional Neural Networks

2019 ◽  
pp. 67-77 ◽  
Author(s):  
Dmitrii Shustrov ◽  
Tuomas Eerola ◽  
Lasse Lensu ◽  
Heikki Kälviäinen ◽  
Heikki Haario
Keyword(s):  
Neural Networks ◽  
Species Identification ◽  
Convolutional Neural Networks ◽  
Wood Species ◽  
Fine Grained

scholarly journals Mosquito species identification using convolutional neural networks with a multitiered ensemble model for novel species detection

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Adam Goodwin ◽  
Sanket Padmanabhan ◽  
Sanchit Hira ◽  
Margaret Glancey ◽  
Monet Slinowsky ◽  
...  
Keyword(s):  
Neural Networks ◽  
Species Identification ◽  
Convolutional Neural Networks ◽  
Novel Species ◽  
Novelty Detection ◽  
Mosquito Species ◽  
Image Database ◽  
Closed Set ◽  
Open Set

AbstractWith over 3500 mosquito species described, accurate species identification of the few implicated in disease transmission is critical to mosquito borne disease mitigation. Yet this task is hindered by limited global taxonomic expertise and specimen damage consistent across common capture methods. Convolutional neural networks (CNNs) are promising with limited sets of species, but image database requirements restrict practical implementation. Using an image database of 2696 specimens from 67 mosquito species, we address the practical open-set problem with a detection algorithm for novel species. Closed-set classification of 16 known species achieved 97.04 ± 0.87% accuracy independently, and 89.07 ± 5.58% when cascaded with novelty detection. Closed-set classification of 39 species produces a macro F1-score of 86.07 ± 1.81%. This demonstrates an accurate, scalable, and practical computer vision solution to identify wild-caught mosquitoes for implementation in biosurveillance and targeted vector control programs, without the need for extensive image database development for each new target region.

scholarly journals Deep convolutional neural networks for classifying head and neck cancer using hyperspectral imaging

2017 ◽  
Vol 22 (6) ◽  
pp. 060503 ◽  
Author(s):  
Martin Halicek ◽  
Guolan Lu ◽  
James V. Little ◽  
Xu Wang ◽  
Mihir Patel ◽  
...  
Keyword(s):  
Neural Networks ◽  
Head And Neck Cancer ◽  
Head And Neck ◽  
Hyperspectral Imaging ◽  
Convolutional Neural Networks ◽  
Neck Cancer ◽  
Deep Convolutional Neural Networks

Hand Gesture Identification System Using Convolutional Neural Networks

2021 ◽  
Author(s):  
Siddhartha Arjaria ◽  
Riya Sahu ◽  
Sejal Agrawal ◽  
Suyash Khare ◽  
Yashi Agarwal ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Hand Gesture ◽  
Identification System

Image-based species identification of wild bees using convolutional neural networks

2020 ◽  
Vol 55 ◽  
pp. 101017 ◽  
Author(s):  
Keanu Buschbacher ◽  
Dirk Ahrens ◽  
Marianne Espeland ◽  
Volker Steinhage
Keyword(s):  
Neural Networks ◽  
Species Identification ◽  
Convolutional Neural Networks ◽  
Wild Bees

Fusion of Low-Illuminance Visible and Near-Infrared Images Based on Convolutional Neural Networks

2020 ◽  
Vol 40 (16) ◽  
pp. 1610001
Author(s):  
唐超影 Tang Chaoying ◽  
浦世亮 Pu Shiliang ◽  
叶鹏钊 Ye Pengzhao ◽  
肖飞 Xiao Fei ◽  
冯华君 Feng Huajun
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Near Infrared ◽  
Infrared Images
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