An Efficient Palmprint Identification System Using Multispectral and Hyperspectral Imaging

2013 ◽  
pp. 155-164 ◽  
Author(s):  
Abdallah Meraoumia ◽  
Salim Chitroub ◽  
Ahmed Bouridane
Keyword(s):  
Hyperspectral Imaging ◽  
Identification System ◽  
Palmprint Identification

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.

Massively parallel palmprint identification system using GPU

2017 ◽  
Vol 22 (S3) ◽  
pp. 7201-7216
Author(s):  
Syed Ali Tariq ◽  
Shahzaib Iqbal ◽  
Mubeen Ghafoor ◽  
Imtiaz A. Taj ◽  
Noman M. Jafri ◽  
...  
Keyword(s):  
Massively Parallel ◽  
Identification System ◽  
Palmprint Identification

A Novel On-Line Palmprint Identification System

2003 ◽  
Vol 5 (4) ◽  
pp. 361-373
Author(s):  
Wenxin Li ◽  
David Zhang ◽  
Zhuoqun Xu
Keyword(s):  
Identification System ◽  
Palmprint Identification ◽  
On Line

Minutiae-Based Offline Palmprint Identification System

2009 ◽  
Author(s):  
Zechao Tan ◽  
Jie Yang ◽  
Zifeng Shang ◽  
Guangshun Shi ◽  
Shengjiang Chang
Keyword(s):  
Identification System ◽  
Palmprint Identification

scholarly journals Hyperspectral Imaging Combined with Artificial Intelligence in the Early Detection of Esophageal Cancer

Cancers ◽  
2021 ◽  
Vol 13 (18) ◽  
pp. 4593
Author(s):  
Cho-Lun Tsai ◽  
Arvind Mukundan ◽  
Chen-Shuan Chung ◽  
Yi-Hsun Chen ◽  
Yao-Kuang Wang ◽  
...  
Keyword(s):  
Esophageal Cancer ◽  
Deep Learning ◽  
Hyperspectral Imaging ◽  
Detection Method ◽  
Narrow Band ◽  
Identification System ◽  
Single Shot ◽  
Test Results ◽  
Endoscopic Images ◽  
Rgb Images

This study uses hyperspectral imaging (HSI) and a deep learning diagnosis model that can identify the stage of esophageal cancer and mark the locations. This model simulates the spectrum data from the image using an algorithm developed in this study which is combined with deep learning for the classification and diagnosis of esophageal cancer using a single-shot multibox detector (SSD)-based identification system. Some 155 white-light endoscopic images and 153 narrow-band endoscopic images of esophageal cancer were used to evaluate the prediction model. The algorithm took 19 s to predict the results of 308 test images and the accuracy of the test results of the WLI and NBI esophageal cancer was 88 and 91%, respectively, when using the spectral data. Compared with RGB images, the accuracy of the WLI was 83% and the NBI was 86%. In this study, the accuracy of the WLI and NBI was increased by 5%, confirming that the prediction accuracy of the HSI detection method is significantly improved.

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