Real-Time Segmentation Method of Lightweight Network For Finger Vein Using Embedded Terminal Technique

Zeng, Junying; Zhu, Boyuan; Huang, Yu-Jie; Qin, Chuanbo; Zhu, Jiajian; Wang, Fan; Zhai, Yikui; Gan, Junying; Chen, Yucong; Wang, Yingbo; Labati, Ruggero Donida; Piuri, Vincenzo; Scotti, Fabio

doi:10.1109/access.2020.3046108

Cited by 8 publications

(2 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Note that the decoder receives input in many different ways, such as encoder, skip connection or data after data transmission via bridge network (or bottle neck). Using these fundamental changes, the decoder variations can be categorized into 16 different type listed as follows: (D1) convolution with dropout [70,76,86,95,101,102,134,138]; (D2) UNet++ type of change [130,144,154]; (D3) UNet+++ (UNet 3+) Full scale deep supervision [157]; (D4) Output from decoders to make a loss function [104,140]; (D5) fusion of the decoder outputs for scale adjustment [59,107]; (D6) recurrent residual [118,129,138]; (D7) residual block [75,84,88,105,138,150]; (D8) channel attention and scale attention block [65,113]; (D9) transpose convolution [66,88,94,95,139]; (D10) squeeze excitation (SE) Network [103,125]; (D11) cascade convolution [99]; (D12) addition of original image to each layer [100]; (D13) batch normalization [95,106,155]; (D14) inception block [97]; (D15) dense layer [87,91,…”

Section: B Decoder Variationsmentioning

confidence: 99%

UNet Deep Learning Architecture for Segmentation of Vascular and Non-Vascular Images: A Microscopic Look at UNet Components Buffered With Pruning, Explainable Artificial Intelligence, and Bias

Suri¹,

Bhagawati

Agarwal³

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Background & Motivation: Biomedical image segmentation (BIS) task is challenging due to the variations in organ types, position, shape, size, scale, orientation, and image contrast. Conventional methods lack accurate and automated designs. Artificial intelligence (AI)-based UNet has recently dominated BIS. This is the first review of its kind that microscopically addressed UNet types by complexity, stratification of UNet by its components, addressing UNet in vascular vs. non-vascular framework, the key to segmentation challenge vs. UNet-based architecture, and finally interfacing the three facets of AI, the pruning, the explainable AI (XAI), and the AI-bias. Methods: PRISMA was used to select 267 UNet-based studies. Five classes were identified and labeled as conventional UNet, superior UNet, attention-channel UNet, hybrid UNet, and ensemble UNet. We discovered 81 variations of UNet by considering six kinds of components, namely encoder, decoder, skip connection, bridge network, loss function, and their combination. Vascular vs. non-vascular UNet architecture was compared. AP(ai)Bias 2.0-UNet was identified in these UNet classes based on (i) attributes of UNet architecture and its performance, (ii) explainable AI (XAI), and, (iii) pruning (compression). Five bias methods such as (i) ranking, (ii) radial, (iii) regional area, (iv) PROBAST, and (v) ROBINS-I were applied and compared using a Venn diagram. Findings and Conclusions: Vascular and non-vascular UNet systems dominated with sUNet classes with attention. The majority of the studies suffered from low interest in XAI and pruning strategies. None of the UNet models qualified to be bias-free. There is a need to move from paper-to-practice paradigms for clinical evaluation and settings. INDEX TERMS Image segmentation; vascular; non-vascular; UNet classes; UNet variations; UNetcomponents; explainable AI; pruning; bias.

show abstract

Section: B Decoder Variationsmentioning

confidence: 99%

UNet Deep Learning Architecture for Segmentation of Vascular and Non-Vascular Images: A Microscopic Look at UNet Components Buffered With Pruning, Explainable Artificial Intelligence, and Bias

Suri¹,

Bhagawati

Agarwal³

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…www.irjmets.com @International Research Journal of Modernization in Engineering, Technology and Science[21] …”

mentioning

confidence: 99%

Soft Biometric Trait on Fingervein Recognition using CNNRESNET

2023

IRJMETS

View full text Add to dashboard Cite

Numerous algorithms for finger vein feature extraction demonstrate commendable performance by emphasizing texture characteristics, yet some overlook the intensity distribution of the finger tissue, treating it as background noise in certain instances. Use this kind of noise as a novel soft biometric feature in this project to achieve better output in finger vein recognition. To begin, a thorough examination of the finger vein imaging theory and image characteristics is presented, showcasing the potential extraction of the intensity distribution generated by the finger tissue background for identification as a soft biometric feature. Subsequently, this study introduces two algorithms for extracting the background layer of finger vein patterns and three algorithms for extracting soft biometric traits from the intensity distribution. In the classification stage developed a system with implementation of convolution neural network specifically RESNET18 for the training image dataset and image retrieving process is done. Purpose of introducing deep learning in developing finger vein identification system is to get accurate more performance and speedy results. These are computed on the basis Euclidean distance between features obtained from test image and features of trained images, the model designed has good robustness in illumination and rotation.

show abstract

Contrastive Learning-Based Finger-Vein Recognition with Automatic Adversarial Augmentation

Deng,

Luo,

Qin

et al. 2024

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

View full text Add to dashboard Cite

Real-Time Segmentation Method of Lightweight Network For Finger Vein Using Embedded Terminal Technique

Cited by 8 publications

References 29 publications

UNet Deep Learning Architecture for Segmentation of Vascular and Non-Vascular Images: A Microscopic Look at UNet Components Buffered With Pruning, Explainable Artificial Intelligence, and Bias

UNet Deep Learning Architecture for Segmentation of Vascular and Non-Vascular Images: A Microscopic Look at UNet Components Buffered With Pruning, Explainable Artificial Intelligence, and Bias

Soft Biometric Trait on Fingervein Recognition using CNNRESNET

Contrastive Learning-Based Finger-Vein Recognition with Automatic Adversarial Augmentation

Contact Info

Product

Resources

About