Filters








98 Hits in 2.8 sec

Fingerprint Distortion Rectification using Deep Convolutional Neural Networks [article]

Ali Dabouei, Hadi Kazemi, Seyed Mehdi Iranmanesh, Jeremi Dawson, Nasser M. Nasrabadi
2018 arXiv   pre-print
In this paper, we develop a rectification model based on a Deep Convolutional Neural Network (DCNN) to accurately estimate distortion parameters from the input image.  ...  Using a comprehensive database of synthetic distorted samples, the DCNN learns to accurately estimate distortion bases ten times faster than the dictionary search methods used in the previous approaches  ...  Network Architecture We used a deep convolutional neural network to learn the two eigenvector-based distortion coefficients.  ... 
arXiv:1801.01198v1 fatcat:vushxzimrje37er6ybphpkjy3a

Fingerprint Distortion Detection

Harshada Kanade, Gauri Uttarwar, Shweta Borse, Archana. K
2020 International Journal of Scientific Research in Computer Science Engineering and Information Technology  
The approach is to utilize local patches centered and aligned using fingerprint details.  ...  Fingerprint is widely used in biometrics, for identification of individual's identity. Biometric recognition is a leading technology for identification and security systems.  ...  Analysis of Fingerprint Pores for Vitality "ImageNet classification with deep convolutional neural network", in Forensics and Security, 11(6), 1206- fingerprint identification system.  ... 
doi:10.32628/cseit2063204 fatcat:mqj5majcjrce7ifnzvs5w5n55m

Fingerprint Identification based on Novel Siamese Rectangular Convolutional Neural Networks

He Zhengfang, College of Information and Computing, University of Southeastern Philippines, Davao City, Davao del Sur, Philippines, Allemar Jhone P. Delima, Ivy Kim D. Machica, Jan Carlo T. Arroyo, Su Weibin, Xu Gang
2022 International Journal of Emerging Technology and Advanced Engineering  
After 2012, some researchers used convolutional neural networks for fingerprint identification tasks and achieved higher accuracy.  ...  Keywords—Fingerprint identification; Siamese; Rectangular Convolutional Neural Network  ...  Furthermore, it expounds on related technologies, including Deep Neural Network, Fingerprint Analysis Based on Convolutional Neural Network (fingerprint classification, fingerprint distortion rectification  ... 
doi:10.46338/ijetae0522_04 fatcat:nchfslvfabcz7piecs76656fhy

FDeblur-GAN: Fingerprint Deblurring using Generative Adversarial Network [article]

Amol S. Joshi, Ali Dabouei, Jeremy Dawson, Nasser M. Nasrabadi
2021 arXiv   pre-print
Using a database of blurred fingerprints and corresponding ridge maps, the deep network learns to deblur from the input blurry samples.  ...  and distortion.  ...  [7] used a deep convolutional neural network to rectify the photometric distortion to improve the recognition performance.  ... 
arXiv:2106.11354v1 fatcat:wct7pqtj6najzeb7djeqmdhoxa

An Automated and Robust Image Watermarking Scheme Based on Deep Neural Networks [article]

Xin Zhong, Pei-Chi Huang, Spyridon Mastorakis, Frank Y. Shih
2020 arXiv   pre-print
In this paper, a robust and blind image watermarking scheme based on deep learning neural networks is proposed.  ...  To minimize the requirement of domain knowledge, the fitting ability of deep neural networks is exploited to learn and generalize an automated image watermarking algorithm.  ...  C This paper introduces an automated and robust image watermarking scheme using deep convolutional neural networks.  ... 
arXiv:2007.02460v1 fatcat:5bd4a3xmjvge7he6wv5cv2jlce

Dense Registration and Mosaicking of Fingerprints by Training an End-to-End Network [article]

Zhe Cui, Jianjiang Feng, Jie Zhou
2020 arXiv   pre-print
Dense registration of fingerprints is a challenging task due to elastic skin distortion, low image quality, and self-similarity of ridge pattern.  ...  Registration and matching experiments on FVC2004 databases, Tsinghua Distorted Fingerprint (TDF) database, and NIST SD27 latent fingerprint database show that our registration method outperforms previous  ...  In this paper, we make an attempt to develop an end-to-end convolutional neural network (CNN) for dense registration of fingerprints, in order to deal with the challenges above.  ... 
arXiv:2004.05972v1 fatcat:zlrmqnp3qvajlp3imc7qbghy44

Angular Coherence Observation in Fingerprints and Lungs for Fingerprint Classification and COVID-19 Differentiation

Hemad Heidari Jobaneh
2020 European Journal of Electrical Engineering and Computer Science  
Two neural networks with the same topology are trained by the features. First, one of the neural networks is trained by cropped images.  ...  Second, another neural network is trained by HOG features obtained from the cropped images.  ...  Many works have been performed to unravel the mystery of the new disease, incorporating deep learning and convolutional neural networks [21] - [28] .  ... 
doi:10.24018/ejece.2020.4.4.235 fatcat:vvr7v3oj5vcxnlscznpytpycsy

2020 Index IEEE Transactions on Circuits and Systems for Video Technology Vol. 30

2020 IEEE transactions on circuits and systems for video technology (Print)  
Blind Image Quality Assessment Using a Deep Bilinear Convolutional Neural Network.  ...  Ding, Y., +, TCSVT Feb. 2020 590-602 Blind Image Quality Assessment Using a Deep Bilinear Convolutional Neural Network.  ... 
doi:10.1109/tcsvt.2020.3043861 fatcat:s6z4wzp45vfflphgfcxh6x7npu

Research on image classification model based on deep convolution neural network

Mingyuan Xin, Yong Wang
2019 EURASIP Journal on Image and Video Processing  
Based on the analysis of the error backpropagation algorithm, we propose an innovative training criterion of depth neural network for maximum interval minimum classification error.  ...  Finally, we tested our proposed M3 CE-CEc on two deep learning standard databases, MNIST and CIFAR-10.  ...  Conclusions Deep convolution neural networks are used to identify scaling, translation, and other forms of distortion-invariant images.  ... 
doi:10.1186/s13640-019-0417-8 fatcat:57zvkhyw7ndedbkxnj7lzm7fmy

2021 Index IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 43

2022 IEEE Transactions on Pattern Analysis and Machine Intelligence  
., +, TPAMI Aug. 2021 2570-2581 Deep Convolutional Neural Network for Multi-Modal Image Restoration and Fusion.  ...  Lee, H., +, TPAMI May 2021 1499-1514 Deep Convolutional Neural Network for Multi-Modal Image Restoration and Fusion.  ... 
doi:10.1109/tpami.2021.3126216 fatcat:h6bdbf2tdngefjgj76cudpoyia

On the Learning of Deep Local Features for Robust Face Spoofing Detection [article]

Gustavo Botelho de Souza, João Paulo Papa, Aparecido Nilceu Marana
2018 arXiv   pre-print
State-of-the-art approaches, based on Convolutional Neural Networks (CNNs), present good results in face spoofing detection.  ...  Initially, each part of the neural network learns features from a given facial region. Afterwards, the whole model is fine-tuned on the whole facial images.  ...  Among the proposed deep learning architectures, Convolutional Neural Networks (CNN) [11] have appeared as one of the most important classes of deep neural networks able to deal with digital images with  ... 
arXiv:1806.07492v2 fatcat:iinu6ioyrvctpc5ec3pbotmpsy

Layer-Wise Relevance Propagation for Explaining Deep Neural Network Decisions in MRI-Based Alzheimer's Disease Classification

Moritz Böhle, Fabian Eitel, Martin Weygandt, Kerstin Ritter
2019 Frontiers in Aging Neuroscience  
In this study, we propose using layer-wise relevance propagation (LRP) to visualize convolutional neural network decisions for AD based on MRI data.  ...  Deep neural networks have led to state-of-the-art results in many medical imaging tasks including Alzheimer's disease (AD) detection based on structural magnetic resonance imaging (MRI) data.  ...  Convolutional Neural Network Architecture Convolutional neural networks (CNNs) are neural networks optimized for array data including images or videos (LeCun et al., 2015) .  ... 
doi:10.3389/fnagi.2019.00194 pmid:31417397 pmcid:PMC6685087 fatcat:r3owbnwehfbfxjb4c5j3lvn3si

2020 Index IEEE Transactions on Image Processing Vol. 29

2020 IEEE Transactions on Image Processing  
Model-Free Distortion Rectification Framework Bridged by Distortion Distribution Map.  ...  ., +, TIP 2020 3957-3969 Model-Free Distortion Rectification Framework Bridged by Distortion Distribution Map.  ... 
doi:10.1109/tip.2020.3046056 fatcat:24m6k2elprf2nfmucbjzhvzk3m

2021 Index IEEE Transactions on Image Processing Vol. 30

2021 IEEE Transactions on Image Processing  
Blinder, D., +, TIP 2021 9418-9428 Optical distortion A Deep Ordinal Distortion Estimation Approach for Distortion Rectification.  ...  Liu, J., +, TIP 2021 9030-9042 Cameras A Deep Ordinal Distortion Estimation Approach for Distortion Rectification.  ... 
doi:10.1109/tip.2022.3142569 fatcat:z26yhwuecbgrnb2czhwjlf73qu

Visualizing evidence for Alzheimer's disease in deep neural networks trained on structural MRI data [article]

Moritz Böhle and Fabian Eitel and Martin Weygandt and Kerstin Ritter
2019 arXiv   pre-print
In this study, we propose using layer-wise relevance propagation (LRP) to visualize convolutional neural network decisions for AD based on MRI data.  ...  Deep neural networks have led to state-of-the-art results in many medical imaging tasks including Alzheimer's disease (AD) detection based on structural magnetic resonance imaging (MRI) data.  ...  Convolutional neural network architecture Convolutional neural networks (CNNs) are neural networks optimized for array data including images or videos (LeCun et al., 2015) .  ... 
arXiv:1903.07317v1 fatcat:z6eamnecrrabboez6jx5bpnoya
« Previous Showing results 1 — 15 out of 98 results