607 citations · 1.7k across the 20 of their papers we have counts for
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Automatic Chronic Degenerative Diseases Identification Using Enteric Nervous System Images
Gustavo Z. Felipe, Jacqueline N. Zanoni, Camila C. Sehaber-Sierakowski +6
Studies recently accomplished on the Enteric Nervous System have shown that chronic degenerative diseases affect the Enteric Glial Cells (EGC) and, thus, the development of recogni…
A Comprehensive Comparison of End-to-End Approaches for Handwritten Digit String Recognition
Andre G. Hochuli, Alceu S. Britto, David A. Saji +3
Over the last decades, most approaches proposed for handwritten digit string recognition (HDSR) have resorted to digit segmentation, which is dominated by heuristics, thereby impos…
Intrapersonal Parameter Optimization for Offline Handwritten Signature Augmentation
Teruo M. Maruyama, Luiz S. Oliveira, Alceu S. Britto +1
Usually, in a real-world scenario, few signature samples are available to train an automatic signature verification system (ASVS). However, such systems do indeed need a lot of sig…
Single-sample writers -- "Document Filter" and their impacts on writer identification
Fabio Pinhelli, Alceu S. Britto, Luiz S. Oliveira +2
The writing can be used as an important biometric modality which allows to unequivocally identify an individual. It happens because the writing of two different persons present dif…
An End-to-End Approach for Recognition of Modern and Historical Handwritten Numeral Strings
Andre G. Hochuli, Alceu S. Britto, Jean P. Barddal +2
An end-to-end solution for handwritten numeral string recognition is proposed, in which the numeral string is considered as composed of objects automatically detected and recognize…
Ocular Recognition Databases and Competitions: A Survey
Luiz A. Zanlorensi, Rayson Laroca, Eduardo Luz +3
The use of the iris and periocular region as biometric traits has been extensively investigated, mainly due to the singularity of the iris features and the use of the periocular re…
Meta-learning for fast classifier adaptation to new users of Signature Verification systems
Luiz G. Hafemann, Robert Sabourin, Luiz S. Oliveira
Offline Handwritten Signature verification presents a challenging Pattern Recognition problem, where only knowledge of the positive class is available for training. While classifie…
Texture CNN for Histopathological Image Classification
Jonathan de Matos, Alceu de S. Britto, Luiz E. S. de Oliveira +1
Biopsies are the gold standard for breast cancer diagnosis. This task can be improved by the use of Computer Aided Diagnosis (CAD) systems, reducing the time of diagnosis and reduc…
Double Transfer Learning for Breast Cancer Histopathologic Image Classification
Jonathan de Matos, Alceu de S. Britto, Luiz E. S. Oliveira +1
This work proposes a classification approach for breast cancer histopathologic images (HI) that uses transfer learning to extract features from HI using an Inception-v3 CNN pre-tra…
Characterizing and evaluating adversarial examples for Offline Handwritten Signature Verification
Luiz G. Hafemann, Robert Sabourin, Luiz S. Oliveira
The phenomenon of Adversarial Examples is attracting increasing interest from the Machine Learning community, due to its significant impact to the security of Machine Learning syst…
Decoupling Direction and Norm for Efficient Gradient-Based L2 Adversarial Attacks and Defenses
Jérôme Rony, Luiz G. Hafemann, Luiz S. Oliveira +3
Research on adversarial examples in computer vision tasks has shown that small, often imperceptible changes to an image can induce misclassification, which has security implication…
The Impact of Preprocessing on Deep Representations for Iris Recognition on Unconstrained Environments
Luiz A. Zanlorensi, Eduardo Luz, Rayson Laroca +3
The use of iris as a biometric trait is widely used because of its high level of distinction and uniqueness. Nowadays, one of the major research challenges relies on the recognitio…
Segmentation-Free Approaches for Handwritten Numeral String Recognition
Andre G Hochuli, Luiz E S Oliveira, Alceu S Britto +1
This paper presents segmentation-free strategies for the recognition of handwritten numeral strings of unknown length. A synthetic dataset of touching numeral strings of sizes 2-,…
Fixed-sized representation learning from Offline Handwritten Signatures of different sizes
Luiz G. Hafemann, Robert Sabourin, Luiz S. Oliveira
Methods for learning feature representations for Offline Handwritten Signature Verification have been successfully proposed in recent literature, using Deep Convolutional Neural Ne…
A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector
Rayson Laroca, Evair Severo, Luiz A. Zanlorensi +4
Automatic License Plate Recognition (ALPR) has been a frequent topic of research due to many practical applications. However, many of the current solutions are still not robust in…
Learning Features for Offline Handwritten Signature Verification using Deep Convolutional Neural Networks
Luiz G. Hafemann, Robert Sabourin, Luiz S. Oliveira
Verifying the identity of a person using handwritten signatures is challenging in the presence of skilled forgeries, where a forger has access to a person's signature and deliberat…
Analyzing features learned for Offline Signature Verification using Deep CNNs
Luiz G. Hafemann, Robert Sabourin, Luiz S. Oliveira
Research on Offline Handwritten Signature Verification explored a large variety of handcrafted feature extractors, ranging from graphology, texture descriptors to interest points.…
Writer-independent Feature Learning for Offline Signature Verification using Deep Convolutional Neural Networks
Luiz G. Hafemann, Robert Sabourin, Luiz S. Oliveira
Automatic Offline Handwritten Signature Verification has been researched over the last few decades from several perspectives, using insights from graphology, computer vision, signa…
Offline Handwritten Signature Verification - Literature Review
Luiz G. Hafemann, Robert Sabourin, Luiz S. Oliveira
The area of Handwritten Signature Verification has been broadly researched in the last decades, but remains an open research problem. The objective of signature verification system…