Search Results for author:
Found 41 papers, 15 papers with code
Vehicle-Rear: A New Dataset to Explore Feature Fusion for Vehicle Identification Using Convolutional Neural Networks
To explore our dataset we design a two-stream CNN that simultaneously uses two of the most distinctive and persistent features available: the vehicle's appearance and its license plate.
Fine-Grained Vehicle Classification
License Plate Detection
+3
On the Cross-dataset Generalization in License Plate Recognition
Automatic License Plate Recognition (ALPR) systems have shown remarkable performance on license plates (LPs) from multiple regions due to advances in deep learning and the increasing availability of datasets.
Federated Learning Enables Big Data for Rare Cancer Boundary Detection
Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data.
A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector
First, in the SSIG dataset, composed of 2, 000 frames from 101 vehicle videos, our system achieved a recognition rate of 93. 53% and 47 Frames Per Second (FPS), performing better than both Sighthound and OpenALPR commercial systems (89. 80% and 93. 03%, respectively) and considerably outperforming previous results (81. 80%).
Ranked #2 on
License Plate Recognition
on SSIG-SegPlate
An Efficient and Layout-Independent Automatic License Plate Recognition System Based on the YOLO detector
This paper presents an efficient and layout-independent Automatic License Plate Recognition (ALPR) system based on the state-of-the-art YOLO object detector that contains a unified approach for license plate (LP) detection and layout classification to improve the recognition results using post-processing rules.
Ranked #1 on
License Plate Recognition
on Caltech Cars
Combining Attention Module and Pixel Shuffle for License Plate Super-Resolution
The License Plate Recognition (LPR) field has made impressive advances in the last decade due to novel deep learning approaches combined with the increased availability of training data.
Super-Resolution of License Plate Images Using Attention Modules and Sub-Pixel Convolution Layers
As a result, the generated images have a Structural Similarity Index Measure (SSIM) of less than 0. 10.
Face Super-Resolution Using Stochastic Differential Equations
To the best of our knowledge, this is the first time SDEs have been used for such an application.
OCFR 2022: Competition on Occluded Face Recognition From Synthetically Generated Structure-Aware Occlusions
This work summarizes the IJCB Occluded Face Recognition Competition 2022 (IJCB-OCFR-2022) embraced by the 2022 International Joint Conference on Biometrics (IJCB 2022).
Tell me what you see: A zero-shot action recognition method based on natural language descriptions
To the best of our knowledge, this is the first work to represent both videos and labels with descriptive sentences.
Image-based Automatic Dial Meter Reading in Unconstrained Scenarios
The replacement of analog meters with smart meters is costly, laborious, and far from complete in developing countries.
Dense Video Captioning Using Unsupervised Semantic Information
We introduce a method to learn unsupervised semantic visual information based on the premise that complex events (e. g., minutes) can be decomposed into simpler events (e. g., a few seconds), and that these simple events are shared across several complex events.
Towards an Effective and Efficient Deep Learning Model for COVID-19 Patterns Detection in X-ray Images
Thus, the main goal of this work is to propose an accurate yet efficient method in terms of memory and processing time for the problem of COVID-19 screening in chest X-rays.
Global Semantic Descriptors for Zero-Shot Action Recognition
This work introduces a new ZSAR method based on the relationships of actions-objects and actions-descriptive sentences.
FRCSyn Challenge at WACV 2024:Face Recognition Challenge in the Era of Synthetic Data
Despite the widespread adoption of face recognition technology around the world, and its remarkable performance on current benchmarks, there are still several challenges that must be covered in more detail.
A Benchmark for Iris Location and a Deep Learning Detector Evaluation
The iris is considered as the biometric trait with the highest unique probability.
Benchmark for License Plate Character Segmentation
In general, ALPR is divided into the following problems: detection of on-track vehicles, license plates detection, segmention of license plate characters and optical character recognition (OCR).
Deep Representations for Iris, Face, and Fingerprint Spoofing Detection
We assume a very limited knowledge about biometric spoofing at the sensor to derive outstanding spoofing detection systems for iris, face, and fingerprint modalities based on two deep learning approaches.
Fully Convolutional Networks and Generative Adversarial Networks Applied to Sclera Segmentation
The initial and paramount step for performing this type of recognition is the segmentation of the region of interest, i. e. the sclera.
The Impact of Preprocessing on Deep Representations for Iris Recognition on Unconstrained Environments
The use of iris as a biometric trait is widely used because of its high level of distinction and uniqueness.
Robust Iris Segmentation Based on Fully Convolutional Networks and Generative Adversarial Networks
In this paper, two approaches for robust iris segmentation based on Fully Convolutional Networks (FCNs) and Generative Adversarial Networks (GANs) are described.
Convolutional Neural Networks for Automatic Meter Reading
This dataset is, to the best of our knowledge, three times larger than the largest public dataset found in the literature and contains a well-defined evaluation protocol to assist the development and evaluation of AMR methods.
Ranked #3 on
Meter Reading
on UFPR-AMR
Simultaneous Iris and Periocular Region Detection Using Coarse Annotations
In this work, we propose to detect the iris and periocular regions simultaneously using coarse annotations and two well-known object detectors: YOLOv2 and Faster R-CNN.
Zero-Shot Action Recognition in Videos: A Survey
Zero-Shot Action Recognition has attracted attention in the last years and many approaches have been proposed for recognition of objects, events and actions in images and videos.
Action Recognition In Still Images
Action Recognition In Videos
+2
Inter-Patient ECG Heartbeat Classification with Temporal VCG Optimized by PSO
Classifying arrhythmias can be a tough task for a human being and automating this task is highly desirable.
Ranked #5 on
Arrhythmia Detection
on MIT-BIH AR
Ocular Recognition Databases and Competitions: A Survey
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 region when the image resolution is not sufficient to extract iris information.
Deep Representations for Cross-spectral Ocular Biometrics
One of the major challenges in ocular biometrics is the cross-spectral scenario, i. e., how to match images acquired in different wavelengths (typically visible (VIS) against near-infrared (NIR)).
Unconstrained Periocular Recognition: Using Generative Deep Learning Frameworks for Attribute Normalization
Ocular biometric systems working in unconstrained environments usually face the problem of small within-class compactness caused by the multiple factors that jointly degrade the quality of the obtained data.
CNN Hyperparameter tuning applied to Iris Liveness Detection
The iris pattern has significantly improved the biometric recognition field due to its high level of stability and uniqueness.
Deep Learning for Image-based Automatic Dial Meter Reading: Dataset and Baselines
Smart meters enable remote and automatic electricity, water and gas consumption reading and are being widely deployed in developed countries.
Ranked #1 on
Meter Reading
on UFPR-ADMR-v1
Towards Image-based Automatic Meter Reading in Unconstrained Scenarios: A Robust and Efficient Approach
Existing approaches for image-based Automatic Meter Reading (AMR) have been evaluated on images captured in well-controlled scenarios.
Ranked #1 on
Meter Reading
on Copel-AMR
Image-based Automatic Meter Reading
Optical Character Recognition (OCR)
Automatic Counting and Identification of Train Wagons Based on Computer Vision and Deep Learning
In this work, we present a robust and efficient solution for counting and identifying train wagons using computer vision and deep learning.
A New Periocular Dataset Collected by Mobile Devices in Unconstrained Scenarios
Thus, the use of datasets containing many subjects is essential to assess biometric systems' capacity to extract discriminating information from the periocular region.
Ranked #1 on
Image Classification
on Imbalanced CUB-200-2011
Open-set Face Recognition for Small Galleries Using Siamese Networks
Therefore, open-set face recognition is a subject of increasing interest as it deals with identifying individuals in a space where not all faces are known in advance.
Computational methods for differentially expressed gene analysis from RNA-Seq: an overview
The analysis of differential gene expression from RNA-Seq data has become a standard for several research areas mainly involving bioinformatics.
A Decidability-Based Loss Function
Nowadays, deep learning is the standard approach for a wide range of problems, including biometrics, such as face recognition and speech recognition, etc.
ORCNet: A context-based network to simultaneously segment the ocular region components
Accurate extraction of the Region of Interest is critical for successful ocular region-based biometrics.
A First Look at Dataset Bias in License Plate Recognition
We performed experiments on eight datasets, four collected in Brazil and four in mainland China, and observed that each dataset has a unique, identifiable "signature" since a lightweight classification model predicts the source dataset of a license plate (LP) image with more than 95% accuracy.
Do We Train on Test Data? The Impact of Near-Duplicates on License Plate Recognition
This work draws attention to the large fraction of near-duplicates in the training and test sets of datasets widely adopted in License Plate Recognition (LPR) research.
Leveraging Model Fusion for Improved License Plate Recognition
License Plate Recognition (LPR) plays a critical role in various applications, such as toll collection, parking management, and traffic law enforcement.
SDFR: Synthetic Data for Face Recognition Competition
The submitted models were trained on existing and also new synthetic datasets and used clever methods to improve training with synthetic data.
