Brazilian vehicle identification using a new embedded plate recognition system

Neto, Edson Cavalcanti; Gomes, Samuel Luz; Filho, Pedro Pedrosa Rebouças; Albuquerque, Victor Hugo C. de

doi:10.1016/j.measurement.2015.03.039

Cited by 45 publications

(31 citation statements)

References 19 publications

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“…The pattern recognition experiments were performed using machine learning algorithms with different settings to obtain the overall classification performance for each feature set built. The classifiers used were: Bayesian [41], Optimum-Path Forest (OPF) with Euclidean distance [42,43], k-Nearest Neighbor (kNN) with k = 1 [44], and Support Vector Machine configured with the radial basis function kernel (SVM) and automatic setup [45]. The classification accuracy was estimated using the 10fold cross-validation method.…”

Section: Resultsmentioning

confidence: 99%

Analysis of human tissue densities: A new approach to extract features from medical images

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Rebouas

Marinho

et al. 2017

Pattern Recognition Letters

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Identification of diseases based on processing and analysis of medical images is of great importance for medical doctors to assist them in their decision making. In this work, a new feature extraction method based on human tissue density patterns, named Analysis of Human Tissue Densities (AHTD) is presented. The proposed method uses radiological densities of human tissues in Hounsfield Units to tackle the extraction of suitable features from medical images. This new method was compared against: the Gray Level Co-occurrence Matrix, Hu's moments, Statistical moments, Zernike's moments, Elliptic Fourier features, Tamura's features and the Statistical Co-occurrence Matrix. Four machine learning classifiers were applied to each feature extractor for two CT image datasets:, one to classify lung disease in CT images of the thorax and the other to classify stroke in CT images of the brain. The attributes were extracted from the lung images in 5.2 milliseconds and obtained an accuracy of 99.01% for the detection and classification of lung diseases, while the attributes from the brain images were extracted in 3.8 milliseconds and obtained an accuracy of 98.81% for the detection and classification of stroke. These results show that the proposed method can be used to classify diseases in medical images, and can be used in real-time applications due to its fast extraction time of suitable attributes. c

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Section: Resultsmentioning

confidence: 99%

Analysis of human tissue densities: A new approach to extract features from medical images

Filho

Rebouas

Marinho

et al. 2017

Pattern Recognition Letters

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“…Initially this technique was created by Cortes and Vapnik to solve binary problems. But its kernel machines are versatile and allow you to classify in a simpler way [39]. The MLP artificial neural network, inspired by the human neural network, it can be represented by a non-linear vector in the input vector and another vector in the output vector.…”

Section: Classifier Specifications and Evaluation Metricsmentioning

confidence: 99%

Intelligent Incipient Fault Detection in Wind Turbines based on Industrial IoT Environment

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et al. 2019

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How to cite this paper: Sousa, P. H. F.; Nascimento, N. M. M.; Almeida, J. S.; Rebouças Filho, P. P. and Albuquerque, V. AbstractThe eagerness and necessity to develop so-called smart applications has taken the Internet of Things (IoT) to a whole new level. Industry has been implementing services that use IoT to increase productivity as well as management systems over the past couple of years. Such services are now encroaching on wind energy, which nowadays is the most acceptable source among renewable energies for electricity generation. This work proposes an intelligent system to identify incipient faults in the electric generators of wind turbines to improve maintenance routines. Four feature extraction methods were applied to vibration signals, and different classifiers were used to predict the running status of the wind turbine. We correctly identified 94.44% of normal conditions, reducing the false positive and negative rates to 0.4% and 1.84%, respectively; a better result than other approaches already reported in the literature. Kiviluoma [29] analyzed the behavior of the power variation in different regions and demonstrated that rapid changes in local wind conditions cause power fluctuations in the IoT system and operational costs of a wind farm

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“…An embedded plate recognition system is proposed in [12]. This system is built through C language on a Linux operating system that can be extended for embedded applications.…”

Section: Related Workmentioning

confidence: 99%

Deep Classifiers-Based License Plate Detection, Localization and Recognition on GPU-Powered Mobile Platform

et al. 2017

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The realization of a deep neural architecture on a mobile platform is challenging, but can open up a number of possibilities for visual analysis applications. A neural network can be realized on a mobile platform by exploiting the computational power of the embedded GPU and simplifying the flow of a neural architecture trained on the desktop workstation or a GPU server. This paper presents an embedded platform-based Italian license plate detection and recognition system using deep neural classifiers. In this work, trained parameters of a highly precise automatic license plate recognition (ALPR) system are imported and used to replicate the same neural classifiers on a Nvidia Shield K1 tablet. A CUDA-based framework is used to realize these neural networks. The flow of the trained architecture is simplified to perform the license plate recognition in real-time. Results show that the tasks of plate and character detection and localization can be performed in real-time on a mobile platform by simplifying the flow of the trained architecture. However, the accuracy of the simplified architecture would be decreased accordingly.

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Brazilian vehicle identification using a new embedded plate recognition system

Cited by 45 publications

References 19 publications

Analysis of human tissue densities: A new approach to extract features from medical images

Analysis of human tissue densities: A new approach to extract features from medical images

Intelligent Incipient Fault Detection in Wind Turbines based on Industrial IoT Environment

Deep Classifiers-Based License Plate Detection, Localization and Recognition on GPU-Powered Mobile Platform

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