Automatic vehicle identfication for Argentinean license plates using intelligent template matching

The problem of automatic number plate recognition (ANPR) has been studied from different aspects since the early 90s. Efficient approaches have been recently developed, particularly based on the features of the license plate representation used in different countries. This paper focuses on a novel approach to solving the ANPR problem for Argentinean license plates, called Intelligent Template Matching (ITM). We compare the performance obtained with other competitive approaches to robust pattern recognition (such as artificial neural networks), showing the advantages both in classification accuracy and training time. The approach can also be easily extended to other license plate representation systems different from the one used in Argentina.Fil: Gazcón, Nicolás Fernando. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Chesñevar, Carlos Iván. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Castro, Silvia Mabel. Universidad Nacional del Sur. Departamento de Ciencias e Ingenieria de la Computacion. Laboratorio de Investigación y Desarrollo en Visualización yComputación Gráfica; Argentin

A new augmentation-based method for text detection in night and day license plate images

Despite a number of methods that have been developed for License Plate Detection (LPD), most of these focus on day images for license plate detection. As a result, license plate detection in night images is still an elusive goal for researchers. This paper presents a new method for LPD based on augmentation and Gradient Vector Flow (GVF) in night and day images. The augmentation involves expanding windows for each pixel in R, G and B color spaces of the input image until the process finds dominant pixels in both night and day license plate images of the respective color spaces. We propose to fuse the dominant pixels in R, G and B color spaces to restore missing pixels. For the results of fusing night and day images, the proposed method explores Gradient Vector Flow (GVF) patterns to eliminate false dominant pixels, which results in candidate pixels. The proposed method explores further GVF arrow patterns to define a unique loop pattern that represents hole in the characters, which gives candidate components. Furthermore, the proposed approach uses a recognition concept to fix the bounding boxes, merging the bounding boxes and eliminating false positives, resulting in text/license plate detection in both night and day images. Experimental results on night images of our dataset and day images of standard license plate datasets, demonstrate that the proposed approach is robust compared to the state-of-the-art methods. To show the effectiveness of the proposed method, we also tested our approach on standard natural scene datasets, namely, ICDAR 2015, MSRA-TD-500, ICDAR 2017-MLT, Total-Text, CTW1500 and MS-COCO datasets, and their results are discussed

Texture and Colour in Image Analysis

Research in colour and texture has experienced major changes in the last few years. This book presents some recent advances in the field, specifically in the theory and applications of colour texture analysis. This volume also features benchmarks, comparative evaluations and reviews

Deep learning and feature engineering techniques applied to the myoelectric signal for accurate prediction of movements

Técnicas de reconhecimento de padrões no Sinal Mioelétrico (EMG) são empregadas no desenvolvimento de próteses robóticas, e para isso, adotam diversas abordagens de Inteligência Artificial (IA). Esta Tese se propõe a resolver o problema de reconhecimento de padrões EMG através da adoção de técnicas de aprendizado profundo de forma otimizada. Para isso, desenvolveu uma abordagem que realiza a extração da característica a priori, para alimentar os classificadores que supostamente não necessitam dessa etapa. O estudo integrou a plataforma BioPatRec (estudo e desenvolvimento avançado de próteses) a dois algoritmos de classificação (Convolutional Neural Network e Long Short-Term Memory) de forma híbrida, onde a entrada fornecida à rede já possui características que descrevem o movimento (nível de ativação muscular, magnitude, amplitude, potência e outros). Assim, o sinal é rastreado como uma série temporal ao invés de uma imagem, o que nos permite eliminar um conjunto de pontos irrelevantes para o classificador, tornando a informação expressivas. Na sequência, a metodologia desenvolveu um software que implementa o conceito introduzido utilizando uma Unidade de Processamento Gráfico (GPU) de modo paralelo, esse incremento permitiu que o modelo de classificação aliasse alta precisão com um tempo de treinamento inferior a 1 segundo. O modelo paralelizado foi chamado de BioPatRec-Py e empregou algumas técnicas de Engenharia de Features que conseguiram tornar a entrada da rede mais homogênea, reduzindo a variabilidade, o ruído e uniformizando a distribuição. A pesquisa obteve resultados satisfatórios e superou os demais algoritmos de classificação na maioria dos experimentos avaliados. O trabalho também realizou uma análise estatística dos resultados e fez o ajuste fino dos hiper-parâmetros de cada uma das redes. Em última instancia, o BioPatRec-Py forneceu um modelo genérico. A rede foi treinada globalmente entre os indivíduos, permitindo a criação de uma abordagem global, com uma precisão média de 97,83%.Pattern recognition techniques in the Myoelectric Signal (EMG) are employed in the development of robotic prostheses, and for that, they adopt several approaches of Artificial Intelligence (AI). This Thesis proposes to solve the problem of recognition of EMG standards through the adoption of profound learning techniques in an optimized way. The research developed an approach that extracts the characteristic a priori to feed the classifiers that supposedly do not need this step. The study integrated the BioPatRec platform (advanced prosthesis study and development) to two classification algorithms (Convolutional Neural Network and Long Short-Term Memory) in a hybrid way, where the input provided to the network already has characteristics that describe the movement (level of muscle activation, magnitude, amplitude, power, and others). Thus, the signal is tracked as a time series instead of an image, which allows us to eliminate a set of points irrelevant to the classifier, making the information expressive. In the sequence, the methodology developed software that implements the concept introduced using a Graphical Processing Unit (GPU) in parallel this increment allowed the classification model to combine high precision with a training time of less than 1 second. The parallel model was called BioPatRec-Py and employed some Engineering techniques of Features that managed to make the network entry more homogeneous, reducing variability, noise, and standardizing distribution. The research obtained satisfactory results and surpassed the other classification algorithms in most of the evaluated experiments. The work performed a statistical analysis of the outcomes and fine-tuned the hyperparameters of each of the networks. Ultimately, BioPatRec-Py provided a generic model. The network was trained globally between individuals, allowing the creation of a standardized approach, with an average accuracy of 97.83%

Assembly Line

An assembly line is a manufacturing process in which parts are added to a product in a sequential manner using optimally planned logistics to create a finished product in the fastest possible way. It is a flow-oriented production system where the productive units performing the operations, referred to as stations, are aligned in a serial manner. The present edited book is a collection of 12 chapters written by experts and well-known professionals of the field. The volume is organized in three parts according to the last research works in assembly line subject. The first part of the book is devoted to the assembly line balancing problem. It includes chapters dealing with different problems of ALBP. In the second part of the book some optimization problems in assembly line structure are considered. In many situations there are several contradictory goals that have to be satisfied simultaneously. The third part of the book deals with testing problems in assembly line. This section gives an overview on new trends, techniques and methodologies for testing the quality of a product at the end of the assembling line

Full depth CNN classifier for handwritten and license plate characters recognition

Character recognition is an important research field of interest for many applications. In recent years, deep learning has made breakthroughs in image classification, especially for character recognition. However, convolutional neural networks (CNN) still deliver state-of-the-art results in this area. Motivated by the success of CNNs, this paper proposes a simple novel full depth stacked CNN architecture for Latin and Arabic handwritten alphanumeric characters that is also utilized for license plate (LP) characters recognition. The proposed architecture is constructed by four convolutional layers, two max-pooling layers, and one fully connected layer. This architecture is low-complex, fast, reliable and achieves very promising classification accuracy that may move the field forward in terms of low complexity, high accuracy and full feature extraction. The proposed approach is tested on four benchmarks for handwritten character datasets, Fashion-MNIST dataset, public LP character datasets and a newly introduced real LP isolated character dataset. The proposed approach tests report an error of only 0.28% for MNIST, 0.34% for MAHDB, 1.45% for AHCD, 3.81% for AIA9K, 5.00% for Fashion-MNIST, 0.26% for Saudi license plate character and 0.97% for Latin license plate characters datasets. The license plate characters include license plates from Turkey (TR), Europe (EU), USA, United Arab Emirates (UAE) and Kingdom of Saudi Arabia (KSA)

Weed/Plant Classification Using Evolutionary Optimised Ensemble Based On Local Binary Patterns

This thesis presents a novel pixel-level weed classification through rotation-invariant uniform local binary pattern (LBP) features for precision weed control. Based on two-level optimisation structure; First, Genetic Algorithm (GA) optimisation to select the best rotation-invariant uniform LBP configurations; Second, Covariance Matrix Adaptation Evolution Strategy (CMA-ES) in the Neural Network (NN) ensemble to select the best combinations of voting weights of the predicted outcome for each classifier. The model obtained 87.9% accuracy in CWFID public benchmark