An Orientation Selective Neural Network and its Application to Cosmic Muon Identification

We propose a novel method for identification of a linear pattern of pixels on a two-dimensional grid. Following principles employed by the visual cortex, we employ orientation selective neurons in a neural network which performs this task. The method is then applied to a sample of data collected with the ZEUS detector at HERA in order to identify cosmic muons which leave a linear pattern of signals in the segmented uranium-scintillator calorimeter. A two dimensional representation of the relevant part of the detector is used. The results compared with a visual scan point to a very satisfactory cosmic muon identification. The algorithm performs well in the presence of noise and pixels with limited efficiency. Given its architecture, this system becomes a good candidate for fast pattern recognition in parallel processing devices.Comment: 19 pages, 10 Postrcipt figure

Generic object classification for autonomous robots

Un dels principals problemes de la interacció dels robots autònoms és el coneixement de l'escena. El reconeixement és fonamental per a solucionar aquest problema i permetre als robots interactuar en un escenari no controlat. En aquest document presentem una aplicació pràctica de la captura d'objectes, de la normalització i de la classificació de senyals triangulars i circulars. El sistema s'introdueix en el robot Aibo de Sony per a millorar-ne la interacció. La metodologia presentada s'ha comprobat en simulacions i problemes de categorització reals, com ara la classificació de senyals de trànsit, amb resultats molt prometedors.Uno de los principales problemas de la interacción de los robots autónomos es el conocimiento de la escena. El reconocimiento es fundamental para solventar este problema y permitir a los robots interactuar en un escenario no controlado. En este documento, presentamos una aplicación práctica de captura del objeto, normalización y clasificación de señales triangulares y circulares. El sistema es introducido en el robot Aibo de Sony para mejorar el comportamiento de la interacción del robot. La metodología presentada ha sido testeada en simulaciones y problemas de categorización reales, como es la clasificación de señales de tráfico, con resultados muy prometedores.One of the main problems of autonomous robots interaction is the scene knowledge. Recognition is concerned to deal with this problem and to allow robots to interact in uncontrolled environments. In this paper, we present a practical application for object fitting, normalization and classification of triangular and circular signs. The system is introduced in the Aibo robot of Sony to increase the robot interaction behaviour. The presented methodology has been tested in real simulations and categorization problems, as the traffic signs classification, with very promising results.Nota: Aquest document conté originàriament altre material i/o programari només consultable a la Biblioteca de Ciència i Tecnologia

Novel Methodologies for Pattern Recognition of Charged Particle Trajectories in the ATLAS Detector

By 2029, the Large Hadron Collider will enter its High Luminosity phase (HL- LHC) in order to achieve an unprecedented capacity for discovery. As this phase is entered, it is essential for many physics analyses that the efficiency of the re- construction of charged particle trajectories in the ATLAS detector is maintained. With levels of pile-up expected to reach = 200, the number of track candidates that must be processed will increase exponentially in the current pattern matching regime. In this thesis, a novel method for charged particle pattern recognition is developed based on the popular computer vision technique known as the Hough Transform (HT). Our method differs from previous attempts to use the HT for tracking in its data-driven choice of track parameterisation using Principal Component Analysis (PCA), and the division of the detector space in to very narrow tunnels known as sectors. This results in well-separated Hough images across the layers of the detector and relatively little noise from pile-up. Additionally, we show that the memory requirements for a pattern-based track finding algorithm can be reduced by approximately a factor of 5 through a two-stage compression process, without sacrificing any significant track finding efficiency. The new tracking algorithm is compared with an existing pattern matching algorithm, which consists of matching detector hits to a collection of pre-defined patterns of hits generated from simulated muon tracks. The performance of our algorithm is shown to achieve similar track finding efficiency while reducing the number of track candidates per event

Segmentation of needle artifacts in real time 2D MR images

During percutaneous minimally invasive procedures a needle is used to access the region to be treated without need for an open surgery. Remarkable is the increasing use of percutaneous thermal ablation to treat neoplastic formation. The effectiveness of such procedures is highly dependent on the correct placement of the needle inside the region to be treated. Imaging monitoring provides the physician with the possibility to inspect the location of the device, which is responsible for a signal void in the MR images acquired referred to as needle artifact. The procedure can be performed under real time Magnetic Resonance Imaging guidance. In this work two algorithms for automatic needle detection in real time MR images were developed. The detection is anticipated to increase the accuracy of the device positionin

Detecção de linha de plantio de cana de açúcar a partir de imagens de VANT usando Segmentação Semântica e Transformada de Radon

In recent years, UAVs (Unmanned Aerial Vehicles) have become increasingly popular in the agricultural sector, promoting and enabling the application of aerial image monitoring in both scientific and business contexts. Images captured by UAVs are fundamental for precision farming practices, as they allow activities that deal with low and medium altitude images. After the effective sowing, the scenario of the planted area may change drastically over time due to the appearance of erosion, gaps, death and drying of part of the crop, animal interventions, etc. Thus, the process of detecting the crop rows is strongly important for planning the harvest, estimating the use of inputs, control of costs of production, plant stand counts, early correction of sowing failures, more-efficient watering, etc. In addition, the geolocation information of the detected lines allows the use of autonomous machinery and a better application of inputs, reducing financial costs and the aggression to the environment. In this work we address the problem of detection and segmentation of sugarcane crop lines using UAV imagery. First, we experimented an approach based on \ac{GA} associated with Otsu method to produce binarized images. Then, due to some reasons including the recent relevance of Semantic Segmentation in the literature, its levels of abstraction, and the non-feasible results of Otsu associated with \ac{GA}, we proposed a new approach based on \ac{SSN} divided in two steps. First, we use a Convolutional Neural Network (CNN) to automatically segment the images, classifying their regions as crop lines or as non-planted soil. Then, we use the Radon transform to reconstruct and improve the already segmented lines, making them more uniform or grouping fragments of lines and loose plants belonging to the same planting line. We compare our results with segmentation performed manually by experts and the results demonstrate the efficiency and feasibility of our approach to the proposed task.Dissertação (Mestrado)Nos últimos anos, os VANTs (Veículos Aéreos Não Tripulados) têm se tornado cada vez mais populares no setor agrícola, promovendo e possibilitando o monitoramento de imagens aéreas tanto no contexto científico, quanto no de negócios. Imagens capturadas por VANTs são fundamentais para práticas de agricultura de precisão, pois permitem a realização de atividades que lidam com imagens de baixa ou média altitude. O cenário da área plantada pode mudar drasticamente ao longo do tempo devido ao aparecimento de erosões, falhas de plantio, morte e ressecamento de parte da cultura, intervenções de animais, etc. Assim, o processo de detecção das linhas de plantio é de grande importância para o planejamento da colheita, controle de custos de produção, contagem de plantas, correção de falhas de semeadura, irrigação eficiente, entre outros. Além disso, a informação de geolocalização das linhas detectadas permite o uso de maquinários autônomos e um melhor planejamento de aplicação de insumos, reduzindo custos e a agressão ao meio ambiente. Neste trabalho, abordamos o problema de segmentação e detecção de linhas de plantio de cana-de-açúcar em imagens de VANTs. Primeiro, experimentamos uma abordagem baseada em Algoritmo Genético (AG) e Otsu para produzir imagens binarizadas. Posteriormente, devido a alguns motivos, incluindo a relevância recente da Segmentação Semântica, seus níveis de abstração e os resultados inviáveis obtidos com AG, estudamos e propusemos uma nova abordagem baseada em \ac{SSN} em duas etapas. Primeiro, usamos uma \ac{SSN} para segmentar as imagens, classificando suas regiões como linhas de plantio ou como solo não plantado. Em seguida, utilizamos a transformada de Radon para reconstruir e melhorar as linhas já segmentadas, tornando-as mais uniformes ou agrupando fragmentos de linhas e plantas soltas. Comparamos nossos resultados com segmentações feitas manualmente por especialistas e os resultados demonstram a eficiência e a viabilidade de nossa abordagem para a tarefa proposta