Submitted by Izabel Franco (izabel-franco@ufscar.br) on 2016-10-10T13:03:01Z No. of bitstreams: 1 DissIJRF.pdf: 11147756 bytes, checksum: c2f35d4805dd8c9a93b43189bacf2899 (MD5) / Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-10-20T19:38:57Z (GMT) No. of bitstreams: 1 DissIJRF.pdf: 11147756 bytes, checksum: c2f35d4805dd8c9a93b43189bacf2899 (MD5) / Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-10-20T19:39:03Z (GMT) No. of bitstreams: 1 DissIJRF.pdf: 11147756 bytes, checksum: c2f35d4805dd8c9a93b43189bacf2899 (MD5) / Made available in DSpace on 2016-10-20T19:39:09Z (GMT). No. of bitstreams: 1 DissIJRF.pdf: 11147756 bytes, checksum: c2f35d4805dd8c9a93b43189bacf2899 (MD5) Previous issue date: 2016-05-20 / Não recebi financiamento / The computational analysis of human mobility can aid in treatment and rehabilitation of people who have some kind of physical disability. Physiotherapeutic studies, called kinematics, aims to correct body’s dysfunction, caused by genetic malformations, degenerative diseases or posture problems. A computer system can be used to precisely describe motion, anatomical position and angular terms during gait. Therefore, the movements need to be captured and represented in three-dimensional space to be evaluated. This work is a study, development and evaluation of a system for capturing human movement based on recorded videos, using low cost computing resources and adapted to the reality of gait analysis. The automation of computing movements capture, as well as aid in neurological, motor or orthopedic rehabilitation will expand its application to community. In this context, a study was conducted about computing methods of human movements capture by passive markers, and defined the four main steps: calibration and synchronization of the cameras, detection and three-dimensional reconstruction. Computer experiments were made to facilitate the theoretical understanding of this work. Kinematic analyzes data were used as a study case for the implementati / A análise computacional de movimentos humanos pode auxiliar no tratamento e reabilitação de pessoas que possuem algum tipo de deficiência motora. Estudos na fisioterapia, chamado de avaliação cinemática, tem por objetivo direcionar tratamentos para corrigir as disfunções do corpo, causadas pela má formação genética, doenças degenerativas ou problemas de postura. Um sistema computacional pode ser utilizado para descrever precisamente os movimentos, posição anatômica e termos angulares durante o período da marcha. Para tanto, os movimentos precisam ser capturados e reconstruídos no espaço tridimensional para posteriormente serem avaliados. Este trabalho consiste no estudo, desenvolvimento e avaliação de um sistema para captura de movimentos humanos baseado em registros de vídeos, utilizando recursos computacionais de baixo custo e adaptado a realidade da análise de marcha. A automatização da captura computacional destes movimentos, além de auxiliar na reabilitação motora neurológica e ou ortopédica de pessoas que possuem alguma deficiência do gênero, ampliará sua aplicação na comunidade. Nesse contexto, foi realizado o levantamento bibliográfico das principais técnicas de captura computacional de movimentos, e no caso dos sistemas ópticos com marcadores passivos, e definido quatro etapas principais: calibração, sincronização das câmeras, detecção e reconstrução tridimensional. Ainda, experimentos computacionais foram realizados para o entendimento prático e teórico do trabalho. Registros de marcha de crianças foram utilizados como estudo de caso para a implementação das etapas de calibração, detecção dos marcadores e reconstrução tridimensional.

Este trabalho discute a determinação de padrões respiratórios em sequências temporais de imagens obtidas por Ressonância Magnética (RM) e o seu uso no registro temporal de imagens coronais e sagitais. O registro é feito sem o uso de qualquer informação de sincronismo e qualquer gás especial para reforçar o contraste. As sequências temporais de imagens são adquiridas em respiração livre. O movimento real do pulmão nunca foi diretamente visto, pois é totalmente dependente dos músculos que o rodeiam. A visualização do pulmão em movimento é um tema atual de pesquisa na medicina. O movimento do pulmão não possui intervalos regulares e é suscetível a variações na respiração. Comparado à Tomografia Computadorizada (TC), a RM necessita de um maior tempo de aquisição e é preferível porque não envolve radiação. Como as sequências de imagens coronais e sagitais são ortogonais entre si, a sua intersecção corresponde a um segmento de reta no espaço tridimensional. O registro se baseia na análise deste segmento interseccional. A variação deste segmento de interseção no tempo pode ser enfileirada, definindo uma imagem espaço-temporal em duas dimensões (2DST). Supõe-se que o movimento diafragmático é o movimento principal de todas as estruturas do pulmão se movem quase que totalmente sincronicamente. A sincronização deste movimento é feita através de um padrão chamado função respiração. Este padrão é obtido através do processamento de uma imagem 2DST. Um algoritmo da transformada de Hough intervalar procura movimentos sincronizados na função respiração. O algoritmo de contornos ativos ajusta pequenas discrepâncias originadas por movimentos assíncronos nos padrões respiratórios . A saída é um conjunto de padrões respiratórios. Finalmente, a composição de imagens coronal e sagital que estão na mesma fase respiratória é realizada através da comparação de padrões respiratórios provenientes das superfícies de contorno superior e diafragmática. Quando disponíveis, os padrões respiratórios associados às estruturas internas do pulmão também são usados. Vários resultados e conclusões são apresentados. / This work addresses the determination of the breathing patterns in time sequence of images obtained from Magnetic Resonance (MR) and their use in the temporal registration of coronal and sagital images. The registration is done without the use of any triggering information and any special gas to enhance the contrast. The temporal sequences of images are acquired in free breathing. The real movement of the lung has never been seen directly, as it is totally dependent on its surrounding muscles and collapses without them. The visualization of the lung in motion is an actual topic of research in medicine. The lung movement is not periodic and it is susceptible to variations in the degree of respiration. Compared to Computerized Tomography (CT), MR imaging involves longer acquisition times and it is preferable because it does not involve radiation. As coronal and sagittal sequences of images are orthogonal to each other, their intersection corresponds to a segment in the three dimensional space. The registration is based on the analysis of this intersection segment. A time sequence of this intersection segment can be stacked, defining a two-dimension spatio-temporal (2DST) image. It is assumed that the diaphragmatic movement is the principal movement and all the lung structures move almost synchronously. The synchronization of this motion is performed through a pattern named respiratory function. This pattern is obtained by processing a 2DST image. An interval Hough transform algorithm searches for synchronized movements with the respiratory function. A greedy searches for synchronized movements with the respiratory function. A greedy active contour algorithm adjusts small discrepancies originated by asynchronous movements in the respiratory patterns. The output is a set of respiratory patterns. Finally, the composition of coronal and sagittal images that are in the same breathing phase is realized by comparing of respiratory patterns originated from diaphragmatic and upper boundary surfaces. When available, the respire tory patterns associated to lung internal structures are also used. Several results and conclusions are shown.

Doenças vasculares são um problema mundial, que representa 28% das mortes no mundo e 66% do total de doenças que acometem os brasileiros. Dessa forma, há um grande interesse em pesquisar formas de prevenção e tratamento dessas doenças. Algumas medidas são relevantes no auxílio de diagnóstico, tal como: tamanho médio dos ramos, diâmetro médio das seções transversais dos vasos e padrões de divisão de ramos. Calcular essas medidas de forma manual é uma tarefa demorada e trabalhosa. Assim, esta Tese tem como objetivo, propor um método computacional de rastreamento e extração de atributos em redes vasculares a partir de imagens 3D de angiografia por ressonância magnética e por tomografia computadorizada. Trata-se de uma abordagem de rastreamento e identificação de bifurcações que difere das técnicas anteriores, utilizando a Transformada de Hough para identificar o diâmetro do vaso em cortes transversais num dado ponto ao longo de um vaso sanguíneo. Mais detalhadamente, essa abordagem utiliza um campo vetorial advindo do cálculo de uma matriz formada por derivadas parciais de segunda ordem, obtida da intensidade luminosa da imagem, para identificar a direção de um ramo de vaso. Além disso, durante o processo de rastreamento de um ramo de vaso, são calculados vários descritores de forma com o objetivo de classificar regiões como pertencentes a uma bifurcação ou não. Em adição a estes descritores, desenvolvemos uma nova medida chamada de variância do raio que permite distinguir, bifurcações, não-bifurcações e segmentos de vaso com stents (aparelho metálico usado para aumentar o diâmetro dos vasos). Para a classificação de bifurcações, criamos a medida de bifurcação, que trata-se de uma combinação linear de todos os descritores de forma apresentados neste trabalho. Testes foram realizados para atestar a eficácia da abordagem proposta, utilizando tanto imagens sintéticas quantoimagens reais. Os resultados mostraram que o método é capaz de rastrear 91% de uma rede vascular sintética variando o ponto de inicialização e 76% variando o nível de ruído. Também foi observado por meio de testes que o método proposto consegue rastrear vasos e identificar bifurcações em imagens reais sem avaliação numérica. Essa abordagem permite a extração da relação hierárquica entre os ramos em uma rede vascular e a extração do padrão de divisão dos vasos, o que contribui sobremaneira para o estudo do comportamento do fenômeno da angiogênese e no auxílio no diagnóstico de anomalias vasculares. / Vascular diseases are a main health problem, representing 28% of deaths worldwide and 66% of all diseases affecting the Brazilian population. Thus, it is important that researches in prevention and treatment of this type of disease increase. Moreover, there are several demands, such as computational tools capable of analyzing and extracting attributes from non-invasive images. The scope of this work is the analysis and extraction of data from magnetic resonance angiography and computed tomography angiography images by highlighting blood vessels. In this context, this thesis aims the development of a novel computational tracking and feature extraction method for vascular networks from 3D images. Our approach presents the following steps: First, identify the vessel cross-sections along it using the Hough transform. Then, compute a matrix composed of second order partial derivatives of image intensity to identify the direction of the vessel. Perform a feature analysis of the vessel contour to classify the bifurcation point, and finally, identify the direction of the new branch in a bifurcation point. The main contribution of this Thesis is the two new measures developed, called radius ratio and bifurcation measure, the radius ratio is capable to distinguish between a region with bifurcation, stents or without both of them. The bifurcation measure is a linear combination that allows to classify a region as bifurcation or not. Tests were performed in order to verify the proposed approach effectiveness, using both synthetic images and real images. The results showed the method is capable to track 91% of synthetic vascular networks varying the seed point and 76% varying the level of noise. Also, we performed tests in real images and by visual evaluation, we could observed that the proposed method was able to track vessels and identify bifurcations from different parts of the body. This approach allows to calculate, in the future, the density of bifurcations in a vascular network, the distance between them, the stenosis and aneurysms grading and characterize specific vessels. In addition, the vascular networks extraction allows the study of the angiogenesis phenomena and vascular anomalies.

Les capacités des technologies de télédétection ont augmenté exponentiellement au cours des dernières années : de nouveaux scanners fournissent maintenant une représentation géométrique de leur environnement sous la forme de nuage de points avec une précision jusqu'ici inégalée. Le traitement de nuages de points est donc devenu une discipline à part entière avec ses problématiques propres et de nombreux défis à relever. Le coeur de cette thèse porte sur la modélisation géométrique et introduit une méthode robuste d'extraction de formes tubulaires à partir de nuages de points. Nous avons choisi de tester nos méthodes dans le contexte applicatif difficile de la foresterie pour mettre en valeur la robustesse de nos algorithmes et leur application à des données volumineuses. Nos méthodes intègrent les normales aux points comme information supplémentaire pour atteindre les objectifs de performance nécessaire au traitement de nuages de points volumineux.Cependant, ces normales ne sont généralement pas fournies par les capteurs, il est donc nécessaire de les pré-calculer.Pour préserver la rapidité d'exécution, notre premier développement a donc consisté à présenter une méthode rapide d'estimation de normales. Pour ce faire nous avons approximé localement la géométrie du nuage de points en utilisant des "patchs" lisses dont la taille s'adapte à la complexité locale des nuages de points. Nos travaux se sont ensuite concentrés sur l’extraction robuste de formes tubulaires dans des nuages de points denses, occlus, bruités et de densité inhomogène. Dans cette optique, nous avons développé une variante de la transformée de Hough dont la complexité est réduite grâce aux normales calculées. Nous avons ensuite couplé ces travaux à une proposition de contours actifs indépendants de leur paramétrisation. Cette combinaison assure la cohérence interne des formes reconstruites et s’affranchit ainsi des problèmes liés à l'occlusion, au bruit et aux variations de densité. Notre méthode a été validée en environnement complexe forestier pour reconstruire des troncs d'arbre afin d'en relever les qualités par comparaison à des méthodes existantes. La reconstruction de troncs d'arbre ouvre d'autres questions à mi-chemin entre foresterie et géométrie. La segmentation des arbres d'une placette forestière est l'une d’entre elles. C'est pourquoi nous proposons également une méthode de segmentation conçue pour contourner les défauts des nuages de points forestiers et isoler les différents objets d'un jeu de données. Durant nos travaux nous avons utilisé des approches de modélisation pour répondre à des questions géométriques, et nous les avons appliqué à des problématiques forestières.Il en résulte un pipeline de traitements cohérent qui, bien qu'illustré sur des données forestières, est applicable dans des contextes variés. / Abstract : The potential of remote sensing technologies has recently increased exponentially: new sensors now provide a geometric representation of their environment in the form of point clouds with unrivalled accuracy. Point cloud processing hence became a full discipline, including specific problems and many challenges to face. The core of this thesis concerns geometric modelling and introduces a fast and robust method for the extraction of tubular shapes from point clouds. We hence chose to test our method in the difficult applicative context of forestry in order to highlight the robustness of our algorithms and their application to large data sets. Our methods integrate normal vectors as a supplementary geometric information in order to achieve the performance goal necessary for large point cloud processing. However, remote sensing techniques do not commonly provide normal vectors, thus they have to be computed. Our first development hence consisted in the development of a fast normal estimation method on point cloud in order to reduce the computing time on large point clouds. To do so, we locally approximated the point cloud geometry using smooth ''patches`` of points which size adapts to the local complexity of the point cloud geometry. We then focused our work on the robust extraction of tubular shapes from dense, occluded, noisy point clouds suffering from non-homogeneous sampling density. For this objective, we developed a variant of the Hough transform which complexity is reduced thanks to the computed normal vectors. We then combined this research with a new definition of parametrisation-invariant active contours. This combination ensures the internal coherence of the reconstructed shapes and alleviates issues related to occlusion, noise and variation of sampling density. We validated our method in complex forest environments with the reconstruction of tree stems to emphasize its advantages and compare it to existing methods. Tree stem reconstruction also opens new perspectives halfway in between forestry and geometry. One of them is the segmentation of trees from a forest plot. Therefore we also propose a segmentation approach designed to overcome the defects of forest point clouds and capable of isolating objects inside a point cloud. During our work we used modelling approaches to answer geometric questions and we applied our methods to forestry problems. Therefore, our studies result in a processing pipeline adapted to forest point cloud analyses, but the general geometric algorithms we propose can also be applied in various contexts.

This diploma thesis deals with object detection using mathematical technique called Hough transform. Hough transform technique is conceived in general terms from the de facto simplest use for the detection of elementary analytically describable shapes such as lines, ellipses, circles or simple analytically definable elements to sophisticated use for the detection of complex - analytically virtually indescribable - objects. These include cars or pedestrians who are detected on the basis of the photographic records of these objects and entities. The document thus maps the definition and use of the respective Hough transform subtechniques along with their basic classification on probabilistic and non-probabilistic methods. The work subsequently culminates in describing the general state-of-the-art technique called Class-Specific Hough Forests for Object Detection, introduces its definition, training procedure on a provided dataset and the detection of test patterns. In conclusion of this work,there is designed and implemented generally trainable object detector using this technique. And there is experimental evaluation of its quality.

La représentation visuelle est un problème fondamental en vision par ordinateur. Le but est de réduire l'information au strict nécessaire pour une tâche désirée. Plusieurs types de représentation existent, comme les caractéristiques de couleur (histogrammes, attributs de couleurs...), de forme (dérivées, points d'intérêt...) ou d'autres, comme les bancs de filtres.Les caractéristiques bas-niveau (locales) sont rapides à calculer. Elles ont un pouvoir de représentation limité, mais leur généricité présente un intérêt pour des systèmes autonomes et multi-tâches, puisque les caractéristiques haut-niveau découlent d'elles.Le but de cette thèse est de construire puis d'étudier l'impact de représentations fondées seulement sur des caractéristiques locales de bas-niveau (couleurs, dérivées spatiales) pour deux tâches : la poursuite d'objets génériques, nécessitant des caractéristiques robustes aux variations d'aspect de l'objet et du contexte au cours du temps; la détection d'objets, où la représentation doit décrire une classe d'objets en tenant compte des variations intra-classe. Plutôt que de construire des descripteurs d'objets globaux dédiés, nous nous appuyons entièrement sur les caractéristiques locales et sur des mécanismes statistiques flexibles visant à estimer leur distribution (histogrammes) et leurs co-occurrences (Transformée de Hough Généralisée). La Transformée de Hough Généralisée (THG), créée pour la détection de formes quelconques, consiste à créer une structure de données représentant un objet, une classe... Cette structure, d'abord indexée par l'orientation du gradient, a été étendue à d'autres caractéristiques. Travaillant sur des caractéristiques locales, nous voulons rester proche de la THG originale.En poursuite d'objets, après avoir présenté nos premiers travaux, combinant la THG avec un filtre particulaire (utilisant un histogramme de couleurs), nous présentons un algorithme plus léger et rapide (100fps), plus précis et robuste. Nous présentons une évaluation qualitative et étudierons l'impact des caractéristiques utilisées (espace de couleur, formulation des dérivées partielles...). En détection, nous avons utilisé l'algorithme de Gall appelé forêts de Hough. Notre but est de réduire l'espace de caractéristiques utilisé par Gall, en supprimant celles de type HOG, pour ne garder que les dérivées partielles et les caractéristiques de couleur. Pour compenser cette réduction, nous avons amélioré deux étapes de l'entraînement : le support des descripteurs locaux (patchs) est partiellement produit selon une mesure géométrique, et l'entraînement des nœuds se fait en générant une carte de probabilité spécifique prenant en compte les patchs utilisés pour cette étape. Avec l'espace de caractéristiques réduit, le détecteur n'est pas plus précis. Avec les mêmes caractéristiques que Gall, sur une même durée d'entraînement, nos travaux ont permis d'avoir des résultats identiques, mais avec une variance plus faible et donc une meilleure répétabilité. / Visual representation is a fundamental problem in computer vision. The aim is to reduce the information to the strict necessary for a query task. Many types of representation exist, like color features (histograms, color attributes...), shape ones (derivatives, keypoints...) or filterbanks.Low-level (and local) features are fast to compute. Their power of representation are limited, but their genericity have an interest for autonomous or multi-task systems, as higher level ones derivate from them. We aim to build, then study impact of low-level and local feature spaces (color and derivatives only) for two tasks: generic object tracking, requiring features robust to object and environment's aspect changes over the time; object detection, for which the representation should describe object class and cope with intra-class variations.Then, rather than using global object descriptors, we use entirely local features and statisticals mecanisms to estimate their distribution (histograms) and their co-occurrences (Generalized Hough Transform).The Generalized Hough Transform (GHT), created for detection of any shape, consists in building a codebook, originally indexed by gradient orientation, then to diverse features, modeling an object, a class. As we work on local features, we aim to remain close to the original GHT.In tracking, after presenting preliminary works combining the GHT with a particle filter (using color histograms), we present a lighter and fast (100 fps) tracker, more accurate and robust.We present a qualitative evaluation and study the impact of used features (color space, spatial derivative formulation).In detection, we used Gall's Hough Forest. We aim to reduce Gall's feature space and discard HOG features, to keep only derivatives and color ones.To compensate the reduction, we enhanced two steps: the support of local descriptors (patches) are partially chosen using a geometrical measure, and node training is done by using a specific probability map based on patches used at this step.With reduced feature space, the detector is less accurate than with Gall's feature space, but for the same training time, our works lead to identical results, but with higher stability and then better repeatability.

The application of Image Processing to Autonomous driving has drawn significant attention in recently. However, the demanding nature of the image processing algorithms conveys a considerable burden to any conventional realtime implementation. On the other hand, the emergence of FPGAs has brought numerous facilities toward fast prototyping and implementation of ASICs so that an image processing algorithm can be designed, tested and synthesized in a relatively short period in comparison to traditional approaches. This thesis investigates the best combination of current algorithms to reach an optimum solution to the problem of lane detection and tracking, while aiming to fit the design to a minimal system. The proposed structure realizes three algorithms, namely Edge Detector, Hough Transform, and Kalman filter. For each module, the theoretical background is investigated and a detailed description of the realization is given followed by an analysis of both achievements and shortages of the design. It is concluded by describing the advantages of implementing this architecture and the use of these kinds of systems. / Tillämpningen av bildbehandling inom autonoma fordon har fått stor uppmärksamhet den senaste tiden. Emellertid förmedlar den krävande karaktären hos bildbehandlingsalgoritmerna en stor belastning på vilken konventionell realtidsimplementering som helst. Å andra sidan har framväxten av FPGAer medfört många möjligheter till snabb prototypering och implementering av ASICar så att en bildbehandlingsalgoritm kan utformas, testas och syntetiseras på relativt kort tid jämfört med traditionella tillvägagångssätt. Denna avhandling undersöker den bästa kombinationen av nuvarande algoritmer för att uppnå en optimal lösning på problemet med spårning och fildetektering, med målet att krympa designen till ett minimalt system. Den föreslagna strukturen realiserar tre algoritmer, nämligen Edge Detector, Hough Transform och Kalman filter. För varje modul undersöks den teoretiska bakgrunden och en detaljerad beskrivning av realiseringen ges följd av en analys av både fördelar och brister i konstruktionen. Avhandlingen avslutas med en beskrivning av fördelarna med att implementera lösningen på det sätt den görs och hur dessa system kan användas.

Master of Science / Department of Biological & Agricultural Engineering / Naiqian Zhang / The primary objective of this research is to achieve automatic evaluation of the efficiency of using Trichogramma bourarachae for biological control of Cadra (=Ephestia) cautella by calculating the rate of parasitization. Cadra cautella is a moth feeding as a larva on dried fruit as well as stored nuts, seeds, and other warehouse foodstuffs. It attacks dates from ripening stages while on tree, throughout storage, and until consumption. These attacks cause significant qualitative and quantitative damages, which negatively affect dates’ marketability, resulting in economic losses. To achieve this research goal, tasks were accomplished by developing image processing algorithms for detecting, identifying, and differentiating between three Cadra cautella egg categories based on the success of Trichogramma parasitization against them. The egg categories were parasitized (black and dark red), fertile (unhatched yellow), and hatched (white) eggs. Color, intensity, and shape information was obtained from digital images of Cadra eggs after they were subjected to Trichogramma parasitization and used to develop detection algorithms. Two image processing methods were developed. The first method included segmentation and extractions of color and morphological features followed by watershed delineation, and is referred to as the "Watershed Method" (WT). The second method utilized the Hough Transformation to find circular objects followed by convolution filtering, and is referred to as the "Hough Transform Method" (HT). The algorithms were developed based on 2 images and then tested on more than 40 images. The WT and the HT methods achieved correct classification rates (CCRs) of parasitized eggs of 92% and 96%, respectively. Their CCRs of yellow eggs were 48% and 94%, respectively, while for white eggs the CCRs were 42% and 73%. Both methods performed satisfactorily in detecting the parasitized eggs, but the HT outperformed the WT in detecting the unparasitized eggs. The developed detection methods will enable automatic evaluation of biological control of Cadra (=Ephestia) cautella using Trichogramma bourarachae. Moreover, with few adjustments these methods can be used in similar applications such as detecting plant diseases in terms of presence of insects or their eggs.

Um novo método para avaliação do tamanho de gotas de chuva e sua distribuição é apresentado. O método é baseado no processamento de imagens com o uso da transformada de Hough circular em conjunto com as técnicas de Backmapping e análise de vizinhança. Esta metodologia trás vantagens, uma vez que viabiliza medidas diretas e de forma automática para identificação e contagem de gotas de chuva. A calibração do método foi desenvolvida utilizando padrões de gotas conhecidos. Gotas, na faixa de 1 &#956m a 85 mm de diâmetro, foram automaticamente reconhecidas e medidas com sucesso. Resultados mostram erro médio percentual não maior que 3,61%. Adicionalmente é apresentado uma comparação de resultados obtidos com um método de análise de correlação em frequência e contagem direta. Resultados mostram a potencialidade da metodologia desenvolvida para aplicações agrícolas. / A new method for evaluating raindrop size and distribution has been developed. It is based on image processing with circular Hough fast transform composed with the Backmapping and neighborhood analysis techniques. This methodology has the advantage of being a direct measurement method that automatically identifies and counts raindrops. Calibration was carried out using standard patterns with known raindrop sizes. Drops sizes ranging from 1 &#956m sizes to 85 mm in diameter has been automatically recognized and successfully measured. Results show perceptual average error not larger than 3,61%. In addition a comparison of results with the correlation analysis in the frequency domain and directed counts methods are presented. Results show the suitability of developed methodology.

Detecção automática de regiões planares em nuvens de pontos é um importante passo para muitas aplicações gráficas, de processamento de imagens e de visão computacional. Enquanto a disponibilidade de digitalizadores a laser e a fotografia digital tem nos permitido capturar nuvens de pontos cada vez maiores, técnicas anteriores para detecção de planos são computacionalmente caras, sendo incapazes de alcançar desempenho em tempo real para conjunto de dados contendo dezenas de milhares de pontos, mesmo quando a detecção é feita de um modo não determinístico. Apresentamos uma abordagem determinística para detecção de planos em nuvens de pontos não estruturadas que apresenta complexidade computacional O(n log n) no número de amostras de entrada. Ela é baseada em um método eficiente de votação para a transformada de Hough. Nossa estratégia agrupa conjuntos de pontos aproximadamente coplanares e deposita votos para estes conjuntos em um acumulador esférico, utilizando núcleos Gaussianos trivariados. Uma comparação com as técnicas concorrentes mostra que nossa abordagem é consideravelmente mais rápida e escala significativamente melhor que as técnicas anteriores, sendo a primeira solução prática para detecção determinística de planos em nuvens de pontos grandes e não estruturadas. / Automatic detection of planar regions in point clouds is an important step for many graphics, image processing, and computer vision applications. While laser scanners and digital photography have allowed us to capture increasingly larger datasets, previous techniques are computationally expensive, being unable to achieve real-time performance for datasets containing tens of thousands of points, even when detection is performed in a non-deterministic way. We present a deterministic technique for plane detection in unorganized point clouds whose cost is O(n log n) in the number of input samples. It is based on an efficient Hough-transform voting scheme and works by clustering approximately co-planar points and by casting votes for these clusters on a spherical accumulator using a trivariate Gaussian kernel. A comparison with competing techniques shows that our approach is considerably faster and scales significantly better than previous ones, being the first practical solution for deterministic plane detection in large unorganized point clouds.