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Real-time multi-circle detection has been a challenging problem in the field of biomedical image processing, due to the variable sizes and non-ideal shapes of cells in microscopic images. In this study, two new multi-circle detection algorithms are developed to facilitate an automatic bladder cancer diagnosis system: one is a modified circular Hough Transform algorithm integrated with edge gradient information; and the other one is a stochastic search approach based on real valued artificial immune systems. Computer simulation results show both algorithms outperform traditional methods such as the Hough Transform and the geometric feature based method, in terms of both precision and speed.

Feature extraction from a tensor based local image representation introduced by Knutsson in [37] is discussed. The tensor representation keeps statements of structure, certainty of statement and energy separate. Further processing for obtaining new features also having these three entities separate is achieved by the use of a new concept, tensor field filtering. Tensor filters for smoothing and for extraction of circular symmetries are presented and discussed in particular. These methods are used for corner detection and extraction of more global features such as lines in images. A novel method for grouping local orientation estimates into global line parameters is introduced. The method is based on a new parameter space, the Möbius Strip parameter space, which has similarities to the Hough transform. A local centroid clustering algorithm is used for classification in this space. The procedure automatically divides curves into line segments with appropriate lengths depending on the curvature. A linked list structure is built up for storing data in an efficient way. / <p>Ogiltigt nummer / annan version: I publ. nr 290:s ISBN: 91-7870-815-X.</p>

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License plate recognition/identification (LPR/I) applies image processing and character recognition technology to identify vehicles by automatically reading their license plates. In the United States, however, each state has its own standard-issue plates, plus several optional styles, which are referred to as special license plates or varieties. There is a clear absence of standardization and multi-colored, complex backgrounds are becoming more frequent in license plates. Commercially available optical character recognition (OCR) systems generally fail when confronted with textured or poorly contrasted backgrounds, therefore creating the need for proper image segmentation prior to classification. The image segmentation problem in LPR is examined in two stages: license plate region detection and license plate character extraction from background. Three different approaches for license plate detection in a scene are presented: region distance from eigenspace, border location by edge detection and the Hough transform, and text detection by spectral analysis. The experiments for character segmentation involve the RGB, HSV/HSI and 1976 CIE L*a*b* color spaces as well as their Karhunen-Loéve transforms. The segmentation techniques applied include multivariate hierarchical agglomerative clustering and minimum-variance color quantization. The trade-off between accuracy and computational expense is used to select a final reliable algorithm for license plate detection and character segmentation. The spectral analysis approach together with the K-L L*a*b* transformed color quantization are found experimentally as the best alternatives for the two identified image segmentation stages for US license plate recognition. / Master of Science

The field of computer vision offers tools that allow machines to derive meaningful infor-mation from video and images and consequently make decisions based on visual inputs. In the paper industry, implementation of machine vision (MV) can be used to automate and speed up processes that require visual inspection, particularly certain segments of quality control – one such application being detection and measurement of paper roll core eccentricity. Core eccentricity is a roll build error in which the roll core is offset from the geometric roll center, potentially causing runnability issues. This particular project aims to improve the detection of paper roll core eccentricity at the Mondi Dynäs integrated pulp and paper mill through creation, calibration and evaluation of a machine-vision-based tool for on-line core eccentricity measurement. The tool utilizes the Hough Transform (HT), since HT is a simple yet fast and robust algorithm when it comes to identification of basic shapes such as lines and circles. The proposed solution was evaluated in two ways; firstly by determining at what level of accuracy the measurements could be provided, accounting for how well the solution deals with correction of systematic error caused by environmental factors, and secondly by analyzing how well characteristic roll features could be accurately identified in large sets of data, necessary to consistently perform measurements. The evaluation of the proposed solution showed a 99.9% detection rate for characteristic paper roll features, and a 98.1% detection rate of laser lines used for correction of position and orientation induced error. Assessment of the measurement accuracy following successful detection was on par with the current optical measurement method, and the proposed solution was able to classify distinctive features with a 96.8% accuracy. Lastly, several improvement actions to address faulty detection were identified, and factors to be considered for future installment were highlighted.

A presente pesquisa tem por objetivo desenvolver um sistema automático de detecção de eixos de caminhões a partir de imagens. Para isso, são apresentados dois sistemas automáticos: o primeiro para extração de imagens de caminhões a partir de filmagens de tráfego rodoviário feitas em seis locais de uma mesma rodovia situada no Estado de São Paulo, e o segundo, para detecção dos eixos dos caminhões nas imagens. Ambos os sistemas foram fundamentados em conceitos de Processamento de Imagens e Visão Computacional e o desenvolvimento foi feito utilizando programação em linguagem Python e as bibliotecas OpenCV e SciKit. O salvamento automático das imagens de caminhões foi necessário para a construção do banco de imagens utilizado no outro método: a detecção dos eixos dos veículos identificados. Neste estágio foram realizadas a segmentação da imagem do caminhão, a detecção propriamente dita e a classificação dos eixos. Na segmentação dos veículos, utilizou-se as técnicas de limiarização adaptativa seguida de morfologia matemática e em outra ocasião, o descritor de texturas LBP; enquanto na detecção, a Transformada de Hough. Da análise de desempenho desses métodos, a taxa de salvamento das imagens foi 69,2% considerando todos os caminhões que se enquadraram nos frames. Com relação às detecções, a segmentação das imagens dos caminhões feita utilizando limiarização adaptativa com morfologia matemática ofereceu resultados de 57,7% da detecção do total de eixos dos caminhões e 65,6% de falsas detecções. A técnica LBP forneceu, para os mesmos casos, respectivamente, 68,3% e 84,2%. O excesso de detecção foi um ponto negativo dos resultados e pode ser relacionado aos problemas do ambiente externo, geralmente intrínsecos às cenas de tráfego de veículos. Dois fatores que interferiram de maneira significativa foram a iluminação e a movimentação das folhas e galhos das árvores devido ao vento. Desconsiderando esse inconveniente, derivado dos fatores recém citados, as taxas de acerto dos dois tipos de segmentação aumentariam para 90,4% e 93,5%, respectivamente, e as falsas detecções mudariam para 66,5% e 54,7%. Desse modo, os dois sistemas propostos podem ser considerados promissores para o objetivo proposto. / This research aims to develop an automatic truck axle detection system using images. Two automatic systems are presented: one for the extraction of truck images from road videos recorded in a São Paulo state highway and one for the axle detection on images. Both systems are based on Image Processing and Computational Vision techniques, with using programming in Python and OpenCV and SciKit libraries. The truck image extraction system was necessary for the creation of image base, to be used on the axle detection system. Thereunto, image segmentation, axle detection and classification on images were made. In segmentation was used adaptive threshold technique, followed by mathematical morphology and on another time, LBP texture descriptors; for detection, was used Hough Transform. Performance analysis on these methods wielded 69.2% on image save rate, on trucks entirely framed on the image. About axle detection, the truck image segmentation using a combination of adaptive threshold and mathematical morphology wielded 57.7% on axle detection, whilst achieving 65.6% of false detection. Using LBP wielded, on the same images, 68.3% on axle detection and 84.2% of false detection. These excesses was a negative result and can be related to intrinsic issues on the external road traffic environment. Two main factors affected the result: lighting condition changes and the movement of tree leaves and branches. Disregarding these two factors, the proposed system had 90.4% axle truck detection rate using adaptive threshold and mathematical morphology and 93.5% using LBP, and the false detection, changed for 66.5% e 54.7%. Thus, both proposed systems are considered promising.

A mobilidade é uma marca da nossa civilização. Tanto o transporte de carga quanto o de passageiros compartilham de uma enorme infra-estrutura de conexões operados com o apoio de um sofisticado sistema logístico. Simbiose otimizada de módulos mecânicos e elétricos, os veículos evoluem continuamente com a integração de avanços tecnológicos e são projetados para oferecer o melhor em conforto, segurança, velocidade e economia. As regulamentações organizam o fluxo de transporte rodoviário e as suas interações, estipulando regras a fim de evitar conflitos. Mas a atividade de condução pode tornar-se estressante em diferentes condições, deixando os condutores humanos propensos a erros de julgamento e criando condições de acidente. Os esforços para reduzir acidentes de trânsito variam desde campanhas de re-educação até novas tecnologias. Esses tópicos têm atraído cada vez mais a atenção de pesquisadores e indústrias para Sistemas de Transporte Inteligentes baseados em imagens que visam a prevenção de acidentes e o auxilio ao seu motorista na interpretação das formas de sinalização urbana. Este trabalho apresenta um estudo sobre técnicas de detecção em tempo real de faixas de sinalização de trânsito em ambientes urbanos e intermunicipais, com objetivo de realçar as faixas de sinalização da pista para o condutor do veículo ou veículo autônomo, proporcionando um controle maior da área de tráfego destinada ao veículo e prover alertas de possíveis situações de risco. A principal contribuição deste trabalho é otimizar a formar como as técnicas de processamento de imagem são utilizas para realizar a extração das faixas de sinalização, com o objetivo de reduzir o custo computacional do sistema. Para realizar essa otimização foram definidas pequenas áreas de busca de tamanho fixo e posicionamento dinâmico. Essas áreas de busca vão isolar as regiões da imagem onde as faixas de sinalização estão contidas, reduzindo em até 75% a área total onde são aplicadas as técnicas utilizadas na extração de faixas. Os resultados experimentais mostraram que o algoritmo é robusto em diversas variações de iluminação ambiente, sombras e pavimentos com cores diferentes tanto em ambientes urbanos quanto em rodovias e autoestradas. Os resultados mostram uma taxa de detecção correta média de 98; 1%, com tempo médio de operação de 13,3 ms. / Mobility is an imprint of our civilization. Both freight and passenger transport share a huge infrastructure of connecting links operated with the support of a sophisticated logistic system. As an optimized symbiosis of mechanical and electrical modules, vehicles are evolving continuously with the integration of technological advances and are engineered to offer the best in comfort, safety, speed and economy. Regulations organize the flow of road transportation machines and help on their interactions, stipulating rules to avoid conflicts. But driving can become stressing on different conditions, leaving human drivers prone to misjudgments and creating accident conditions. Efforts to reduce traffic accidents that may cause injuries and even deaths range from re-education campaigns to new technologies. These topics have increasingly attracted the attention of researchers and industries to Image-based Intelligent Transportation Systems that aim to prevent accidents and help your driver in the interpretation of urban signage forms. This work presents a study on real-time detection techniques of traffic signaling signs in urban and intermunicipal environments, aiming at the signaling lanes of the lane for the driver of the vehicle or autonomous vehicle, providing a greater control of the area of traffic destined to the vehicle and to provide alerts of possible risk situations. The main contribution of this work is to optimize how the image processing techniques are used to perform the lanes extraction, in order to reduce the computational cost of the system. To achieve this optimization, small search areas of fixed size and dynamic positioning were defined. These search areas will isolate the regions of the image where the signaling lanes are contained, reducing up to 75% the total area where the techniques used in the extraction of lanes are applied. The experimental results showed that the algorithm is robust in several variations of ambient light, shadows and pavements with different colors, in both urban environments and on highways and motorways. The results show an average detection rate of 98.1%, with average operating time of 13.3 ms.

Schistosoma mansoni é o parasita causador da esquistossomose mansônica que, de acordo com o Ministério da Saúde do Brasil, afeta atualmente vários milhões de pessoas no país. Uma das formas de diagnóstico da esquistossomose é a detecção de ovos do parasita através da análise de lâminas microscópicas com material fecal. Esta tarefa é extremamente cansativa, principalmente nos casos de baixa endemicidade, pois a quantidade de ovos é muito pequena. Nesses casos, uma abordagem computacional para auxílio na detecção de ovos facilitaria o trabalho de diagnóstico. Os ovos têm formato ovalado, possuem uma membrana translúcida, apresentam uma espícula e sua cor é ligeiramente amarelada. Porém nem todas essas características são observadas em todos os ovos e algumas delas são visíveis apenas com uma ampliação adequada. Além disso, o aspecto visual do material fecal varia muito de indivíduo para indivíduo em termos de cor e presença de diversos artefatos (tais como partículas que não são desintegradas pelo sistema digestivo), tornando difícil a tarefa de detecção dos ovos. Neste trabalho investigamos, em particular, o problema de detecção das linhas que contornam a borda de vários dos ovos. Propomos um método composto por duas fases. A primeira fase consiste na detecção de estruturas do tipo linha usando operadores morfológicos. A detecção de linhas é dividida em três etapas principais: (i) realce de linhas, (ii) detecção de linhas, e (iii) refinamento do resultado para eliminar segmentos de linhas que não são de interesse. O resultado dessa fase é um conjunto de segmentos de linhas. A segunda fase consiste na detecção de subconjuntos de segmentos de linha dispostos em formato elíptico, usando um algoritmo baseado na transformada Hough. As elipses detectadas são fortes candidatas a contorno de ovos de S. mansoni. Resultados experimentais mostram que a abordagem proposta pode ser útil para compor um sistema de auxílio à detecção dos ovos. / Schistosoma mansoni is one of the parasites which causes schistosomiasis. According to the Brazilian Ministry of Health, several million people in the country are currently affected by schistosomiasis. One way of diagnosing it is by egg identification in stool. This task is extremely time-consuming and tiring, especially in cases of low endemicity, when only few eggs are present. In such cases, a computational approach to help the detection of eggs would greatly facilitate the diagnostic task. Schistosome eggs present oval shape, have a translucent membrane and a spike, and their color is slightly yellowish. However, not all these features are observed in every egg and some of them are visible only with an adequate microscopic magnification. Furthermore, the visual aspect of the fecal material varies widely from person to person in terms of color and presence of different artifacts (such as particles which are not disintegrated by the digestive system), making it difficult to detect the eggs. In this work we investigate the problem of detecting lines which delimit the contour of the eggs. We propose a method comprising two steps. The first phase consists in detecting line-like structures using morphological operators. This line detection phase is divided into three steps: (i) line enhancement, (ii) line detection, and (iii) result refinement in order to eliminate line segments that are not of interest. The output of this phase is a set of line segments. The second phase consists in detecting subsets of line segments arranged in an elliptical shape, using an algorithm based on the Hough transform. Detected ellipses are strong candidates to contour of S. mansoni eggs. Experimental results show that the proposed approach has potential to be effectively used as a component in a computer system to help egg detection.

Ces travaux de recherche visaient à développer un système d'aide à la mobilité pour les personnes ayant une déficience visuelle (VIP ‘Visually Impaired Person’) appelé ‘Smart Environment Explorer Stick (SEES)’. Le but particulier de cette recherche était de développer de nouveaux algorithmes pour améliorer la précision de la détection de la présence de poteaux de la canne SEE-stick en utilisant la méthode de calcul de distance et la recherche de paires de lignes verticales basées sur l'optimisation de la technique de détection de contour de Canny. Désormais, l'algorithme de détection des poteaux est appelé l’algorithme YuRHoS. Le SEES développé comme système de support d'aide à la mobilité VIP a été intégré avec succès à plusieurs dispositifs tels que le serveur distant dénommé iSEE, le serveur local embarqué dénommé SEE-phone et la canne intelligente dénommée SEE-stick. Les performances de SEE-stick ont été améliorées grâce à l'algorithme YuRHoS qui permet de discriminer avec précision les objets (obstacles) en forme de poteau parmi les objets détectés. La comparaison des résultats de détection des poteaux avec ceux des autres algorithmes a conclu que l'algorithme YuRHoS était plus efficace et précis. Le lieu et la couleur des poteaux de test d’évaluation étaient deux des facteurs les plus importants qui influaient sur la capacité du SEE-stick à détecter leur présence. Le niveau de précision de SEE-stick est optimal lorsque le test d’évaluation est effectué à l'extérieur et que les poteaux sont de couleur argentée. Les statistiques montrent que la performance de l'algorithme YuRHoS à l'intérieur était 0,085 fois moins bonne qu'à l'extérieur. De plus, la détection de la présence de poteaux de couleur argentée est 11 fois meilleure que celle de poteaux de couleur noir. / This research aimed to develop a technology system of mobility aid for Visually Impaired Person (VIP) called Smart Environment Explorer Stick (SEES).Particular purpose of this research was developing new algorithm in improving accuracy of SEE-stick for pole detection using distance calculation method and vertical line pair search based on Canny edge detection optimization and Hough transform. Henceforth, the pole detection algorithm was named as YuRHoS algorithm.The developed SEES as supporting system of VIP mobility aid had been successfully integrated several devices such as global remote server (iSEE), embedded local server (SEE-phone) and smart stick (SEE-stick). Performance of SEE-stick could be improved through YuRHoS algorithm, which was able to fix the accuracy of SEE-stick in detecting pole. Test comparison of pole detection results among others algorithm concluded that YuRHoS algorithm had better accuracy in pole detection.Two most significant factors affecting SEE-stick ability in detecting pole was test location and pole color. Level of accuracy of SEE-stick would be optimum once the test location was performed outdoor and pole color was silver. Statistics result shown that YuRHoS algorithm performance indoor was 0.085 times worse than outdoor. Meanwhile, silver-pole-color as object detection could increase YuRHoS algorithm performance as much as 11 times better compare to black-pole-color.