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A lightweight, powerful, yet efficient quad-rotor platform was designed and constructed to obtain experimental results of completely autonomous control of a hovering micro-UAV using a complete on-board vision system. The on-board vision and control system is composed of a Helios FPGA board, an Autonomous Vehicle Toolkit daughterboard, and a Kestrel Autopilot. The resulting platform is referred to as the Helio-copter. An efficient algorithm to detect, correlate, and track features in a scene and estimate attitude information was implemented with a combination of hardware and software on the FPGA, and real-time performance was obtained. The algorithms implemented include a Harris feature detector, template matching feature correlator, RANSAC similarity-constrained homography, color segmentation, radial distortion correction, and an extended Kalman filter with a standard-deviation outlier rejection technique (SORT). This implementation was designed specifically for use as an on-board vision solution in determining movement of small unmanned air vehicles that have size, weight, and power limitations. Experimental results show the Helio-copter capable of maintaining level, stable flight within a 6 foot by 6 foot area for over 40 seconds without human intervention.

The reason for the study is that IT-enabled organizational change processes such as information system implementations have high costs and disappointing results. Studies to identify causes of the mentioned failures are mainly based on a variance approach. This study applies another approach which is not yet performed in this field of research and affects several themes. Based on a process approach data is compared with ideal-process theories to identify the generative mechanisms causing the unfolding of the process. Thus, the study identifies a recipe and not the ingredients.

Travel-time information is essential in Advanced Traveler Information Systems (ATISs) and Advanced Traffic Management Systems (ATMSs). A key component of these systems is the prediction of the spatiotemporal evolution of roadway traffic state and travel time. From the perspective of travelers, such information can result in better traveler route choice and departure time decisions. From the transportation agency perspective, such data provide enhanced information with which to better manage and control the transportation system to reduce congestion, enhance safety, and reduce the carbon footprint of the transportation system. The objective of the research presented in this dissertation is to develop a framework that includes three major categories of methodologies to predict the spatiotemporal evolution of the traffic state. The proposed methodologies include macroscopic traffic modeling, computer vision and recursive probabilistic algorithms. Each developed method attempts to predict traffic state, including roadway travel times, for different prediction horizons. In total, the developed multi-tool framework produces traffic state prediction algorithms ranging from short – (0~5 minutes) to medium-term (1~4 hours) considering departure times up to an hour into the future. The dissertation first develops a particle filter approach for use in short-term traffic state prediction. The flow continuity equation is combined with the Van Aerde fundamental diagram to derive a time series model that can accurately describe the spatiotemporal evolution of traffic state. The developed model is applied within a particle filter approach to provide multi-step traffic state prediction. The testing of the algorithm on a simulated section of I-66 demonstrates that the proposed algorithm can accurately predict the propagation of shockwaves up to five minutes into the future. The developed algorithm is further improved by incorporating on- and off-ramp effects and more realistic boundary conditions. Furthermore, the case study demonstrates that the improved algorithm produces a 50 percent reduction in the prediction error compared to the classic LWR density formulation. Considering the fact that the prediction accuracy deteriorates significantly for longer prediction horizons, historical data are integrated and considered in the measurement update in the developed particle filter approach to extend the prediction horizon up to half an hour into the future. The dissertation then develops a travel time prediction framework using pattern recognition techniques to match historical data with real-time traffic conditions. The Euclidean distance is initially used as the measure of similarity between current and historical traffic patterns. This method is further improved using a dynamic template matching technique developed as part of this research effort. Unlike previous approaches, which use fixed template sizes, the proposed method uses a dynamic template size that is updated each time interval based on the spatiotemporal shape of the congestion upstream of a bottleneck. In addition, the computational cost is reduced using a Fast Fourier Transform instead of a Euclidean distance measure. Subsequently, the historical candidates that are similar to the current conditions are used to predict the experienced travel times. Test results demonstrate that the proposed dynamic template matching method produces significantly better and more stable prediction results for prediction horizons up to 30 minutes into the future for a two hour trip (prediction horizon of two and a half hours) compared to other state-of-the-practice and state-of-the-art methods. Finally, the dissertation develops recursive probabilistic approaches including particle filtering and agent-based modeling methods to predict travel times further into the future. Given the challenges in defining the particle filter time update process, the proposed particle filtering algorithm selects particles from a historical dataset and propagates particles using data trends of past experiences as opposed to using a state-transition model. A partial resampling strategy is then developed to address the degeneracy problem in the particle filtering process. INRIX probe data along I-64 and I-264 from Richmond to Virginia Beach are used to test the proposed algorithm. The results demonstrate that the particle filtering approach produces less than a 10 percent prediction error for trip departures up to one hour into the future for a two hour trip. Furthermore, the dissertation develops an agent-based modeling approach to predict travel times using real-time and historical spatiotemporal traffic data. At the microscopic level, each agent represents an expert in the decision making system, which predicts the travel time for each time interval according to past experiences from a historical dataset. A set of agent interactions are developed to preserve agents that correspond to traffic patterns similar to the real-time measurements and replace invalid agents or agents with negligible weights with new agents. Consequently, the aggregation of each agent's recommendation (predicted travel time with associated weight) provides a macroscopic level of output – predicted travel time distribution. The case study demonstrated that the agent-based model produces less than a 9 percent prediction error for prediction horizons up to one hour into the future. / Ph. D.

This thesis aims at proposing vehicle license plate detection and recognition algorithms, suitable for vehicle re-identification. Simple urban traffic analysis system is also proposed. Multiple stages of this system was developed and tested. Specifically - vehicle detection, license plate detection and recognition. Vehicle detection is based on background substraction method, which results in an average hit rate of ~92%. License plate detection is done by cascade classifiers and achieves an average hit rate of 81.92% and precision rate of 94.42%. License plate recognition based on Template matching results in an average precission rate of 60.55%. Therefore the new license plate recognition method based on license plate scanning using the sliding window principle and neural network recognition was introduced. Neural network achieves a precision rate of 64.47% for five input features. Low precision rate of neural network is caused by small amount of training sample for some specific license plate characters.

Este trabalho aborda o problema de casamento entre duas imagens. Casamento de imagens pode ser do tipo casamento de modelos (template matching) ou casamento de pontos-chaves (keypoint matching). Estes algoritmos localizam uma região da primeira imagem numa segunda imagem. Nosso grupo desenvolveu dois algoritmos de casamento de modelos invariante por rotação, escala e translação denominados Ciratefi (Circula, radial and template matchings filter) e Forapro (Fourier coefficients of radial and circular projection). As características positivas destes algoritmos são a invariância a mudanças de brilho/contraste e robustez a padrões repetitivos. Na primeira parte desta tese, tornamos Ciratefi invariante a transformações afins, obtendo Aciratefi (Affine-ciratefi). Construímos um banco de imagens para comparar este algoritmo com Asift (Affine-scale invariant feature transform) e Aforapro (Affine-forapro). Asift é considerado atualmente o melhor algoritmo de casamento de imagens invariante afim, e Aforapro foi proposto em nossa dissertação de mestrado. Nossos resultados sugerem que Aciratefi supera Asift na presença combinada de padrões repetitivos, mudanças de brilho/contraste e mudanças de pontos de vista. Na segunda parte desta tese, construímos um algoritmo para filtrar casamentos de pontos-chaves, baseado num conceito que denominamos de coerência geométrica. Aplicamos esta filtragem no bem-conhecido algoritmo Sift (scale invariant feature transform), base do Asift. Avaliamos a nossa proposta no banco de imagens de Mikolajczyk. As taxas de erro obtidas são significativamente menores que as do Sift original. / This work deals with image matching. Image matchings can be modeled as template matching or keypoints matching. These algorithms search a region of the first image in a second image. Our group has developed two template matching algorithms invariant by rotation, scale and translation called Ciratefi (circular, radial and template matching filter) and Forapro (Fourier coefficients of radial and circular projection). The positive characteristics of Ciratefi and Forapro are: the invariance to brightness/contrast changes and robustness to repetitive patterns. In the first part of this work, we make Ciratefi invariant to affine transformations, getting Aciratefi (Affine-ciratefi). We have built a dataset to compare Aciratefi with Asift (Affine-scale invariant feature transform) and Aforapro (Affine-forapro). Asift is currently considered the best affine invariant image matching algorithm, and Aforapro was proposed in our master\'s thesis. Our results suggest that Aciratefi overcome Asift in the combined presence of repetitive patterns, brightness/contrast and viewpoints changes. In the second part of this work, we filter keypoints matchings based on a concept that we call geometric coherence. We apply this filtering in the well-known algorithm Sift (scale invariant feature transform), the basis of Asift. We evaluate our proposal in the Mikolajczyk images database. The error rates obtained are significantly lower than those of the original Sift.

Reconhecimento de objetos é uma aplicação básica da área de processamento de imagens e visão computacional. O procedimento comum do reconhecimento consiste em achar ocorrências de uma imagem modelo numa outra imagem a ser analisada. Consequentemente, se as imagens apresentarem mudanças no ponto de vista da câmera o algoritmo normalmente falha. A invariância a pontos de vista é uma qualidade que permite reconhecer um objeto, mesmo que este apresente distorções resultantes de uma transformação em perspectiva causada pela mudança do ponto de vista. Uma abordagem baseada na simulação de pontos de vista, chamada ASIFT, tem sido recentemente proposta no entorno desta problemática. O ASIFT é invariante a pontos de vista, no entanto falha na presença de padrões repetitivos e baixo contraste. O objetivo de nosso trabalho é utilizar uma variante da técnica de simulação de pontos de vista em combinação com a técnica de extração dos coeficientes de Fourier de projeções radiais e circulares (FORAPRO), para propor um algoritmo invariante a pontos de vista, e robusto a padrões repetitivos e baixo contraste. De maneira geral, a nossa proposta resume-se nas seguintes fases: (a) Distorcemos a imagem, variando os parâmetros de inclinação e rotação da câmera, para gerar alguns modelos e conseguir a invariância a deformações em perspectiva, (b) utilizamos cada como modelo a ser procurado na imagem, para escolher o que melhor case, (c) realizamos o casamento de padrões. As duas últimas fases do processo baseiam-se em características invariantes por rotação, escala, brilho e contraste extraídas pelos coeficientes de Fourier. Nossa proposta, que chamamos AFORAPRO, foi testada com 350 imagens que continham diversidade nos requerimentos, e demonstrou ser invariante a pontos de vista e ter ótimo desempenho na presença de padrões repetitivos e baixo contraste. / Object recognition is a basic application from the domain of image processing and computer vision. The common process recognition consists of finding occurrences of an image query in another image to be analyzed A. Consequently, if the images changes viewpoint in the camera it will normally result in the algorithm failure. The invariance viewpoints are qualities that permit recognition of an object, even if this present distortion resultant of a transformation of perspective is caused by the change in viewpoint. An approach based on viewpoint simulation, called ASIFT, has recently been proposed surrounding this issue. The ASIFT algorithm is invariant viewpoints; however there are flaws in the presence of repetitive patterns and low contrast. The objective of our work is to use a variant of this technique of viewpoint simulating, in combination with the technique of extraction of the Coefficients of Fourier Projections Radials and Circulars (FORAPRO), and to propose an algorithm of invariant viewpoints and robust repetitive patterns and low contrast. In general, our proposal summarizes the following stages: (a) We distort the image, varying the parameters of inclination and rotation of the camera, to produce some models and achieve perspective invariance deformation, (b) use as the model to be search in the image, to choose the that match best, (c) realize the template matching. The two last stages of process are based on invariant features by images rotation, scale, brightness and contrast extracted by Fourier coefficients. Our approach, that we call AFORAPRO, was tested with 350 images that contained diversity in applications, and demonstrated to have invariant viewpoints, and to have excellent performance in the presence of patterns repetitive and low contrast.

Este trabalho aborda o problema de casamento entre duas imagens. Casamento de imagens pode ser do tipo casamento de modelos (template matching) ou casamento de pontos-chaves (keypoint matching). Estes algoritmos localizam uma região da primeira imagem numa segunda imagem. Nosso grupo desenvolveu dois algoritmos de casamento de modelos invariante por rotação, escala e translação denominados Ciratefi (Circula, radial and template matchings filter) e Forapro (Fourier coefficients of radial and circular projection). As características positivas destes algoritmos são a invariância a mudanças de brilho/contraste e robustez a padrões repetitivos. Na primeira parte desta tese, tornamos Ciratefi invariante a transformações afins, obtendo Aciratefi (Affine-ciratefi). Construímos um banco de imagens para comparar este algoritmo com Asift (Affine-scale invariant feature transform) e Aforapro (Affine-forapro). Asift é considerado atualmente o melhor algoritmo de casamento de imagens invariante afim, e Aforapro foi proposto em nossa dissertação de mestrado. Nossos resultados sugerem que Aciratefi supera Asift na presença combinada de padrões repetitivos, mudanças de brilho/contraste e mudanças de pontos de vista. Na segunda parte desta tese, construímos um algoritmo para filtrar casamentos de pontos-chaves, baseado num conceito que denominamos de coerência geométrica. Aplicamos esta filtragem no bem-conhecido algoritmo Sift (scale invariant feature transform), base do Asift. Avaliamos a nossa proposta no banco de imagens de Mikolajczyk. As taxas de erro obtidas são significativamente menores que as do Sift original. / This work deals with image matching. Image matchings can be modeled as template matching or keypoints matching. These algorithms search a region of the first image in a second image. Our group has developed two template matching algorithms invariant by rotation, scale and translation called Ciratefi (circular, radial and template matching filter) and Forapro (Fourier coefficients of radial and circular projection). The positive characteristics of Ciratefi and Forapro are: the invariance to brightness/contrast changes and robustness to repetitive patterns. In the first part of this work, we make Ciratefi invariant to affine transformations, getting Aciratefi (Affine-ciratefi). We have built a dataset to compare Aciratefi with Asift (Affine-scale invariant feature transform) and Aforapro (Affine-forapro). Asift is currently considered the best affine invariant image matching algorithm, and Aforapro was proposed in our master\'s thesis. Our results suggest that Aciratefi overcome Asift in the combined presence of repetitive patterns, brightness/contrast and viewpoints changes. In the second part of this work, we filter keypoints matchings based on a concept that we call geometric coherence. We apply this filtering in the well-known algorithm Sift (scale invariant feature transform), the basis of Asift. We evaluate our proposal in the Mikolajczyk images database. The error rates obtained are significantly lower than those of the original Sift.

Reconhecimento de objetos é uma aplicação básica da área de processamento de imagens e visão computacional. O procedimento comum do reconhecimento consiste em achar ocorrências de uma imagem modelo numa outra imagem a ser analisada. Consequentemente, se as imagens apresentarem mudanças no ponto de vista da câmera o algoritmo normalmente falha. A invariância a pontos de vista é uma qualidade que permite reconhecer um objeto, mesmo que este apresente distorções resultantes de uma transformação em perspectiva causada pela mudança do ponto de vista. Uma abordagem baseada na simulação de pontos de vista, chamada ASIFT, tem sido recentemente proposta no entorno desta problemática. O ASIFT é invariante a pontos de vista, no entanto falha na presença de padrões repetitivos e baixo contraste. O objetivo de nosso trabalho é utilizar uma variante da técnica de simulação de pontos de vista em combinação com a técnica de extração dos coeficientes de Fourier de projeções radiais e circulares (FORAPRO), para propor um algoritmo invariante a pontos de vista, e robusto a padrões repetitivos e baixo contraste. De maneira geral, a nossa proposta resume-se nas seguintes fases: (a) Distorcemos a imagem, variando os parâmetros de inclinação e rotação da câmera, para gerar alguns modelos e conseguir a invariância a deformações em perspectiva, (b) utilizamos cada como modelo a ser procurado na imagem, para escolher o que melhor case, (c) realizamos o casamento de padrões. As duas últimas fases do processo baseiam-se em características invariantes por rotação, escala, brilho e contraste extraídas pelos coeficientes de Fourier. Nossa proposta, que chamamos AFORAPRO, foi testada com 350 imagens que continham diversidade nos requerimentos, e demonstrou ser invariante a pontos de vista e ter ótimo desempenho na presença de padrões repetitivos e baixo contraste. / Object recognition is a basic application from the domain of image processing and computer vision. The common process recognition consists of finding occurrences of an image query in another image to be analyzed A. Consequently, if the images changes viewpoint in the camera it will normally result in the algorithm failure. The invariance viewpoints are qualities that permit recognition of an object, even if this present distortion resultant of a transformation of perspective is caused by the change in viewpoint. An approach based on viewpoint simulation, called ASIFT, has recently been proposed surrounding this issue. The ASIFT algorithm is invariant viewpoints; however there are flaws in the presence of repetitive patterns and low contrast. The objective of our work is to use a variant of this technique of viewpoint simulating, in combination with the technique of extraction of the Coefficients of Fourier Projections Radials and Circulars (FORAPRO), and to propose an algorithm of invariant viewpoints and robust repetitive patterns and low contrast. In general, our proposal summarizes the following stages: (a) We distort the image, varying the parameters of inclination and rotation of the camera, to produce some models and achieve perspective invariance deformation, (b) use as the model to be search in the image, to choose the that match best, (c) realize the template matching. The two last stages of process are based on invariant features by images rotation, scale, brightness and contrast extracted by Fourier coefficients. Our approach, that we call AFORAPRO, was tested with 350 images that contained diversity in applications, and demonstrated to have invariant viewpoints, and to have excellent performance in the presence of patterns repetitive and low contrast.

With the improvement of multimedia technologies such as broadband-enabled HDTV, video on demand and internet TV, the computer and the TV are merging to become a single device. Moreover the previously cited technologies as well as DVD or Blu-ray can provide menu navigation and interactive content. The growing interest in video conferencing led to the integration of the webcam in different devices such as laptop, cell phones and even the TV set. Our approach is to directly use an embedded webcam to remotely control a TV set using hand gestures. Using specific gestures, a user is able to control the TV. A dedicated interface can then be used to select a TV channel, adjust volume or browse videos from an online streaming server. This approach leads to several challenges. The first is the use of a simple webcam which leads to a vision based system. From the single webcam, we need to recognize the hand and identify its gesture or trajectory. A TV set is usually installed in a living room which implies constraints such as a potentially moving background and luminance change. These issues will be further discussed as well as the methods developed to resolve them. Video browsing is one example of the use of gesture recognition. To illustrate another application, we developed a simple game controlled by hand gestures. The emergence of 3D TVs is allowing the development of 3D video conferencing. Therefore we also consider the use of a stereo camera to recognize hand gesture.