Tesis sobre el tema "Outdoor vision and weather"
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CROCI, ALBERTO. "A novel approach to rainfall measuring: methodology, field test and business opportunity". Doctoral thesis, Politecnico di Torino, 2017. http://hdl.handle.net/11583/2677708.
Texto completoAsmar, Daniel. "Vision-Inertial SLAM using Natural Features in Outdoor Environments". Thesis, University of Waterloo, 2006. http://hdl.handle.net/10012/2843.
Texto completoThe above issues are addressed as follows. Firstly, a camera is used to recognize the environmental context (e. g. , indoor office, outdoor park) by analyzing the holistic spectral content of images of the robot's surroundings. A type of feature (e. g. , trees for a park) is then chosen for SLAM that is likely observable in the recognized setting. A novel tree detection system is introduced, which is based on perceptually organizing the content of images into quasi-vertical structures and marking those structures that intersect ground level as tree trunks. Secondly, a new tree recognition system is proposed, which is based on extracting Scale Invariant Feature Transform (SIFT) features on each tree trunk region and matching trees in feature space. Thirdly, dead-reckoning is performed via an Inertial Navigation System (INS), bounded by non-holonomic constraints. INS are insensitive to slippage and varying ground conditions. Finally, the developed Computer Vision and Inertial systems are integrated within the framework of an Extended Kalman Filter into a working Vision-INS SLAM system, named VisSLAM.
VisSLAM is tested on data collected during a real test run in an outdoor unstructured environment. Three test scenarios are proposed, ranging from semi-automatic detection, recognition, and initialization to a fully automated SLAM system. The first two scenarios are used to verify the presented inertial and Computer Vision algorithms in the context of localization, where results indicate accurate vehicle pose estimation for the majority of its journey. The final scenario evaluates the application of the proposed systems for SLAM, where results indicate successful operation for a long portion of the vehicle journey. Although the scope of this thesis is to operate in an outdoor park setting using tree trunks as landmarks, the developed techniques lend themselves to other environments using different natural objects as landmarks.
Catchpole, Jason James. "Adaptive Vision Based Scene Registration for Outdoor Augmented Reality". The University of Waikato, 2008. http://hdl.handle.net/10289/2581.
Texto completoAhmed, Maryum F. "Development of a stereo vision system for outdoor mobile robots". [Gainesville, Fla.] : University of Florida, 2006. http://purl.fcla.edu/fcla/etd/UFE0016205.
Texto completoLin, Li-Heng. "Enhanced stereo vision SLAM for outdoor heavy machine rotation sensing". Thesis, University of British Columbia, 2010. http://hdl.handle.net/2429/25966.
Texto completoAlamgir, Nyma. "Computer vision based smoke and fire detection for outdoor environments". Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/201654/1/Nyma_Alamgir_Thesis.pdf.
Texto completoWilliams, Samuel Grant Dawson. "Real-Time Hybrid Tracking for Outdoor Augmented Reality". Thesis, University of Canterbury. Computer Science and Software Engineering, 2014. http://hdl.handle.net/10092/9188.
Texto completoSchreiber, Michael J. "Outdoor tracking using computer vision, xenon strobe illumination and retro-reflective landmarks". Diss., Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/18940.
Texto completoRosenquist, Calle y Andreas Evesson. "Visual Servoing In Semi-Structured Outdoor Environments". Thesis, Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-653.
Texto completoThe field of autonomous vehicle navigation and localization is a highly active research
topic. The aim of this thesis is to evaluate the feasibility to use outdoor visual navigation in a semi-structured environment. The goal is to develop a visual navigation system for an autonomous golf ball collection vehicle operating on driving ranges.
The image feature extractors SIFT and PCA-SIFT was evaluated on an image database
consisting of images acquired from 19 outdoor locations over a period of several weeks to
allow different environmental conditions. The results from these tests show that SIFT-type
feature extractors are able to find and match image features with high accuracy. The results also show that this can be improved further by a combination of a lower nearest neighbour threshold and an outlier rejection method to allow more matches and a higher ratio of correct matches. Outliers were found and rejected by fitting the data to a homography model with the RANSAC robust estimator algorithm.
A simulator was developed to evaluate the suggested system with respect to pixel noise from illumination changes, weather and feature position accuracy as well as the distance to features, path shapes and the visual servoing target image (milestone) interval. The system was evaluated on a total of 3 paths, 40 test combinations and 137km driven. The results show that with the relatively simple visual servoing navigation system it is possible to use mono-vision as a sole sensor and navigate semi-structured outdoor environments such as driving ranges.
Linegar, Chris. "Vision-only localisation under extreme appearance change". Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:608762bd-5608-4e50-ab7b-da454dd52887.
Texto completoEriksson, Ida. "Idrottslärares syn på friluftslivsundervisningen i skolan". Thesis, Karlstads universitet, Fakulteten för hälsa, natur- och teknikvetenskap (from 2013), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-31776.
Texto completoOutdoor education covers a large part of the core content of Lgr11 in physical education. The purpose of the survey is to get a picture of a group of PE teachers' views on, and visions with outdoor education in school. I´m also interested in how, in fact, the reality looks like and what teachers believe is needed to visions will be achieved. Therefore the questions will be: How do outdoor education looks like in school today, according to the teachers? What is the teacher's vision of outdoor education? What´s needed so the vision will be achieved according to the teachers? The survey was conducted by four semi-structured interviews with PE teachers working at high school. These were then transcribed and the result of their beliefs is that outdoor education in school differs from their vision. The outdoor education that is available in schools today is usually linked to a major project such as sports days and it results in point actions. The overall picture of the teachers´ vision is to reduce point actions while still having the ability to take the students on longer trips where they can try to go skiing, hiking, fishing, camping etc. In order for the vision to become reality it requires time, money and materials according to the respondents, as previous research also shows.
El, Natour Ghina. "Towards 3D reconstruction of outdoor scenes by mmw radar and a vision sensor fusion". Thesis, Clermont-Ferrand 2, 2016. http://www.theses.fr/2016CLF22773/document.
Texto completoThe main goal of this PhD work is to develop 3D mapping methods of large scale environment by combining panoramic radar and cameras. Unlike existing sensor fusion methods, such as SLAM (simultaneous localization and mapping), we want to build a RGB-D sensor which directly provides depth measurement enhanced with texture and color information. After modeling the geometry of the radar/camera system, we propose a novel calibration method using points correspondences. To obtain these points correspondences, we designed special targets allowing accurate point detection by both the radar and the camera. The proposed approach has been developed to be implemented by non-expert operators and in unconstrained environment. Secondly, a 3D reconstruction method is elaborated based on radar data and image point correspondences. A theoretical analysis is done to study the influence of the uncertainty zone of each sensor on the reconstruction method. This theoretical study, together with the experimental results, show that the proposed method outperforms the conventional stereoscopic triangulation for large scale outdoor scenes. Finally, we propose an efficient strategy for automatic data matching. This strategy uses two calibrated cameras. Taking into account the heterogeneity of cameras and radar data, the developed algorithm starts by segmenting the radar data into polygonal regions. The calibration process allows the restriction of the search by defining a region of interest in the pair of images. A similarity criterion based on both cross correlation and epipolar constraint is applied in order to validate or reject region pairs. While the similarity test is not met, the image regions are re-segmented iteratively into polygonal regions, generating thereby a shortlist of candidate matches. This process promotes the matching of large regions first which allows obtaining maps with locally dense patches. The proposed methods were tested on both synthetic and real experimental data. The results are encouraging and prove the feasibility of radar and vision sensor fusion for the 3D mapping of large scale urban environment
Botero, galeano Diego andres. "Development of algorithms and architectures for driving assistance in adverse weather conditions using FPGAs". Thesis, Toulouse, INSA, 2012. http://www.theses.fr/2012ISAT0062/document.
Texto completoDue to the increase of traffic volume and complexity of new transport systems, new Advanced Driver Assistance Systems (ADAS) are a subject of research of many companies, laboratories and universities. These systems include algorithms with techniques that have been studied during the last decades like Simultaneous Lo- calization and Mapping (SLAM), obstacle detection, stereo vision, etc. Thanks to the advances in electronics, robotics and other domains, new embedded systems are being developed to guarantee the safety of the users of these critical systems. For most of these systems a low power consumption as well as reduced size is required. It creates the constraint of execute the algorithms in embedded devices with limited resources. In most of algorithms, moreover for computer vision ones, a big amount of data must be processed at high frequencies, this amount of data demands strong computing resources. FPGAs satisfy this requirement; its parallel architecture combined with its low power consumption and exibility allows developing and executing some algorithms more efficiently than any other processing platforms. In this thesis different embedded computer vision architectures intended to be used in ADAS using FPGAs are presented such as: We present the implementation of a distortion correction architecture operating at 100 Hz in two cameras simultaneously. The correction module allows also to rectify two images for implementation of stereo vision. Obstacle detection algorithms based on Inverse Perspective Mapping (IPM) and classiffication based on Color/Texture attributes are presented. The IPM transform is based in the perspective effect of a scene perceived from two different points of view. Moreover results of the detection algorithms from color/texture attributes applied on a multi-cameras system, are fused in an occupancy grid. An accelerator to apply homographies on images, is presented; this accelerator can be used for different applications like the generation of Bird's eye view or Side view. Multispectral vision is studied using both infrared images and color ones. Syn- thetic images are generated from information acquired from visible and infrared sources to provide a visual aid to the driver. Image enhancement specific for infrared images is also implemented and evaluated, based on the Contrast Lim- ited Adaptive Histogram Equalization (CLAHE). An embedded SLAM algorithm is presented with different hardware acceler- ators (point detection, landmark tracking, active search, correlation, matrix operations). All the algorithms were simulated, implemented and verified using as target FPGAs. The validation was done using development kits. A custom board integrating all the presented algorithms is presented. Virtual components developed in this thesis were used in three different projects: PICASSO (stereo vision), COMMROB (obstacle detection from a multi-cameras system) and SART (multispectral vision)
Shi, Jie. "Obstacle detection using thermal imaging sensors for large passenger airplane". Thesis, Cranfield University, 2012. http://dspace.lib.cranfield.ac.uk/handle/1826/7944.
Texto completoSadel, Juraj. "Rozpoznání počasí z kamerových snímků/sekvencí". Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442513.
Texto completoBotero-Galeano, Diego. "Development of algorithms and architectures for driving assistance in adverse weather conditions using FPGAs". Phd thesis, INSA de Toulouse, 2012. http://tel.archives-ouvertes.fr/tel-00771869.
Texto completoKakarlapudi, Swarna. "APPLICATION OF IMAGE ANALYSIS TECHNIQUES IN FORWARD LOOKING SYNTHETIC VISION SYSTEM INTEGRITY MONITORS". Ohio University / OhioLINK, 2004. http://www.ohiolink.edu/etd/view.cgi?ohiou1090265512.
Texto completoBlanco, Myra. "Relationship Between Driver Characteristics, Nighttime Driving Risk Perception, and Visual Performance under Adverse and Clear Weather Conditions and Different Vision Enhancement Systems". Diss., Virginia Tech, 2002. http://hdl.handle.net/10919/27806.
Texto completoPh. D.
Öberg, Johan y Erik Wiege. "Moisture risks with CLT-panels subjected to outdoor climate during construction : focus on mould and wetting processes". Thesis, KTH, Byggteknik och design, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-231115.
Texto completoNär man går igenom relevant forskning, riktlinjer för fuktsäkerhet och pratar med byggare, fuktexperter och arkitekter är det tydligt - och inte överraskande - att vatten och trä inte är någon enkel kombination. Erfarenheterna från att bygga med korslimmat trä (KL-trä) skiljer sig från byggarbetsplatser och det finns bra och dåliga exempel från byggande utan väderskydd. I denna studie analyseras fuktpåverkan på KL-trä. KL-trä är en typ av massivt trä med limmade lameller, som ökar i användningen över hela världen som strukturella element i byggnader. Huvuddelen av arbetet utförs i det hygrotermiska beräkningsverktyget WUFI (™). Fokus ligger på uppfuktning och utvärdering av mögelrisker från regnbelastning under produktion i nordiskt klimat. Efterföljande torkning efter inbyggnad i väggar och golv utvärderas också. En omfattande litteraturstudie utförs för att jämföra och välja materialdata för modellering av KL-trä. Efter simuleringsarbetet bedöms fuktinnehåll, mögeltillväxt och fuktrörelser både med och utan väderskydd. Resultaten jämförs också med mätdata från fältförsök. Det konstateras att korta byggtider är avgörande, någon form av väderskydd krävs året runt och tidig planering och konstruktion för fuktsäkerhet är avgörande. Fördelarna med prefabricering och korta byggtider med KL-trä bör utnyttjas. Om det finns risk för nederbörd över 10-20 mm bör åtgärder vidtas för att avleda regn. Om de förväntade regnbelastningarna är över 40 mm eller om byggtiden överstiger 2 veckor krävs ett regnskydd. Vid luftfuktigheter på i medeltal 80 % och årligt regn över 1200 mm rekommenderas ett väderskydd runt hela byggnaden. En kontrollerad miljö kan vara dyr, men det påskyndar produktionen och förkortar torkningstiden.
Velandia, Henry Roncancio. "Object detection and classication in outdoor environments for autonomous passenger vehicle navigation based on Data Fusion of Articial Vision System and LiDAR sensor". Universidade de São Paulo, 2014. http://www.teses.usp.br/teses/disponiveis/18/18149/tde-24072016-152124/.
Texto completoEste projeto de pesquisa fez parte do projeto SENA (Sistema Embarcado de Navegação Autônoma), ele foi realizado no Laboratório de Robótica Móvel do Grupo de Mecatrônica da Escola de Engenharia de São Carlos (EESC), em colaboração com o Instituto de Física de São Carlos (IFSC). A grande motivação do projeto SENA é o desenvolvimento de tecnologias assistidas e autônomas que possam atender às necessidades de diferentes tipos de motoristas (inexperientes, idosos, portadores de limitações, etc.). Vislumbra-se que a aplicação em larga escala desse tipo de tecnologia, em um futuro próximo, certamente reduzirá drasticamente a quantidade de pessoas feridas e mortas em acidentes automobilísticos em estradas e em ambientes urbanos. Nesse contexto, este projeto de pesquisa teve como objetivo proporcionar informações relativas ao ambiente ao redor do veículo, ao sistema de controle e de tomada de decisão embarcado no veículo autônomo. As informações mais básicas fornecidas são as posições dos objetos (obstáculos) ao redor do veículo; além disso, informações como o tipo de objeto (ou seja, sua classificação em carros, pedestres, postes e a própria rua mesma), assim como o tamanho deles. Os dados do ambiente são adquiridos através do emprego de uma câmera e um Velodyne LiDAR. Um estudo do tipo ceiling foi usado para simular a metodologia da detecção dos obstáculos. Estima-se que , após realizar o estudo, que analisar regiões especificas da imagem, chamadas de regiões de interesse, onde é mais provável encontrar um obstáculo, é o melhor jeito de melhorar o sistema de reconhecimento. Observou-se na implementação da fusão dos sensores que encontrar regiões de interesse usando LiDAR, e classificá-las usando visão artificial fornece um melhor resultado na hora de compará-lo com os resultados ao usar apenas câmera ou LiDAR. Obteve-se uma classificação com precisão de 100% para pedestres e 92,3% para carros, rodando em uma frequência de 6 Hz. A fusão dos sensores também forneceu um método para estimar a estrada mesmo quando esta tinha sombra ou faixas de cor. Em geral, a classificação baseada em visão artificial e LiDAR mostrou uma solução para detecção de objetos em várias escalas e mesmo para o problema da iluminação não uniforme do ambiente.
Riffel, Alvin Daniel. "Effects of a dialogical argumentation based instruction on grade 9 learners' conceptions of a meteorological concept: Cold Fronts in the Western Cape, South Africa". Thesis, University of Western Cape, 2012. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=gen8Srv25Nme4_6262_1384164748.
Texto completo 
mixed methods&rsquo
) to collect data in a public secondary school in Cape Town, in the Western Cape Province. A survey questionnaire on attitudes and perceptions towards high school as well as conceptions of weather was administered before the main study to give the researcher baseline information and to develop pilot instruments to use in the main study.
The study employed a dialogical instructional model (DAIM) with an experimental group of learners exposed to the intervention, and recorded differences from a control group which had no intervention. Learners from the two groups were exposed to a meteorological literacy test evaluation before and after the DAIM intervention. The results from the two groups were then compared and analysed according to the two theoretical frameworks that underpin the study namely: Toulmin&rsquo
s Argumentation Pattern - TAP (Toulmin, 1958) and Contiguity Argumentation Theory - CAT (Ogunniyi, 1997).
Further analyses were conducted on learners&rsquo
beliefs and indigenous knowledge, according to their conceptual understanding of weather related concepts used in the current NCS (National Curriculum Statement). 
After completing the study some interesting findings were made and based on these findings certain recommendations were suggested on how to implement a DAIM-model into classroom teaching using Indigenous Knowledge (IK). These recommendations are suggestions to plot the way towards developing a science&ndash
IK curriculum for the Natural Sciences subjects in South African schools.
Moohialdin, Ammar. "A real-time worker activity intensity identification system for construction workers under hot and humid weather conditions". Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/206448/1/Ammar_Moohialdin_Thesis.pdf.
Texto completoDuthon, Pierre. "Descripteurs d'images pour les systèmes de vision routiers en situations atmosphériques dégradées et caractérisation des hydrométéores". Thesis, Université Clermont Auvergne (2017-2020), 2017. http://www.theses.fr/2017CLFAC065/document.
Texto completoComputer vision systems are increasingly being used on roads. They can be installed along infrastructure for traffic monitoring purposes. When mounted in vehicles, they perform driver assistance functions. In both cases, computer vision systems enhance road safety and streamline travel.A literature review starts by retracing the introduction and rollout of computer vision algorithms in road environments, and goes on to demonstrate the importance of image descriptors in the processing chains implemented in such algorithms. It continues with a review of image descriptors from a novel approach, considering them in parallel with final applications, which opens up numerous analytical angles. Finally the literature review makes it possible to assess which descriptors are the most representative in road environments.Several databases containing images and associated meteorological data (e.g. rain, fog) are then presented. These databases are completely original because image acquisition and weather condition measurement are at the same location and the same time. Moreover, calibrated meteorological sensors are used. Each database contains different scenes (e.g. black and white target, pedestrian) and different kind of weather (i.e. rain, fog, daytime, night-time). Databases contain digitally simulated, artificial and natural weather conditions.Seven of the most representative image descriptors in road context are then selected and their robustness in rainy conditions is evaluated. Image descriptors based on pixel intensity and those that use vertical edges are sensitive to rainy conditions. Conversely, the Harris feature and features that combine different edge orientations remain robust for rainfall rates ranging in 0 – 30 mm/h. The robustness of image features in rainy conditions decreases as the rainfall rate increases. Finally, the image descriptors most sensitive to rain have potential for use in a camera-based rain classification application.The image descriptor behaviour in adverse weather conditions is not necessarily related to the associated final function one. Thus, two pedestrian detectors were assessed in degraded weather conditions (rain, fog, daytime, night-time). Night-time and fog are the conditions that have the greatest impact on pedestrian detection. The methodology developed and associated database could be reused to assess others final functions (e.g. vehicle detection, traffic sign detection).In road environments, real-time knowledge of local weather conditions is an essential prerequisite for addressing the twin challenges of enhancing road safety and streamlining travel. Currently, the only mean of quantifying weather conditions along a road network requires the installation of meteorological stations. Such stations are costly and must be maintained; however, large numbers of cameras are already installed on the roadside. A new method that uses road traffic cameras to detect weather conditions has therefore been proposed. This method uses a combination of a neural network and image descriptors applied to image patches. It addresses a clearly defined set of constraints relating to the ability to operate in real-time and to classify the full spectrum of meteorological conditions and grades them according to their intensity. The method differentiates between normal daytime, rain, fog and normal night-time weather conditions. After several optimisation steps, the proposed method obtains better results than the ones reported in the literature for comparable algorithms
Williams, Stephen Vincent. "Visual arctic navigation: techniques for autonomous agents in glacial environments". Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/41135.
Texto completoDahmane, Khouloud. "Analyse d'images par méthode de Deep Learning appliquée au contexte routier en conditions météorologiques dégradées". Thesis, Université Clermont Auvergne (2017-2020), 2020. http://www.theses.fr/2020CLFAC020.
Texto completoNowadays, vision systems are becoming more and more used in the road context. They ensure safety and facilitate mobility. These vision systems are generally affected by the degradation of weather conditions, like heavy fog or strong rain, phenomena limiting the visibility and thus reducing the quality of the images. In order to optimize the performance of the vision systems, it is necessary to have a reliable detection system for these adverse weather conditions.There are meteorological sensors dedicated to physical measurement, but they are expensive. Since cameras are already installed on the road, they can simultaneously perform two functions: image acquisition for surveillance applications and physical measurement of weather conditions instead of dedicated sensors. Following the great success of convolutional neural networks (CNN) in classification and image recognition, we used a deep learning method to study the problem of meteorological classification. The objective of our study is to first seek to develop a classifier of time, which discriminates between "normal" conditions, fog and rain. In a second step, once the class is known, we seek to develop a model for measuring meteorological visibility.The use of CNN requires the use of train and test databases. For this, two databases were used, "Cerema-AWP database" (https://ceremadlcfmds.wixsite.com/cerema-databases), and the "Cerema-AWH database", which has been acquired since 2017 on the Fageole site on the highway A75. Each image of the two bases is labeled automatically thanks to meteorological data collected on the site to characterize various levels of precipitation for rain and fog.The Cerema-AWH base, which was set up as part of our work, contains 5 sub-bases: normal day conditions, heavy fog, light fog, heavy rain and light rain. Rainfall intensities range from 0 mm/h to 70mm/h and fog weather visibilities range from 50m to 1800m. Among the known neural networks that have demonstrated their performance in the field of recognition and classification, we can cite LeNet, ResNet-152, Inception-v4 and DenseNet-121. We have applied these networks in our adverse weather classification system. We start by the study of the use of convolutional neural networks. The nature of the input data and the optimal hyper-parameters that must be used to achieve the best results. An analysis of the different components of a neural network is done by constructing an instrumental neural network architecture. The conclusions drawn from this analysis show that we must use deep neural networks. This type of network is able to classify five meteorological classes of Cerema-AWH base with a classification score of 83% and three meteorological classes with a score of 99%Then, an analysis of the input and output data was made to study the impact of scenes change, the input's data and the meteorological classes number on the classification result.Finally, a database transfer method is developed. We study the portability from one site to another of our adverse weather conditions classification system. A classification score of 63% by making a transfer between a public database and Cerema-AWH database is obtained.After the classification, the second step of our study is to measure the meteorological visibility of the fog. For this, we use a neural network that generates continuous values. Two fog variants were tested: light and heavy fog combined and heavy fog (road fog) only. The evaluation of the result is done using a correlation coefficient R² between the real values and the predicted values. We compare this coefficient with the correlation coefficient between the two sensors used to measure the weather visibility on site. Among the results obtained and more specifically for road fog, the correlation coefficient reaches a value of 0.74 which is close to the physical sensors value (0.76)
Bri, Molinero Diana. "ESTUDIO DEL EFECTO DE FACTORES EXTERNOS SOBRE LAS REDES WLAN Y DISEÑO DE UN ALGORITMO COGNITIVO ENERGÉTICAMENTE EFICIENTE". Doctoral thesis, Universitat Politècnica de València, 2015. http://hdl.handle.net/10251/53450.
Texto completo[ES] Hoy en día existen muchos trabajos que analizan e intentan mejorar el rendimiento de las redes de área local inalámbricas desde diferentes perspectivas. Gran parte de estos trabajos se centran en aspectos de diseño, como son la distribución de frecuencias o la asignación de canales. Por lo tanto, como estos aspectos ya han sido ampliamente estudiados, los esfuerzos de esta tesis se han dirigido a estudiar otros factores que también podrían afectar a su rendimiento y que no han sido analizadas en profundidad todavía. El objetivo principal de esta tesis doctoral ha sido realizar un estudio detallado que analice el impacto de las condiciones meteorológicas sobre el rendimiento de las redes IEEE 802.11b/g. Para realizar este estudio, se han analizado dos escenarios reales con el fin de verificar los resultados y encontrar relaciones precisas. A partir de las conclusiones de estos análisis previos, el segundo objetivo ha sido diseñar un algoritmo cognitivo que, en base a las condiciones meteorológicas y a los parámetros de rendimiento de red, permita a las redes ajustar sus características de transmisión con el fin de superar tal impacto. Con el fin de llevar a cabo este estudio, primero fue necesario estudiar qué métodos estadísticos podían ser utilizados para extraer el nivel de correlación entre los parámetros de rendimiento de las redes y las condiciones meteorológicas del entorno. En segundo lugar, se tuvo que analizar qué parámetros de rendimiento de red se podían extraer de la red exterior de la UPV y seleccionarlos de acuerdo con el objetivo perseguido. A continuación, se definió el periodo de tiempo durante el cual se almacenarían los parámetros seleccionados de forma periódica. El siguiente paso fue seleccionar y almacenar las condiciones meteorológicas de una estación cercana durante el mismo periodo de tiempo. Finalmente, se realizó un preprocesado detallado con el fin de poner en orden todo el volumen de datos y se analizaron estadísticamente. Los resultados fueron exitosos, sin embargo aparecieron varios problemas por el hecho de estudiar una red real muy variable. Por lo tanto, se tuvo que desarrollar un escenario experimental con el fin de verificar los resultados. Para ello se diseñó y desarrolló un enlace exterior IEEE 802.11b/g punto a multipunto, y se analizó de nuevo el impacto de las condiciones meteorológicas. Se consideró un enlace multipunto para analizar también cómo influía el impacto del tiempo según la distancia y los diferentes esquemas de modulación. A partir de los resultados, se diseñó un algoritmo cognitivo energéticamente eficiente con el fin de reducir el impacto de los fenómenos meteorológicos en las redes IEEE 802.11b/g. Dicho algoritmo ha sido simulado y los resultados obtenidos han sido satisfactorios, tanto en términos de eficiencia energética como de rendimiento de la red. Para concluir, otro factor externo que se ha estudiado en esta tesis ha sido la tasa de absorción específica. Este parámetro está relacionado con una de las grandes preocupaciones actuales en cuanto a salud pública, ya que se utiliza para medir la exposición de los tejidos del cuerpo humano a los campos electromagnéticos. Obviamente, la absorción de señal por parte del cuerpo humano afecta a las redes y, por lo tanto, este parámetro se debería tener en cuenta a la hora de diseñar redes eficientes. Por esta razón se ha incluido en esta tesis doctoral.
[CAT] Actualment hi ha molts treballs que analitzen i intenten millorar el rendiment de les xarxes d'àrea local sense fils des de diferents perspectives. Gran part d'aquests treballs es focalitzen en aspectes de disseny, com són la distribució de freqüències o l'assignació de canals. Per tant, com aquests aspectes ja han sigut àmpliament estudiats, els esforços d'aquesta tesi doctoral s'han dirigit a estudiar altres factors que també podrien afectar al seu rendiment i que encara no han sigut analitzats amb profunditat. L'objectiu principal d'aquesta tesi doctoral ha sigut realitzar un estudi minuciós per analitzar l'impacte de les condicions meteorològiques sobre el rendiment de les xarxes IEEE 802.11b/g. Per a realitzar aquest estudi s'han analitzat dos escenaris reals per tal de verificar els resultats i trobar relacions precises. A partir de les conclusions d'aquests anàlisis previ, el següent objectiu ha sigut dissenyar un algoritme cognitiu que, en base a les condicions meteorològiques i als paràmetres de rendiment de la xarxa, permeti a les xarxes ajustar les seues característiques de transmissió per tal de superar tal impacte. Per tal de dur a terme aquest estudi, primer va ser necessari estudiar quins mètodes estadístics podien ser utilitzats per extraure el nivell de correlació entre els paràmetres de rendiment de les xarxes i les condicions meteorològiques de l'entorn. En segon lloc, es va haver d'analitzar quins paràmetres de rendiment es podien extraure de la xarxa exterior de la UPV i es van seleccionar d'acord a l'objectiu plantejat. A continuació, es va definir el període temporal al llarg del qual s'emmagatzemarien els paràmetres seleccionats de manera periòdica. El següent pas va ser seleccionar i emmagatzemar les condicions meteorològiques d'una estació propera durant el mateix període de temps. Finalment, es va realitzar un preprocessat per tal de posar en ordre tot el volum de dades i es van analitzar estadísticament. Els resultats van ser exitosos, però van aparèixer diversos problemes pel fet d'estudiar una xarxa real molt variable. Per tant, es va haver de desenvolupar un escenari experimental amb l'objectiu de verificar els resultats. Per aquesta raó es va dissenyar i implementar un enllaç exterior IEEE 802.11b/g punt a multipunt, i es va analitzar de nou l'impacte de les condicions meteorològiques. Es va considerar un enllaç multipunt per tal de d'analitzar també com influïa el impacte del temps segons la distància i els diferents esquemes de modulació. A partir d'aquests resultats, es va dissenyar un algoritme cognitiu energèticament eficient per tal de reduir l'impacte dels fenòmens meteorològics sobre les xarxes IEEE 802.11b/g. Aquest algoritme va ser simulat i els resultats obtinguts van ser satisfactoris, tant en termes d'eficiència energètica com de rendiment de la xarxa. va comprovar que la proposta aporta millores. Per concloure, un altre factor extern que s'ha estudiat en aquesta tesi doctoral ha sigut la taxa d'absorció específica. Aquest paràmetre està relacionat amb una de les preocupacions actuals pel que fa a la salut pública, ja que s'utilitza per a mesurar l'exposició dels teixits del cos humà als camps electromagnètics. Òbviament, aquesta absorció de la senyal afecta el rendiment de les xarxes i, per això, aquest paràmetre s'hauria de tenir en compte a l'hora d'implementar futures xarxes sense fils eficients. Per aquesta raó s'ha inclòs en aquesta tesi doctoral.
Bri Molinero, D. (2015). ESTUDIO DEL EFECTO DE FACTORES EXTERNOS SOBRE LAS REDES WLAN Y DISEÑO DE UN ALGORITMO COGNITIVO ENERGÉTICAMENTE EFICIENTE [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/53450
TESIS
Ellingson, Gary James. "Cooperative Navigation of Fixed-Wing Micro Air Vehicles in GPS-Denied Environments". BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/8706.
Texto completoRousset, Xavier. "La tarification dynamique, l'utile et le juste Seasonal factors and marketing mix: Literature survey and proposed guidelines An analytical framework for retailer price and advertising decisions for products with temperature-sensitive demand The impact of outdoor temperature on pricing and advertising policies for weather-sensitive products Tarification dynamique en ligne et éthicalité perçue par le consommateur : synthèse et voies de recherche Designing algorithmic dynamic pricing from an ethical perspective Are consumers vulnerable to algorithmic dynamic pricing? An empirical investigation". Thesis, Sorbonne Paris Cité, 2019. http://www.theses.fr/2019USPCB039.
Texto completoThis PhD thesis brings together different research projects on dynamic pricing. The objective of the thesis, located at the interface of economics, management sciences and political sciences, is twofold: first, to study the determinants and conditions of use of dynamic pricing at the level of firm in a perspective of maximizing its profits and, on the other hand, to show how, at a collective level, the consideration of ethical issues in the study of dynamic pricing allows a better understanding of its scope. Our contributions, both theoretical and empirical, are presented in two parts. Part 1 focuses on economic efficiency (point of view of the useful), and asks questions about the maximization of profit considerations related to dynamic pricing. From the study of so-called weather-sensitive products in the physical distribution channels, we present a literature review that introduces the dynamic adaptation of the price to factors influencing the demand and we propose a theoretical model of dynamic adaptation of the price in time following the temperature. We complete this theoretical approach with an empirical study that examines how pricing, exercised dynamically from the firm's point of view in response to external factors, allows it to maximize its interests. Part 2 brings together work on ethical considerations related to dynamic pricing (the point of view of the right). Focusing on online distribution channels, we discuss, on a theoretical level, the potential impact of dynamic pricing on consumers' ethical perception, highlighting potential risks of unfairness or vulnerability that price fixing raises. From an empirical point of view, we thoroughly analyse the determinants of the ethical perception of online dynamic pricing by the consumer, in particular according to the conditions of its setting, as well as the dimensions of vulnerability that concern consumers.The conclusion of the thesis brings together future lines of research on the deepening of the measurement of perceived ethics, on the potentialities of the hybridization of economic science with ethics on a subject such as dynamic pricing and on the considerations we have seen on the link between dynamic pricing and the revelation of exchange value (point of view of the true)
Wheeler, David Orton. "Relative Navigation of Micro Air Vehicles in GPS-Degraded Environments". BYU ScholarsArchive, 2017. https://scholarsarchive.byu.edu/etd/6609.
Texto completoSvoboda, Ondřej. "Analýza vlastností stereokamery ZED ve venkovním prostředí". Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2019. http://www.nusl.cz/ntk/nusl-399416.
Texto completoJackson, James Scott. "Enabling Autonomous Operation of Micro Aerial Vehicles Through GPS to GPS-Denied Transitions". BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/8709.
Texto completoBuluswar, Shashi Dhar. "Color-based models for outdoor machine vision". 2002. https://scholarworks.umass.edu/dissertations/AAI3039343.
Texto completoGiuffrida, Laura Margarita. "Assessing the effect of weather on human outdoor perception using Twitter". Master's thesis, 2017. http://hdl.handle.net/10362/34460.
Texto completoHuman Comfort in Outdoor Spaces (HCOS) is linked to physical, physiological and psychological responses of people to environmental variables. Previous studies have established comfort ranges for these variables through questionnaires, reaching only small populations. However, larger amounts of data could not only generate more robust results in local studies, but also allow the possibility of creating an approach that could be applied into a wider range of weather conditions and different climates. This thesis describes a new methodology to assess people’s perception of weather based on human responses to weather conditions extracted from tweets, with the purpose of establishing comfort ranges for environmental variables. Tweets containing weather-associated keywords were collected using the Twitter API and then linked to real-time meteorological data acquired from the Open Weather Map API, which provides weather variables measured nearby the locations in which the tweets were posted. Afterwards, people’s perception of weather was extracted from the tweets using a classifier trained specifically on weather data that identified irrelevant, neutral, positive and negative tweets. The obtained tweets and their related meteorological data were analyzed to establish comfort ranges. Comparing the resulting ranges to others obtained in previous studies, a generally good agreement was found with the indices Effective Temperature (ET) and Termohygrometric index (THI) derived from questionnaires, but the peak of comfort is shifted towards lower and higher temperatures, respectively. Regarding the single weather variables, the obtained comfort ranges are alike the ones found in previous research, in particular, the temperature comfort range matches perfectly at 20 °C – 22 °C. Therefore, it was concluded that tweets can be used for the assessment of HCOS; not only the results of this methodology are comparable to the ones obtained in previous studies, but also the procedure itself shows new features and opportunities for future applications.
Catchpole, Jason. "Adaptive vision based scene registration for outdoor augmentated reality". 2007. http://adt.waikato.ac.nz/public/adt-uow20081117.084239/index.html.
Texto completoLin, Ze-Ming y 林澤明. "Outdoor Road Classification Using Binocular Computer Vision Based onCounterpropagation Network". Thesis, 2008. http://ndltd.ncl.edu.tw/handle/j3uj72.
Texto completo國立臺北科技大學
機電整合研究所
96
In the study, we propose the binocular computer vision based on counterpropagation network (CPN) to classify the road outdoors. First, we employ counterpropagation network using hue and saturation for road classification initially. Second, use 8-neighbors block voting method to improve result of CPN classification, but now the classified results only have similar local properties. Third, we calculate the height of feature points in the captured image. However, we first calibrate camera. We employ the linear least square method to obtain calibration parameters of the left and the right cameras using eight known 3D points and image points projected from real world into cameras. Then we can reconstruct the 3D information by using the calibration parameters and the image points of two cameras. In the stereo correspondence, we use Harris corner detector to extract the feature points of the left and the right image. These feature points are candidates using the fundamental matrix, epipolar geometry constraints, and template matching to look for the best corresponding points. After we know 3D information of corresponding points, we can understand whether classified results of CPN mistake or not. We combine result of CPN with 3D information of corresponding points. We can divide image into road and not road area. The result is helpful to ALV navigation.
Wu, Zong-Yuan y 吳中遠. "Real-Time Vision-Assisted Outdoor Unmanned Vehicle System Design and Control". Thesis, 2006. http://ndltd.ncl.edu.tw/handle/51511979214147808166.
Texto completo國立臺灣大學
應用力學研究所
94
The main theme of this thesis is to develop an integrated navigation and control system for an autonomous vehicle using Vision and DGPS in outdoor environments. The vehicle uses vision to detect landmarkers and DGPS information to determine the vehicle''s position. The vehicle is constructed step by step and the data exchange mechanism between sub-systems is set up through wireless network. The task of path following is performed such that the vehicle can track along a landmarker specified in advance. In the path following control problem, we use the Fuzzy control theory to determine the velocity of the vehicle. We use black-and-white landmarks in order to increase the contrast between the roads and the landmarks. The concept of template correlation is used to identify existing landmarkers in vision. The techniques of Edge Detention and Randomized Hough Transform are then applied to obtain the parameters of the landmarker. If no landmarker is detected by the vision system, the DGPS is used to control the vehicle approaching the specified position of the landmarkers. When the vehicle is passing a crossroad, we also use the GPS information to control the attitude of the vehicle toward the destination. Experimental results showed the effectiveness of our proposed navigation and control methodology.
Luo, Guan-Ming y 駱冠銘. "The Vehicle-Vision-Based Moving-Object Detection System in All-Weather Situations". Thesis, 2016. http://ndltd.ncl.edu.tw/handle/ngp49u.
Texto completo國立高雄應用科技大學
電子工程系碩士班
104
This paper proposed a moving object detection method for the vehicle vision based moving object detection system in all weather situations, which can effectively detect the moving objects shown in the image sequence. The detection system included three steps: (1) Image pre-processing: The users would be asked to set a region of interest (ROI). The ROI is set as the region to detect the moving objects. The upper half of region of image is detect daytime or nighttime and whether the car is moving or not. (2) Feature information detection: If image pre-processing has done, the system is divided daytime moving object detection and nighttime moving object detection, if the system judge that the weather is daytime state, the ROI would do road cutting, this method can filter left and right building and retain the lane information, then system use shadow feature to detect moving object, besides use HOG and SVM classifier to improve the detection accuracy. However in the left and right side moving object we can’t use shadow feature to detect moving object, therefore we use optical flow analysis, consider the optical flow length and direction to judge whether there are moving objects around on both sides; if the system judge that the weather is nighttime state, image’s RGB color space would convert to image’s HSV color space, then detect car light. (3) detection the moving objects: Use feature information to do morphological processing, noise filter and, intensify feature, finally do the bounding box for moving objects. Experimental results show this paper’s method can use vehicle camera to detect moving object in the urban roads. It’s Detection Rate has 80% in the day time; and has 60% in the night time. It’s False Detection Rate has less 5% in the daytime; and has less 15% in the night time, so the effect of showing the feasibility and value of this paper’s method.
劉明豐. "Outdoor autonomous vehicle navigation using stereo vision based on sensor-like points". Thesis, 2002. http://ndltd.ncl.edu.tw/handle/10212944397516616639.
Texto completo國立臺北科技大學
機電整合研究所
90
Due to vast variability of the brightness in outdoor environment, the roads, trees, and lawns usually are not easy to recognize. In this study, we have developed a new navigating approach based on binocular stereovision that has successfully applied to an autonomous land vehicle (ALV) guidance and collision avoidance in outdoor environment. The study topics in the thesis include the information of road learning, stereo corresponding of the sensor-like points, collision avoidance and navigation. In a known outdoor environment, a learning approach to record the information of road before navigating is useful. The information of road includes hue, saturation and intensity (HSI) color feature distribution and the range of their color gradients. This approach will not be influenced by brightness easily, because the approach does not apply the intensity property. According to the learning information, we can label the pixel in the image whose H and S values are similar to the learned information as road. Furthermore, to reduce the cycle time, the image buffer is segmented into blocks of size . Stereo corresponding is a time-consuming task. If we perform corresponding on whole image, we can not navigate the ALV in real time. In the thesis, we propose an efficient and fast approach to sense five suitable points for corresponding, called sensor-like points approach. After sensing points, we suggest an improved approach to solve the stereo corresponding. The approach combines the advantages of feature-based matching and block matching to solve the optimal correspondence point. Using the corresponding point, we can reconstruct five 3-D points, and obtain five coordinates relative to ALV. Using those information we can navigate in the outdoor environment, and perform collision avoidance. The ALV system has been implemented, and it can be flexible to navigate in campus.
Carr, G. Peter K. "Enhancing surveillance video captured in inclement weather". Phd thesis, 2010. http://hdl.handle.net/1885/150329.
Texto completoNathalie, El Nabbout. "Vehicle Tracking in Outdoor Environments using 3D Models". Thesis, 2008. http://hdl.handle.net/10012/3822.
Texto completoYEH, SHING-YU y 葉星虞. "Using Deep Learning Approaches to Predict Indoor Thermal Comfort and Outdoor Rainfall Probability by Embedded Weather Box". Thesis, 2017. http://ndltd.ncl.edu.tw/handle/zfyezh.
Texto completo逢甲大學
資訊工程學系
105
With new technological advancements, the mobile phones and 3C products have been popularized in the life. Internet of Things (IoT) is also equipped in our living space so that the internet can be anywhere and make our society more digitized. Face the pressure of work and the irritable things in life, people hope that they can have a more comfortable living space and so resulting in the applications for smart home appear quickly. For highly developed countries which faced aging societies, the health management of the elderly is one of the most important problems. The body of elderly is not only low resistance but also poor temperature regulation and sensitivity. Slight temperature changes may cause colds, fever and other diseases. Therefore, how to use a simple application to give people a thermal comfortable living space will be an important issue. Raining can not only affect thermal comfort but also cause inconvenience to people, e.g., shopping or hanging the clothes. If we can provide more accurate prediction of raining, people will be able to facilitate their planning schedules. This thesis aims to use the Arduino weather box to collect the weather data from the user’s living space, and then these data can be analyzed via Support Vector Machine (SVM) and Neural Network (NN) to predict thermal comfort and probability of rainfall. We compare the prediction accuracy of temperature and rainfall probability using the two machine learning approaches. We use accuracy and correlation coefficient to determine which one is the best. From the experimental results, we can find using NN can get better results of temperature prediction, and using SVM can get better results to predict rainfall probability.
Liao, Chiem-chun y 廖建鈞. "Automatic Detection and Recognition of Traffic Signs in Outdoor Environment Using Machine Vision Techniques". Thesis, 2006. http://ndltd.ncl.edu.tw/handle/23uv3d.
Texto completo國立臺灣科技大學
高分子系
94
This thesis applies image processing techniques to detect and recognize various colors and shapes of traffic signs. This information of road signs, provided to drivers in a computer-based vision system, is helpful for driving safety. In this study, the traffic sign detection and recognition system which is able to work well in complicated backgrounds and conditions is developed. We use HSV color model to reduce the disturbance of illumination and weather, and present a robust method to identify shapes of incomplete traffic signs. Two different color-pixel definitions are used for color detection and recognition, and calculation of gray-level variance, together with the Otsu statistical threshold selecting method, determines the black and white pixels. In order to increase recognition efficiency, we use the preliminary classification and cut part of the sign information. Furthermore, we divide traffic signs into three categories, which are graph signs, text signs and numeral signs, and they have their own recognition systems. In the experiment, 87 images include 102 traffic signs to discover 87 traffic signs based on detection system, which are used by the recognition system to identify accurately 77 traffic signs. The results demonstrate that the detection system can detect traffic signs under different weather conditions and slight cover conditions, and the recognition system has good performance.
Dai, Yu-Shu y 戴玉書. "A Vision-based Vacant Parking Space Detection Framework for All-Day Outdoor Parking Lot Management". Thesis, 2011. http://ndltd.ncl.edu.tw/handle/66287648969280737925.
Texto completo國立交通大學
電子研究所
100
In this thesis, we propose a vacant parking space detection system that works 24 hours a day. Especially, to capture images at night, we design a capture mode that takes images under different exposure settings and fuses these multi-exposure images into a clearer image. Besides, we combine a proposed Bayesian hierarchical framework (BHF) with the 3D-scene information by treating the whole parking lot as a structure consisting of plentiful surfaces. With the proposed framework, we extract feature vectors from each surface based on a modified version of the Histogram of Oriented Gradients (HOG) approach. By incorporating these feature vectors into specially designed probabilistic models, we can estimate the current parking status by finding the optimal statistical hypothesis among all possible status hypotheses. Experiments over real parking lot scenes have shown that our system can reliably detect vacant parking spaces day and night on an outdoor parking lot.
HSIEH, SHUO-PING y 謝說評. "Comparison between Associations of Outdoor Air Pollutants and Weather Factors with Allergic Conjunctivitis and with Allergic Rhinitis in Taiwan". Thesis, 2019. http://ndltd.ncl.edu.tw/handle/6z4g3j.
Texto completo慈濟大學
公共衛生學系碩士班
107
Background and Motivation: In the recent years, influences by industrialization and urbanization on air quality have been prevalent in Taiwan. The improvement on air pollutant emissions is limited, and the problem of air pollution has always existed. The increase in air pollutant concentration not only has an impact on the environment, but also has a bad impact on human health. Allergic conjunctivitis (AC) and allergic rhinitis (AR) are air pollution-related diseases that do affect each other. Most studies only focus on a single allergic condition, and rarely compare the similarities and differences between air pollutants and the two diseases further. Thus, the purpose of this study is to use diagnose of AC and AR from Taiwan’s National Health Insurance Research Database, to explore the relationship between the first incidence of first-time AC or AR and the exposure to air pollutants in Taiwan, and to further compare the relationships of the air pollutants with the two diseases. Methods: This study used one million samples of the Longitudinal National Health Insurance Database from 2004 to 2013. According to the 9th version of the international disease classification (ICD-9-CM), the study selected patients diagnosed with AC or AR, and linked them with the air quality monitoring data by Environmental Protection Administration as exposure data. The case-crossover design was used for this case control study. The study used the conditional logistic regression model to estimate the relationship between air pollutant concentrations, AC and AR. In addition, considering the lag effect of allergic reactions possibly tiggered by air pollutants, the concentrations of air pollutants on the day (Lag0) and 1 and 2 previous days (Lag-1, Lag-2) were included and stratified by sex, age and season. The comparison between the two air pollutant-disease (AC or AR) relationships was conducte by the logistic regression model. Results: There were 100,636 patients with first occurrence of AC during 2004-2013, with an average age of 27.6 years. The first occurrences of AC were significantly the most for 19-49 years by age (38.43%), for women by sex (56.23%), and in spring by season (29.63%). There were 140,365 patients with first occurrence of AR during 2004-2013, with an average age of 31.41 years. The first occurrences of AR were significantly the most for 19-49 years by age (44.67%), for women by sex (53.36%), and in winter by season (28.65%). Multivariate conditional logistic regression analyses indicated that ozone (O3), nitrogen dioxide (NO2) and temperature were positively associated with AC (P < 0.001), while relative humidity was negatively related to AC (P < 0.001). For analysis of AR, carbon monoxide (CO) and nitrogen dioxide (NO2) were positively associated (P < 0.001), while temperature and relative humidity was negatively related (P < 0.001).We used multivariate logistic regression analysis to compare the two air pollutant-disease relationships, after adjustment for basic demographics (gender, age), season O3, RH and temperature. Compared with AC, AR was found to be associated with the lower NO2 (Model 4: OR=0.990). Conclusions: AC and AR are often considered to be comorbid, and related to weather changes, albeit with different trends related to temperature. They are both associated with air pollution NO2, presumably the main source to be traffic emissions. Comparing the relations of NO2 with the two diseases, we found that AR is associated with the lower NO2 concentration, which may slightly explain more patients with AR than with AC.
Lai, Chih-Chiun y 賴志群. "Outdoor Autonomous Land Vehicle Guidance by Road Information Using Computer Vision and Fuzzy Wheel Adjustment Techniques". Thesis, 1993. http://ndltd.ncl.edu.tw/handle/11862535598705120996.
Texto completo國立交通大學
資訊科學研究所
81
An approach to the guidance of autonotnous land vehicles (ALV's) for outdoor navigation by road information using comater vision and fuzzy wheel adjustment techniques is proposed. Several types of landmarks or features on roads, such as different kinds of path lines and lampposts, are usilized for vision-based navigation. Lamppost positions and road conditions are extracted as the model information for navigation guidance. Using a line following method, the ALV can be guided on roads with path lines on them. By model information, the time that lampposts appear in the image can be predicted. The edges of lampposts are extracted to locate the ALV correctly and quickly. A dynamic image thresholding method is used in real time to solve the problem caused by the sunlight change A modified least square-error line approximation method is employed to extract path lines. Fuzzy set theory is applied to determine the turn angle for wheel adjustment. In addition, a speed adjustment approach is used to keep the ALV at stable speeds on fluctuating roads. Lots of successful navigation experiments using a real ALV confirm the effectiveness of the proposed methods.
Chen, Kuang-Hsiung y 陳光雄. "Vision-based Autonomous Land Vehicle Guidance in Outdoor Road Environments Using Feature Clustering and Shape Matching Techniques". Thesis, 1999. http://ndltd.ncl.edu.tw/handle/42415586063050695099.
Texto completo國立交通大學
資訊科學系
87
Autonomous land vehicles (ALV''s) are useful for many automation applications in both indoor and outdoor environments. Successful ALV navigation requires integration of techniques of environment sensing and learning, image processing and feature extraction, ALV location, path planning, wheel control, and so on. In outdoor environments, because of the great variety of object and road conditions like irregular and unstable features on objects, moving objects, shadows, degraded regions, curved roads, ascending or descending roads, changes of illumination, and even rain, we need to combine different problem-solving algorithms and perhaps equip multiple sensors to solve the complex problem of ALV guidance in roads. In this dissertation, five approaches to vision-based ALV guidance in outdoor road environments are proposed. The conventional ways of ALV guidance, which are generally complex and time-consuming, are avoided in the proposed approaches; instead, efficient and effective ways of ALV guidance, which are usually easier and faster, are adopted. In the first approach, color information clustering and combined line and road following techniques are used for ALV guidance on straight roads with constant widths. The clustering algorithm is used to solve the problem caused by great changes of intensity in navigation. The combined line and road following technique is used to achieve faster and more flexible navigations. To locate the ALV for line following or road following, the line-model or road-model, which are constructed using path lines or road boundaries, are matched with the extracted path lines or road surface in the image, respectively. In the second approach, three tangent lines, that are extracted from the dotted central path line and collected from the images of the previous and current cycles, are used to judge whether the ALV is leaving a straight road and entering a curved road in the current cycle. When the ALV enters a curved road, the three tangent lines collected so far are used again to derive the navigation path at a curved turning road section. The navigation path is assumed to be a circle and is re-derived cyclically for safe navigation. Moreover, the three tangent lines can also be used to judge whether the ALV is leaving a curved road and entering a straight road. The third approach allows variations of road widths, which are caused by existence of static cars on the roadside or moving cars on the road lane. The conventional way of detecting obstacles and cars in the navigation route, which is in general complex and inefficient, is avoided; instead, collision-free road area detecting, which is usually easier and faster, is adopted. Road boundaries are used to construct the reference model, and the road surface intensity is selected as the visual feature in this approach. The reference model is then matched with the extracted road surface in the image to find the safe road area and the ALV location on the safe road area. In the fourth approach, image sequence and coordination transformation techniques are used to detect obstacles ahead on the safe road area in navigation. To judge whether one object newly appearing in the image of the current cycle is an obstacle, the object boundary shape is first extracted from the image. After the translation vector from the ALV location in the current cycle to that in the next cycle is estimated, the position of the boundary shape in the image of the next cycle is predicted using coordinate transformation techniques. The predicted boundary shape is then matched with the extracted boundary shape of the object in the image of the next cycle to judge whether the object is an obstacle. In the fifth approach, the ALV can keep driving forward even when ascending or descending roads appear ahead of the ALV. When the ALV keeps driving on a flat road, it detects and follows the flat road. When the ALV navigates at a transition from a flat road into an ascending road, both of the flat and the ascending road boundaries are extracted from the image, which are then used to estimate the slant angle of the ascending road and compute accordingly the connection points of the flat and the ascending road boundaries. When the ALV drives on the flat road but the flat road boundaries disappear from the image, the flat road boundaries are derived using the estimated slant angle, the stable features of the ascending road boundaries in the image, and the predicted connection points of the ascending and the flat road boundaries. The ALV then follows the derived flat road because it still drives on the flat road. At the beginning of subsequent each navigation cycle, the ALV predicts the flat road boundaries in the image using the derived flat road boundaries just described in the previous cycle. The predicted flat road boundaries are then matched with the extracted ascending road boundaries in the image to judge whether the ALV has entered the ascending road in the current cycle. This way of guidance is also used when the ALV navigates at a transition from a flat road into a descending road. A lot of successful navigation tests show that the proposed approaches are effective for ALV guidance in outdoor road environments.
Chen, Yung-Der y 陳永德. "A Study on Outdoor Guidance of Autonomous Land Vehicles by Computer Vision Based on Improved A* Search". Thesis, 2007. http://ndltd.ncl.edu.tw/handle/59f66e.
Texto completo國立臺北科技大學
電腦與通訊研究所
95
In this paper, we develop the system of autonomous land vehicles (ALV) by computer vision based on artificial intelligent (AI) policy. It makes ALV adapted to the complex environment. This system uses two cameras to capture two images from the front of the vehicle and calculate the 3D information of environment. Though the obtained 3D information, we can recognize region of road and region of obstacle in front of the vehicle. Using navigation policy based on AI to make vehicle navigate in outdoor environment. In path planning, combining ant colony optimization (ACO) and search program the best path. First, we obtain the navigation map of the front of the vehicle from charge-coupled device (CCD). After path planning of ACO, we can get a kind of novel trail information (pheromone). Using trail information improves the parameters of search to get novel estimation of node. According to estimation of nodes on searching path, we obtain a path from origin to target. After obtaining road information, the path of navigation is searched by E-compass and improved search. We make ALV avoid the obstacle safely and run toward the goal in an appropriate path. The ALV system has been performed in the outdoor to demonstrate the effectiveness of the presented method.
chang, yu-ching y 張煜青. "A Study on Outdoor Guidance of Autonomous Land Vehicles by Binocular Computer Vision Based on Artificial Intelligent Policy". Thesis, 2003. http://ndltd.ncl.edu.tw/handle/84844257100244703271.
Texto completo國立臺北科技大學
自動化科技研究所
91
In this thesis, we have developed a new outdoor guidance system of autonomous land vehicle (ALV) equipped with binocular stereovision system based on artificial intelligence (AI) policy. The system uses two cameras to reconstruct the 3D structure of the scene. We can utilize the 3D information of the scene to recognize all obstacles. Hence, The ALV can perform navigation and obstacle avoidance in the outdoor environment using AI policy. The study topics in the thesis include camera calibration, stereo corresponding, improve information of the scene, obstacle avoidance and navigation based on AI policy. The correspondence problem is the important and most difficult problem of stereovision. The accuracy of 3D scene information will be affected greatly by the correspondence. In the study, first, we present a fast and simple approach to select reference points from the left image. The approach combines edge of the scene and several sensory vertical lines to select reference points. Then, we present a two-layer hierarchical stereo correspondence to the right image in binocular stereovision. A low-level processing is employed to obtain a set of points that are candidates for correspondence using Grey relation approach (GRA). The refinement of the set of low-level correspondences is performed by a high-level correspondence process. The high-level processing uses a permutation of genetic algorithm (GAP) approach for searching optimal solution from the solution space. Some constraints is embedded in the fitness function. The methods have been tested on a series of real images and perform very well. After the calibrated parameters and correspondence pairs are obtained, the 3D information of the scene can be computed by linear least-square method. But there are also several departures result from correspondence. We employ the nearest neighbor decision rules (NNDR) approach to solve this problem. Hence, we can obtain correct information of the scene via the approach on whole images. After we derive the 3D structure of the scene, we can know where the obstacles are. Hence, we propose navigation policy based on AI approach. The approach adopts a simple reactive navigation strategy by combining repulsion from obstacles with attraction to a goal. That is locally optimal in the sense of minimizing the hitting probability based on what is currently known about the world. Therefore, we can find an appropriate path for the ALV navigation. The ALV system has been performed in campus to demonstrate the effectiveness of the presented method.
ZHU, ZE-HONG y 朱澤宏. "Autonomous land vehicle guidance by computer vision aechniquse using path line and road lamppost information for outdoor navigation". Thesis, 1992. http://ndltd.ncl.edu.tw/handle/89369183714029878642.
Texto completoTang, Hsin-Jun y 唐心駿. "In-car Tour Guidance in Outdoor Parks Using Augmented Reality and Omni-vision Techniques with an Automatic Learning Capability". Thesis, 2014. http://ndltd.ncl.edu.tw/handle/71962519957595476371.
Texto completo國立交通大學
資訊科學與工程研究所
102
In this study, an augmented-reality based in-car tour guidance system with an automatic learning capability for use in outdoor park areas using computer vision techniques has been proposed. With the proposed system, a user can construct a tour guidance map for a park area in a simple and clear way, and use this map to provide tour guidance information to in-car passengers. When a passenger is in a vehicle driven in a park area, he/she can get from the system tour guidance information mainly about the names of the nearby buildings appearing along the way on the guidance path. The building names are augmented on the passenger-view image which is displayed on the mobile device held by the passenger. To implement the proposed system with the above-mentioned capability, at first an environment map is generated in the learning phase, which includes the information about the tour path and the along-path buildings (mainly the building names). All the data are learned either manually or by programs, and saved into the database for use in the navigation phase. Secondly, a method for automatic learning of the along-path vertical-line features, mainly, the edges of light poles, is proposed for use by the system. In this feature-learning stage, the vehicle equipped with a GPS device and a two-camera omni-imaging device is driven on a pre-selected guidance path. On each visited spot of the path, the system analyzes the input omni-image pair taken by the upper and lower cameras of the imaging device respectively, to detect the nearby vertical-line features and compute the positions and heights of them by the use of the GPS device. And the learned features are added to the map as landmarks for vehicle localization in the navigation phase. Next, a method for vehicle localization is proposed for use by the system. The method analyzes the omni-image taken by the upper camera of the imaging device to detect the learned features by the use of the learned information about them and the GPS device. It then computes the vehicle position by using the relation between the features and the vehicle. Finally, a method for AR-based guidance is proposed, which at first generates a passenger-view image by transforming the omni-image acquired from the upper omni-camera onto the user’s mobile-device screen. The method then uses the passenger-view image as a base, and augments the building names on the image before the image is displayed. To accomplish this function, the system computes the position of each building on the passenger-view image by using the result of vehicle localization. Good experimental results are also presented to show the feasibility of the proposed methods for real applications.