Submitted by Samara Calçado de Azevedo (samara_calcado@hotmail.com) on 2018-09-17T13:15:08Z No. of bitstreams: 1 Azevedo_SC.pdf: 8234866 bytes, checksum: 4f6512aa127d3ab1d52e793725f3a75a (MD5) / Approved for entry into archive by Claudia Adriana Spindola null (claudia@fct.unesp.br) on 2018-09-17T13:46:15Z (GMT) No. of bitstreams: 1 azevedo_sc_dr_prud.pdf: 7732297 bytes, checksum: bebf4345bd7542863867084e462c9ff9 (MD5) / Made available in DSpace on 2018-09-17T13:46:15Z (GMT). No. of bitstreams: 1 azevedo_sc_dr_prud.pdf: 7732297 bytes, checksum: bebf4345bd7542863867084e462c9ff9 (MD5) Previous issue date: 2018-08-30 / Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) / Imagens orbitais com alta resolução espacial abriram uma nova era na extração de informações, especialmente em ambientes urbanos, em que valiosas informações sobre as superfícies tornaram-se disponíveis e de forma detalhada. No entanto, a obtenção de informação em áreas urbanas complexas pode ser comprometida pela presença de sombras, que chegam a ocupar uma parte significativa da imagem, causando sérias interferências na sua análise. A remoção de sombras é, portanto, um tema importante e ainda não resolvido devido à dificuldade da tarefa, sendo necessária na etapa de pré-processamento de diversas aplicações. Este trabalho propõe o desenvolvimento de uma nova abordagem automática para a restauração de áreas de sombras em imagens de satélite multiespectrais com alta resolução espacial. A abordagem se divide em três etapas principais e sequenciais, sendo a primeira o pré-processamento, que consiste na conversão das imagens para reflectância aparente e na fusão das bandas para a geração de índices espectrais. Na segunda etapa, a detecção das áreas de sombras é realizada pela combinação do top-hat por fechamento com a injunção de um parâmetro de área, calculado em função do índice de sombras NSDVI (Normalized Saturation-Value Difference Index). As regiões de sombras são refinadas para a geração da máscara de sombras, a qual é utilizada na terceira etapa, como guia na restauração pelo inpainting automático. A estratégia de restauração pelo inpainting híbrido, adaptado para o contexto multiespectral, combina a difusão anisotrópica, ordem de preenchimento definida a partir de uma imagem cartoon e equação de transporte, e o preenchimento baseado em blocos por amostragem local. A avaliação experimental do método proposto foi realizada em um conjunto de 215 imagens, fruto do recorte de duas cenas dos satélites WorldView-2 (WV-2) e Pléiades-1B (PL-1B) sobre a área urbana de São Paulo. Os resultados mostraram uma boa performance da detecção de sombras pelo método proposto, alcançando uma acurácia global média de 94,20% e de 90,84%, para as imagens WV-2 e PL-1B, respectivamente, sendo inclusive superior, quando comparado com outros dois métodos propostos na literatura. A remoção dos efeitos das sombras obtido pelo inpainting, proporcionou uma restauração satisfatória e coerente de feições como vias e telhados, que ficaram melhor discerníveis pelo inpainting. Além disso, quando quantidades substanciais de áreas não contamindas por sombras estão disponíveis, o inpainting aumentou as áreas úteis da imagem mais do que a restauração baseada em outras técnicas de transformação de intensidades, como o matching de histogramas. Assim, frente à necessidade de informações extraídas de imagens multiespectrais, espera-se que o método possa contribuir com a detecção e remoção automática dos efeitos de sombras em imagens multiespectrais com alta resolução espacial de áreas urbanas. / High resolution satellite images has played an important role in information extraction especially in urban areas, since valuable information and higher level of details from surface become available with these images. Nevertheless, the task of extracting information from complex urban environment can be hampered by shadows, which can occupy a significant part into the image and, thus negatively affecting the image analysis. Therefore, due to the complexity of the problem, shadow removal is a crucial as one of the first pre-processing steps to enhance the performance of many subsequent steps and applications. The main goal of this thesis is to present a new automatic method for shadow removal in high spatial resolution satellite multispectral images. The proposed method comprises three main steps: the first one is the pre-processing, which includesthe conversion of the target image to the top of atmosphere (TOA) reflectance and the image pansherpening to spectral indices generation. Secondly, a shadow pixels candidates’ identification is performed, combining black-top-hat (BTH) transformation with area injunction driven by the normalized saturation-value difference index (NSDVI) mask. The obtained output is a shadow mask, which is used to properly guide our automatic inpainting-inspired strategy in the restoration step. In the third step, a hybrid inpainting-based strategy specifically adapted for the multispectral imagery context is applied to recover shadow areas, which unifies anisotropic diffusion, filling-in priority based on cartoon image representations, transport equation and block-based pixel replication using local dynamic sampling. The performance of our approach has been evaluated by taking 215 subset images from WorldView-2 (WV-2) and Pléiades-1B (PL-1B) that encompasses the city of São Paulo. The method achieves an overall accuracy on shadow detection up to 94.2%, for WV-2, and 90.84%, for PL-1B. The comparative results indicate that the proposed method outperforms two existing state-of-the-art methods. Shadow effects are mitigated by local inpainting method in which the satisfactory outputs demonstrate good coherence for highway and building rooftops recovery. Moreover, when largely non-contaminated areas are available in the image, the proposed approach improves significantly more areas of the image than the intensity-based transformation techniques, such as the histogram matching method. Once only multispectral imagery is required as input data, the approach can be suitable to support other remote sensing applications as well. / 2013/25257-4

A common problem when using background models to segment moving objects from video sequences is that objects cast shadow usually significantly differ from the background and therefore get detected as foreground. This causes several problems when extracting and labeling objects, such as object shape distortion and several objects merging together. The purpose of this thesis is to explore various possibilities to handle this problem. Three methods for statistical background modeling are reviewed. All methods work on a per pixel basis, the first is based on approximating the median, the next on using Gaussian mixture models, and the last one is based on channel representation. It is concluded that all methods detect cast shadows as foreground. A study of existing methods to handle cast shadows has been carried out in order to gain knowledge on the subject and get ideas. A common approach is to transform the RGB-color representation into a representation that separates color into intensity and chromatic components in order to determine whether or not newly sampled pixel-values are related to the background. The color spaces HSV, IHSL, CIELAB, YCbCr, and a color model proposed in the literature (Horprasert et al.) are discussed and compared for the purpose of shadow detection. It is concluded that Horprasert's color model is the most suitable for this purpose. The thesis ends with a proposal of a method to combine background modeling using Gaussian mixture models with shadow detection using Horprasert's color model. It is concluded that, while not perfect, such a combination can be very helpful in segmenting objects and detecting their cast shadow.

This Master’s thesis investigates the application of dynamically generated procedural terrain textures for texturing 3D representations of the Earth’s surface. The study explores techniques to overcome limitations of the currently most common method – projecting satellite imagery onto the mesh – such as insufficient resolution for close-up views and challenges in accommodating external lighting models. Textures for sand, rock and grass were generated procedurally on the GPU. Aliasing was prevented using a clamping technique, dynamically changing the level of detail when freely navigating across diverse landscapes. The general color of each terrain type was extracted from the satellite images, guided by land cover rasters, in a process where shadows were eliminated using HSV color space conversion and filtering. The procedurally generated textures provide significantly more details than the satellite images in close-up views, while missing some information in medium- to far-distance views, due to the satellite images containing information lacking in the 3D mesh. A qualitative analysis spanning six data sets from diverse global locations demonstrates that the proposed methods are applicable across a range of landscapes and climates.

Au cours de la dernière décennie, les technologies permettant la numérisation d'espaces urbains ont connu un développement rapide. Des campagnes d'acquisition de données couvrant des villes entières ont été menées en utilisant des scanners LiDAR (Light Detection And Ranging) installés sur des véhicules mobiles. Les résultats de ces campagnes d'acquisition laser, représentants les bâtiments numérisés, sont des nuages de millions de points pouvant également contenir un ensemble de photographies. On s'intéresse ici à l'amélioration du nuage de points à l'aide des données présentes dans ces photographies. Cette thèse apporte plusieurs contributions notables à cette amélioration. La position et l'orientation des images acquises sont généralement connues à l'aide de dispositifs embarqués avec le scanner LiDAR, même si ces informations de positionnement sont parfois imprécises. Pour obtenir un recalage précis d'une image sur un nuage de points, nous proposons un algorithme en deux étapes, faisant appel à l'information mutuelle normalisée et aux histogrammes de gradients orientés. Cette méthode permet d'obtenir une pose précise même lorsque les estimations initiales sont très éloignées de la position et de l'orientation réelles. Une fois ces images recalées, il est possible de les utiliser pour inférer la couleur de chaque point du nuage en prenant en compte la variabilité des points de vue. Pour cela, nous nous appuyons sur la minimisation d'une énergie prenant en compte les différentes couleurs associables à un point et les couleurs présentes dans le voisinage spatial du point. Bien entendu, les différences d'illumination lors de l'acquisition des données peuvent altérer la couleur à attribuer à un point. Notamment, cette couleur peut dépendre de la présence d'ombres portées amenées à changer avec la position du soleil. Il est donc nécessaire de détecter et de corriger ces dernières. Nous proposons une nouvelle méthode qui s'appuie sur l'analyse conjointe des variations de la réflectance mesurée par le LiDAR et de la colorimétrie des points du nuage. En détectant suffisamment d'interfaces ombre/lumière nous pouvons caractériser la luminosité de la scène et la corriger pour obtenir des scènes sans ombre portée. Le dernier problème abordé par cette thèse est celui de la densification du nuage de points. En effet la densité locale du nuage de points est variable et parfois insuffisante dans certaines zones. Nous proposons une approche applicable directement par la mise en oeuvre d'un filtre bilatéral joint permettant de densifier le nuage de points en utilisant les données des images / Recent years saw a rapid development of city digitization technologies. Acquisition campaigns covering entire cities are now performed using LiDAR (Light Detection And Ranging) scanners embedded aboard mobile vehicles. These acquisition campaigns yield point clouds, composed of millions of points, representing the buildings and the streets, and may also contain a set of images of the scene. The subject developed here is the improvement of the point cloud using the information contained in the camera images. This thesis introduces several contributions to this joint improvement. The position and orientation of acquired images are usually estimated using devices embedded with the LiDAR scanner, even if this information is inaccurate. To obtain the precise registration of an image on a point cloud, we propose a two-step algorithm which uses both Mutual Information and Histograms of Oriented Gradients. The proposed method yields an accurate camera pose, even when the initial estimations are far from the real position and orientation. Once the images have been correctly registered, it is possible to use them to color each point of the cloud while using the variability of the point of view. This is done by minimizing an energy considering the different colors associated with a point and the potential colors of its neighbors. Illumination changes can also change the color assigned to a point. Notably, this color can be affected by cast shadows. These cast shadows are changing with the sun position, it is therefore necessary to detect and correct them. We propose a new method that analyzes the joint variation of the reflectance value obtained by the LiDAR and the color of the points. By detecting enough interfaces between shadow and light, we can characterize the luminance of the scene and to remove the cast shadows. The last point developed in this thesis is the densification of a point cloud. Indeed, the local density of a point cloud varies and is sometimes insufficient in certain areas. We propose a directly applicable approach to increase the density of a point cloud using multiple images

Submitted by PPG Engenharia El?trica (engenharia.pg.eletrica@pucrs.br) on 2017-11-08T12:48:45Z No. of bitstreams: 1 Guilherme_Silva_Dissertacao.pdf: 5806780 bytes, checksum: dd97b70975650b889aaa277b7e6f2b19 (MD5) / Approved for entry into archive by Caroline Xavier (caroline.xavier@pucrs.br) on 2017-11-17T12:39:26Z (GMT) No. of bitstreams: 1 Guilherme_Silva_Dissertacao.pdf: 5806780 bytes, checksum: dd97b70975650b889aaa277b7e6f2b19 (MD5) / Made available in DSpace on 2017-11-17T12:50:25Z (GMT). No. of bitstreams: 1 Guilherme_Silva_Dissertacao.pdf: 5806780 bytes, checksum: dd97b70975650b889aaa277b7e6f2b19 (MD5) Previous issue date: 2017-08-14 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior - CAPES

Foreground segmentation is a common first step in tracking and surveillance applications.  The purpose of foreground segmentation is to provide later stages of image processing with an indication of where interesting data can be found.  This thesis is an investigation of how foreground segmentation can be performed in two contexts: as a pre-step to trajectory tracking and as a pre-step in indoor surveillance applications. Three methods are selected and detailed: a single Gaussian method, a Gaussian mixture model method, and a codebook method.  Experiments are then performed on typical input video using the methods.  It is concluded that the Gaussian mixture model produces the output which yields the best trajectories when used as input to the trajectory tracker.  An extension is proposed to the Gaussian mixture model which reduces shadow, improving the performance of foreground segmentation in the surveillance context.

Tato práce se zabývá a analyzuje možnosti, kterými je možno zjistit geografickou polohu ze snímků nebo videa pouze za použití vizuální informace z obrazu.    Výsledkem práce jsou dvě rozdílné metody geo-lokalizace. První z nich pracuje na principu hledání časů východu a západu Slunce. Hlavní výhodou této metody je její univerzálnost. Funguje s jakoukoliv kamerou umístěnou v externích prostorech a nevyžaduje přítomnost žádných specifických objektů ve scéně. Pro správný výsledek je třeba alespoň celodenní záznam z kamery. Výsledky jsou uspokojivé za každého počasí. Druhá metoda pracuje na základě analýzy stínů ve scéně. Správnou pozici je možno určit, s poměrně velkou přesností, pouze na základě dvou snímků pořízených v různém čase. Tato metoda vyžaduje přítomnost dvou objektů v obraze, které vrhají stín. Přesnosti výsledků navržených metod jsou vyhodnoceny a porovnány. Z výsledků vyplývá, že obě metody lze úspěšně použít pro odhad geografické polohy. Dále byla v rámci práce pořízena rozsáhlá datová sada obrazových sekvencí z volně přístupných webových kamer.

This thesis describes the theoretical, experimental, and practical aspects of a noise-limited method for scene-change detection in images. The research is divided into three sections: noise analysis and modelling, dual illumination scene-change modelling, and integration of noise into the scene-change model. The sources of noise within commercially available digital cameras are described, with a new model for image noise derived for charge-coupled device (CCD) cameras. The model is validated experimentally through the development of techniques that allow the individual noise components to be measured from the analysis of output images alone. A generic model for complementary metal-oxide-semiconductor (CMOS) cameras is also derived. Methods for the analysis of spatial (inter-pixel) and temporal (intra-pixel) noise are developed. These are used subsequently to investigate the effects of environmental temperature on camera noise. Based on the cameras tested, the results show that the CCD camera noise response to variation in environmental temperature is complex whereas the CMOS camera response simply increases monotonically. A new concept for scene-change detection is proposed based upon a dual illumination concept where both direct and ambient illumination sources are present in an environment, such as that which occurs in natural outdoor scenes with direct sunlight and ambient skylight. The transition of pixel colour from the combined direct and ambient illuminants to the ambient illuminant only is modelled. A method for shadow-free scene-change is then developed that predicts a pixel's colour when the area in the scene is subjected to ambient illumination only, allowing pixel change to be distinguished as either being due to a cast shadow or due to a genuine change in the scene. Experiments on images captured in controlled lighting demonstrate 91% of scene-change and 83% of cast shadows are correctly determined from analysis of pixel colour change alone. A statistical method for detecting shadow-free scene-change is developed. This is achieved by bounding the dual illumination model by the confidence interval associated with the pixel's noise. Three benefits arise from the integration of noise into the scene-change detection method: - The necessity for pre-filtering images for noise is removed; - All empirical thresholds are removed; and - Performance is improved. The noise-limited scene-change detection algorithm correctly classifies 93% of scene-change and 87% of cast shadows from pixel colour change alone. When simple post-analysis size-filtering is applied both these figures increase to 95%.