Construção de mosaicos georreferenciados usando imagens aéreas de pequeno formato

In this work we propose a technique that uses uncontrolled small format aerial images, or SFAI, and stereohotogrammetry techniques to construct georeferenced mosaics. Images are obtained using a simple digital camera coupled with a radio controlled (RC) helicopter. Techniques for removing common distortions are applied and the relative orientation of the models are recovered using projective geometry. Ground truth points are used to get absolute orientation, plus a definition of scale and a coordinate system which relates image measures to the ground. The mosaic is read into a GIS system, providing useful information to different types of users, such as researchers, governmental agencies, employees, fishermen and tourism enterprises. Results are reported, illustrating the applicability of the system. The main contribution is the generation of georeferenced mosaics using SFAIs, which have not yet broadly explored in cartography projects. The proposed architecture presents a viable and much less expensive solution, when compared to systems using controlled pictures

Utilizando mapas de conectividade fuzzy no desenvolvimento de algoritmos reparadores de imagens binárias 3D

A 3D binary image is considered well-composed if, and only if, the union of the faces shared by the foreground and background voxels of the image is a surface in R3. Wellcomposed images have some desirable topological properties, which allow us to simplify and optimize algorithms that are widely used in computer graphics, computer vision and image processing. These advantages have fostered the development of algorithms to repair bi-dimensional (2D) and three-dimensional (3D) images that are not well-composed. These algorithms are known as repairing algorithms. In this dissertation, we propose two repairing algorithms, one randomized and one deterministic. Both algorithms are capable of making topological repairs in 3D binary images, producing well-composed images similar to the original images. The key idea behind both algorithms is to iteratively change the assigned color of some points in the input image from 0 (background)to 1 (foreground) until the image becomes well-composed. The points whose colors are changed by the algorithms are chosen according to their values in the fuzzy connectivity map resulting from the image segmentation process. The use of the fuzzy connectivity map ensures that a subset of points chosen by the algorithm at any given iteration is the one with the least affinity with the background among all possible choices

RR3D: Uma solução para renderização remota de imagens médicas tridimensionais

The visualization of three-dimensional(3D)images is increasigly being sed in the area of medicine, helping physicians diagnose desease. the advances achived in scaners esed for acquisition of these 3d exames, such as computerized tumography(CT) and Magnetic Resonance imaging (MRI), enable the generation of images with higher resolutions, thus, generating files with much larger sizes. Currently, the images of computationally expensive one, and demanding the use of a righ and computer for such task. The direct remote acess of these images thruogh the internet is not efficient also, since all images have to be trasferred to the user´s equipment before the 3D visualization process ca start. with these problems in mind, this work proposes and analyses a solution for the remote redering of 3D medical images, called Remote Rendering (RR3D). In RR3D, the whole hedering process is pefomed a server or a cluster of servers, with high computational power, and only the resulting image is tranferred to the client, still allowing the client to peform operations such as rotations, zoom, etc. the solution was developed using web services written in java and an architecture that uses the scientific visualization packcage paraview, the framework paraviewWeb and the PACS server DCM4CHEE.The solution was tested with two scenarios where the rendering process was performed by a sever with graphics hadwere (GPU) and by a server without GPUs. In the scenarios without GPUs, the soluction was executed in parallel with several number of cores (processing units)dedicated to it. In order to compare our solution to order medical visualization application, a third scenario was esed in the rendering process, was done locally. In all tree scenarios, the solution was tested for different network speeds. The solution solved satisfactorily the problem with the delay in the transfer of the DICOM files, while alowing the use of low and computers as client for visualizing the exams even, tablets and smart phones

Segmentação fuzzy de imagens e vídeos

Image segmentation is the process of subdiving an image into constituent regions or objects that have similar features. In video segmentation, more than subdividing the frames in object that have similar features, there is a consistency requirement among segmentations of successive frames of the video. Fuzzy segmentation is a region growing technique that assigns to each element in an image (which may have been corrupted by noise and/or shading) a grade of membership between 0 and 1 to an object. In this work we present an application that uses a fuzzy segmentation algorithm to identify and select particles in micrographs and an extension of the algorithm to perform video segmentation. Here, we treat a video shot is treated as a three-dimensional volume with different z slices being occupied by different frames of the video shot. The volume is interactively segmented based on selected seed elements, that will determine the affinity functions based on their motion and color properties. The color information can be extracted from a specific color space or from three channels of a set of color models that are selected based on the correlation of the information from all channels. The motion information is provided into the form of dense optical flows maps. Finally, segmentation of real and synthetic videos and their application in a non-photorealistic rendering (NPR) toll are presented

Renderizações não fotorealísticas para estilização de imagens e vídeos usando areia colorida

Non-Photorealisitc Rendering (NPR) is a class of techniques that aims to reproduce artistic techniques, trying to express feelings and moods on the rendered scenes, giving an aspect of that they had been made "by hand". Another way of defining NPR is that it is the processing of scenes, images or videos into artwork, generating scenes, images or videos that can have the visual appeal of pieces of art, expressing the visual and emotional characteristics of artistic styles. This dissertation presents a new method of NPR for stylization of images and videos, based on a typical artistic expression of the Northeast region of Brazil, that uses colored sand to compose landscape images on the inner surface of glass bottles. This method is comprised by one technique for generating 2D procedural textures of sand, and two techniques that mimic effects created by the artists using their tools. It also presents a method for generating 21 2D animations in sandbox from the stylized video. The temporal coherence within these stylized videos can be enforced on individual objects with the aid of a video segmentation algorithm. The present techniques in this work were used on stylization of synthetic and real videos, something close to impossible to be produced by artist in real life

Classificador neural híbrido para imagens obtidas por sensoriamento remoto

Remote sensing is one technology of extreme importance, allowing capture of data from the Earth's surface that are used with various purposes, including, environmental monitoring, tracking usage of natural resources, geological prospecting and monitoring of disasters. One of the main applications of remote sensing is the generation of thematic maps and subsequent survey of areas from images generated by orbital or sub-orbital sensors. Pattern classification methods are used in the implementation of computational routines to automate this activity. Artificial neural networks present themselves as viable alternatives to traditional statistical classifiers, mainly for applications whose data show high dimensionality as those from hyperspectral sensors. This work main goal is to develop a classiffier based on neural networks radial basis function and Growing Neural Gas, which presents some advantages over using individual neural networks. The main idea is to use Growing Neural Gas's incremental characteristics to determine the radial basis function network's quantity and choice of centers in order to obtain a highly effective classiffier. To demonstrate the performance of the classiffier three studies case are presented along with the results.

Segmentação fuzzy de imagens coloridas com características texturais: uma aplicação a rochas sedimentares

Image segmentation is the process of labeling pixels on di erent objects, an important step in many image processing systems. This work proposes a clustering method for the segmentation of color digital images with textural features. This is done by reducing the dimensionality of histograms of color images and using the Skew Divergence to calculate the fuzzy a nity functions. This approach is appropriate for segmenting images that have colorful textural features such as geological, dermoscopic and other natural images, as images containing mountains, grass or forests. Furthermore, experimental results of colored texture clustering using images of aquifers' sedimentary porous rocks are presented and analyzed in terms of precision to verify its e ectiveness.

Detecção da área e extração do campo em imagens de impressão digital

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Métodos para extração de informações a partir de imagens multiespectrais de escalas grandes

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Integração de imagens vetoriais e matriciais na visualização dos levantamentos realizados com a unidade móvel de mapeamento digital

Pós-graduação em Ciências Cartográficas - FCT

Inferência espacial de clorofila a por redes neurais artificiais aplicadas a imagens multiespectrais e medidas tomadas in situ

Pós-graduação em Ciências Cartográficas - FCT

Uma análise da infestação por plantas aquáticas utilizando imagens multiescala e redes neurais artificiais

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Mosaicagem de imagens digitais

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Extração semi-automática do eixo de rodovia em imagens de média e alta resolução usando programação dinâmica

Pós-graduação em Ciências Cartográficas - FCT

Geração automática de Trimap para Matting Digital de Imagens e Vídeo

Pós-graduação em Ciência da Computação - IBILCE