968 resultados para Large Image Collections


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

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The task of segmenting cell nuclei from cytoplasm in conventional Papanicolaou (Pap) stained cervical cell images is a classical image analysis problem which may prove to be crucial to the development of successful systems which automate the analysis of Pap smears for detection of cancer of the cervix. Although simple thresholding techniques will extract the nucleus in some cases, accurate unsupervised segmentation of very large image databases is elusive. Conventional active contour models as introduced by Kass, Witkin and Terzopoulos (1988) offer a number of advantages in this application, but suffer from the well-known drawbacks of initialisation and minimisation. Here we show that a Viterbi search-based dual active contour algorithm is able to overcome many of these problems and achieve over 99% accurate segmentation on a database of 20 130 Pap stained cell images. (C) 1998 Elsevier Science B.V. All rights reserved.

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CODEX SEARCH es un motor de recuperación de información especializado en derecho de extranjería que está basado en herramientas y conocimiento lingüísticos. Un motor o Sistema de Recuperación de Información (SRI) es un software capaz de localizar información en grandes colecciones documentales (entorno no trivial) en formato electrónico. Mediante un estudio previo se ha detectado que la extranjería es un ámbito discursivo en el que resulta difícil expresar la necesidad de información en términos de una consulta formal, objeto de los sistemas de recuperación actuales. Por lo tanto, para desarrollar un SRI eficiente en el dominio indicado no basta con emplear un modelo tradicional de RI, es decir, comparar los términos de la pregunta con los de la respuesta, básicamente porque no expresan implicaciones y porque no tiene que haber necesariamente una relación 1 a 1. En este sentido, la solución lingüística propuesta se basa en incorporar el conocimiento del especialista mediante la integración en el sistema de una librería de casos. Los casos son ejemplos de procedimientos aplicados por expertos a la solución de problemas que han ocurrido en la realidad y que han terminado en éxito o fracaso. Los resultados obtenidos en esta primera fase son muy alentadores pero es necesario continuar la investigación en este campo para mejorar el rendimiento del prototipo al que se puede acceder desde &http://161.116.36.139/~codex/&.

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Aquest projecte resol les fases inicials d'un altre projecte més gran que té com a objectiu la conversió automàtica de seqüències d'imatges a 3D. El projecte s'ha centrat en la reconstrucció calibrada de col·leccions d'imatges mitjançant la tècnica anomenada structure from motion. Aquesta tècnica forma part de l'àmbit de la visió per computador i s'utilitza per obtenir la posició i l'orientació de les diferents càmeres juntament amb una reconstrucció 3D de l'escena en forma de núvol de punts.

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The number of digital images has been increasing exponentially in the last few years. People have problems managing their image collections and finding a specific image. An automatic image categorization system could help them to manage images and find specific images. In this thesis, an unsupervised visual object categorization system was implemented to categorize a set of unknown images. The system is unsupervised, and hence, it does not need known images to train the system which needs to be manually obtained. Therefore, the number of possible categories and images can be huge. The system implemented in the thesis extracts local features from the images. These local features are used to build a codebook. The local features and the codebook are then used to generate a feature vector for an image. Images are categorized based on the feature vectors. The system is able to categorize any given set of images based on the visual appearance of the images. Images that have similar image regions are grouped together in the same category. Thus, for example, images which contain cars are assigned to the same cluster. The unsupervised visual object categorization system can be used in many situations, e.g., in an Internet search engine. The system can categorize images for a user, and the user can then easily find a specific type of image.

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This is a test version of the kit for localising the Moving Image Collections (MIC) site, designed in the context of a research project undertaken by the Audiovisual and Multimedia Section (AVMS) of IFLA.

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The ability to create accurate geometric models of neuronal morphology is important for understanding the role of shape in information processing. Despite a significant amount of research on automating neuron reconstructions from image stacks obtained via microscopy, in practice most data are still collected manually. This paper describes Neuromantic, an open source system for three dimensional digital tracing of neurites. Neuromantic reconstructions are comparable in quality to those of existing commercial and freeware systems while balancing speed and accuracy of manual reconstruction. The combination of semi-automatic tracing, intuitive editing, and ability of visualizing large image stacks on standard computing platforms provides a versatile tool that can help address the reconstructions availability bottleneck. Practical considerations for reducing the computational time and space requirements of the extended algorithm are also discussed.

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Pós-graduação em Ciência da Computação - IBILCE

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Given a large image set, in which very few images have labels, how to guess labels for the remaining majority? How to spot images that need brand new labels different from the predefined ones? How to summarize these data to route the user’s attention to what really matters? Here we answer all these questions. Specifically, we propose QuMinS, a fast, scalable solution to two problems: (i) Low-labor labeling (LLL) – given an image set, very few images have labels, find the most appropriate labels for the rest; and (ii) Mining and attention routing – in the same setting, find clusters, the top-'N IND.O' outlier images, and the 'N IND.R' images that best represent the data. Experiments on satellite images spanning up to 2.25 GB show that, contrasting to the state-of-the-art labeling techniques, QuMinS scales linearly on the data size, being up to 40 times faster than top competitors (GCap), still achieving better or equal accuracy, it spots images that potentially require unpredicted labels, and it works even with tiny initial label sets, i.e., nearly five examples. We also report a case study of our method’s practical usage to show that QuMinS is a viable tool for automatic coffee crop detection from remote sensing images.

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Purpose To this day, the slit lamp remains the first tool used by an ophthalmologist to examine patient eyes. Imaging of the retina poses, however, a variety of problems, namely a shallow depth of focus, reflections from the optical system, a small field of view and non-uniform illumination. For ophthalmologists, the use of slit lamp images for documentation and analysis purposes, however, remains extremely challenging due to large image artifacts. For this reason, we propose an automatic retinal slit lamp video mosaicking, which enlarges the field of view and reduces amount of noise and reflections, thus enhancing image quality. Methods Our method is composed of three parts: (i) viable content segmentation, (ii) global registration and (iii) image blending. Frame content is segmented using gradient boosting with custom pixel-wise features. Speeded-up robust features are used for finding pair-wise translations between frames with robust random sample consensus estimation and graph-based simultaneous localization and mapping for global bundle adjustment. Foreground-aware blending based on feathering merges video frames into comprehensive mosaics. Results Foreground is segmented successfully with an area under the curve of the receiver operating characteristic curve of 0.9557. Mosaicking results and state-of-the-art methods were compared and rated by ophthalmologists showing a strong preference for a large field of view provided by our method. Conclusions The proposed method for global registration of retinal slit lamp images of the retina into comprehensive mosaics improves over state-of-the-art methods and is preferred qualitatively.

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Monte Carlo integration is firmly established as the basis for most practical realistic image synthesis algorithms because of its flexibility and generality. However, the visual quality of rendered images often suffers from estimator variance, which appears as visually distracting noise. Adaptive sampling and reconstruction algorithms reduce variance by controlling the sampling density and aggregating samples in a reconstruction step, possibly over large image regions. In this paper we survey recent advances in this area. We distinguish between “a priori” methods that analyze the light transport equations and derive sampling rates and reconstruction filters from this analysis, and “a posteriori” methods that apply statistical techniques to sets of samples to drive the adaptive sampling and reconstruction process. They typically estimate the errors of several reconstruction filters, and select the best filter locally to minimize error. We discuss advantages and disadvantages of recent state-of-the-art techniques, and provide visual and quantitative comparisons. Some of these techniques are proving useful in real-world applications, and we aim to provide an overview for practitioners and researchers to assess these approaches. In addition, we discuss directions for potential further improvements.

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A method for fast colour and geometric correction of a tiled display system is presented in this paper. Such kind of displays are a common choice for virtual reality applications and simulators, where a high resolution image is required. They are the cheapest and more flexible alternative for large image generation but they require a precise geometric and colour correction. The purpose of the proposed method is to correct the projection system as fast as possible so in case the system needs to be recalibrated it doesn’t interfere with the normal operation of the simulator or virtual reality application. This technique makes use of a single conventional webcam for both geometric and photometric correction. Some previous assumptions are made, like planar projection surface and negligibleintra-projector colour variation and black-offset levels. If these assumptions hold true, geometric and photometric seamlessness can be achievedfor this kind of display systems. The method described in this paper is scalable for an undefined number of projectors and completely automatic.

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At the present time almost all map libraries on the Internet are image collections generated by the digitization of early maps. This type of graphics files provides researchers with the possibility of accessing and visualizing historical cartographic information keeping in mind that this information has a degree of quality that depends upon elements such as the accuracy of the digitization process and proprietary constraints (e.g. visualization, resolution downloading options, copyright, use constraints). In most cases, access to these map libraries is useful only as a first approach and it is not possible to use those maps for scientific work due to the sparse tools available to measure, match, analyze and/or combine those resources with different kinds of cartography. This paper presents a method to enrich virtual map rooms and provide historians and other professional with a tool that let them to make the most of libraries in the digital era.

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This paper describes a low complexity strategy for detecting and recognizing text signs automatically. Traditional approaches use large image algorithms for detecting the text sign, followed by the application of an Optical Character Recognition (OCR) algorithm in the previously identified areas. This paper proposes a new architecture that applies the OCR to a whole lightly treated image and then carries out the text detection process of the OCR output. The strategy presented in this paper significantly reduces the processing time required for text localization in an image, while guaranteeing a high recognition rate. This strategy will facilitate the incorporation of video processing-based applications into the automatic detection of text sign similar to that of a smartphone. These applications will increase the autonomy of visually impaired people in their daily life.

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Using a 9.4 T MRI instrument, we have obtained images of the mouse brain response to photic stimulation during a period between deep anesthesia and the early stages of arousal. The large image enhancements we observe (often >30%) are consistent with literature results extrapolated to 9.4 T. However, there are also two unusual aspects to our findings. (i) The visual area of the brain responds only to changes in stimulus intensity, suggesting that we directly detect operations of the M visual system pathway. Such a channel has been observed in mice by invasive electrophysiology, and described in detail for primates. (ii) Along with the typical positive response in the area of the occipital portion of the brain containing the visual cortex, another area displays decreased signal intensity upon stimulation.