135 research outputs found

    Multiresolution Maximum Intensity Volume Rendering by Morphological Pyramids

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    Comparison of Morphological Pyramids for Multiresolution MIP Volume Rendering

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    A new class of morphological pyramids for multiresolution image analysis

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    Fusion of MultiSpectral and Panchromatic Images Based on Morphological Operators

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    International audienceNonlinear decomposition schemes constitute an alternative to classical approaches for facing the problem of data fusion. In this paper we discuss the application of this methodology to a popular remote sensing application called pansharpening, which consists in the fusion of a low resolution multispectral image and a high resolution panchromatic image. We design a complete pansharpening scheme based on the use of morphological half gradients operators and demonstrate the suitability of this algorithm through the comparison with state of the art approaches. Four datasets acquired by the Pleiades, Worldview-2, Ikonos and Geoeye-1 satellites are employed for the performance assessment, testifying the effectiveness of the proposed approach in producing top-class images with a setting independent of the specific sensor

    Visualisation volumique par projection du maximum d'intensité avec ondelettes

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    Les techniques d'acquisition en imagerie médicale génèrent de plus en plus de données. Les processus de visualisation demandent à être performants autant d'un point de vue vitesse de calcul que qualité de résultat. C'est pourquoi il est intéressant d'intégrer des outils de représentation de données efficaces comme les ondelettes, dans le but d'optimiser le stockage en mémoire, et les processus de traitement. Nous nous intéressons plus particulièrement à une méthode de visualisation très utilisée en imagerie médicale: la projection du maximum d'intensité (MIP); elle consiste à afficher uniquement la valeur maximale rencontrée sur chaque rayon de projection. Une nouvelle représentation des données volumiques est proposée s'inspirant de la théorie de l'ondelette morphologique et de l'algorithme proposé par Roerdink du MIP par représentation en pyramide d'adjonction. Deux approches de MIP progressif on été développées avec cette nouvelle représentation. La deuxième plus prometteuse permet une compression des données initiales de plus de 70% pour une qualité de résultats presque parfaite. Aussi, la vitesse d'exécution du MIP proposé rivalise avec celle des meilleurs algorithmes développés jusqu'à ce jour

    A Panorama on Multiscale Geometric Representations, Intertwining Spatial, Directional and Frequency Selectivity

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    The richness of natural images makes the quest for optimal representations in image processing and computer vision challenging. The latter observation has not prevented the design of image representations, which trade off between efficiency and complexity, while achieving accurate rendering of smooth regions as well as reproducing faithful contours and textures. The most recent ones, proposed in the past decade, share an hybrid heritage highlighting the multiscale and oriented nature of edges and patterns in images. This paper presents a panorama of the aforementioned literature on decompositions in multiscale, multi-orientation bases or dictionaries. They typically exhibit redundancy to improve sparsity in the transformed domain and sometimes its invariance with respect to simple geometric deformations (translation, rotation). Oriented multiscale dictionaries extend traditional wavelet processing and may offer rotation invariance. Highly redundant dictionaries require specific algorithms to simplify the search for an efficient (sparse) representation. We also discuss the extension of multiscale geometric decompositions to non-Euclidean domains such as the sphere or arbitrary meshed surfaces. The etymology of panorama suggests an overview, based on a choice of partially overlapping "pictures". We hope that this paper will contribute to the appreciation and apprehension of a stream of current research directions in image understanding.Comment: 65 pages, 33 figures, 303 reference

    Multi-scale pixel-based image fusion using multivariate empirical mode decomposition.

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    A novel scheme to perform the fusion of multiple images using the multivariate empirical mode decomposition (MEMD) algorithm is proposed. Standard multi-scale fusion techniques make a priori assumptions regarding input data, whereas standard univariate empirical mode decomposition (EMD)-based fusion techniques suffer from inherent mode mixing and mode misalignment issues, characterized respectively by either a single intrinsic mode function (IMF) containing multiple scales or the same indexed IMFs corresponding to multiple input images carrying different frequency information. We show that MEMD overcomes these problems by being fully data adaptive and by aligning common frequency scales from multiple channels, thus enabling their comparison at a pixel level and subsequent fusion at multiple data scales. We then demonstrate the potential of the proposed scheme on a large dataset of real-world multi-exposure and multi-focus images and compare the results against those obtained from standard fusion algorithms, including the principal component analysis (PCA), discrete wavelet transform (DWT) and non-subsampled contourlet transform (NCT). A variety of image fusion quality measures are employed for the objective evaluation of the proposed method. We also report the results of a hypothesis testing approach on our large image dataset to identify statistically-significant performance differences

    Multi-scale metric for objective synthesized image quality assessment for FTV

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    Основни допринос ове докторске дисертације је развој алгоритама за објективну процену визуелног квалитета слике синтетизоване применом ДИБР (Depth Image Based Rendering) техника које узрокују неуниформна изобличења у области ивица. Применом нелинеарних морфолошких филтара у мултирезолуционој декомпозицији слика код израчунавања предложене метрике, важне геометријске информације као што су ивице су добро очуване без помака и замућења у сликама на различитим скалама мултирезолуционе репрезентације. Израчунавањем МСЕ по подопсезима који садрже ивице, пиксел по пиксел, прецизно се мери разлика две мултирезолуционе репрезентације. Тако се највећи значај у процени квалитета додељује области ивица. Процене предложене метрике се добро поклапају са субјективним оценама.Osnovni doprinos ove doktorske disertacije je razvoj algoritama za objektivnu procenu vizuelnog kvaliteta slike sintetizovane primenom DIBR (Depth Image Based Rendering) tehnika koje uzrokuju neuniformna izobličenja u oblasti ivica. Primenom nelinearnih morfoloških filtara u multirezolucionoj dekompoziciji slika kod izračunavanja predložene metrike, važne geometrijske informacije kao što su ivice su dobro očuvane bez pomaka i zamućenja u slikama na različitim skalama multirezolucione reprezentacije. Izračunavanjem MSE po podopsezima koji sadrže ivice, piksel po piksel, precizno se meri razlika dve multirezolucione reprezentacije. Tako se najveći značaj u proceni kvaliteta dodeljuje oblasti ivica. Procene predložene metrike se dobro poklapaju sa subjektivnim ocenama.The main contribution of this doctoral thesis is the development of algorithms for objective DIBR-synthesized view quality assessment. DIBR algorithms introduce nonuniform geometric distortions affecting the edge coherency in the synthesized images.The non-linear morphological filters used in multi-scale image decompositions of the proposed metric maintain important geometric information such as edges across different resolution levels.Calculating MSE pixel-by-pixel through subbands in which the edges are extracted, the difference of the two multiresolution representations, the reference and the synthesized image, is precisely measured. In that way the importance of edge areas which are prone to synthesis artifacts is emphasized in the image quality assessment. The proposed metric has very good agreement with human judgment
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