333 research outputs found
Motion textures: mixed-state markov fields and segmentation
No full text[ES] El objeto de este trabajo es la modelización de movimiento en secuencias de imágenes que presentan cierta dinámica estacionaria y homogénea. En este caso se adopta un modelo de Campos Aleatorios Markovianos con estados mixtos, como representación de las llamadas texturas de movimiento. El enfoque consiste en describir la distribución espacial de algún tipo de medida de movimiento, la cual consiste de dos tipos de valores: una componente discreta relativa a la ausencia de movimiento y una parte continua para mediciones diferentes de cero. Se proponen varias extensiones importantes y se aplica el modelo al problema de segmentación de texturas, tanto en secuencias sintéticas como reales. [EN] The aim of this work is the modelling of motion in image sequences that show some stationary and homogeneous dynamic. We adopt the mixed-state Markov Random Fields (MRF) models to represent the socalled motion textures. The approach consists in describing the spatial distribution of some motion measurements which exhibit values of two types: a discrete component related to the absence of motion and a continuous part for measurements different from zero. We propose several significant extensions to this model and apply it to the problem of motion texture segmentation on synthetic and real sequences.Crivelli, T.; Cernuschi-Frias, B.; Bouthemy, P. (2007). Texturas de Movimiento: Campos Markovianos Mixtos y Segmentacion. Revista Iberoamericana de Automática e Informática industrial. 4(4):80-86. https://riunet.upv.es/handle/10251/145949OJS80864
Intensity-based methods for fully automated registration in 2D and 3D CLEM
No full textInternational audienc
Détermination du mouvement apparent dans une séquence d'images: Extraction de primitives locales, structuration intermédiaire, estimation du champ des vitesses
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An automatic image-based registration method for correlative light-electron microscopy
No full textInternational audienceCorrelative light-electron microscopy (CLEM) enables to combine information on cell dynamics, studied with light microscopy (LM) techniques, and cell ultrastructure provided by electron microscopy systems (EM), for a better understanding of cell mechanisms.Registration of LM and EM modalities is an open and difficult problem since LM and EM images are very different both in field-of-view, pixel size, image size, and appearance. We will present a user-friendly image-based automatic registration method to overlay LM and EM images.It comprises three steps: 1) Laplacian of Gaussian (LoG) representation of images with an adaptive associated scale (or blurring), which provides more comparable appearance for the LM and EM images; 2) Search of the region corresponding to the LM region of interest (ROI) in the EM image (or the other way around), using a patch-based exhaustive search method. Several similarity criteria have been compared, based on the LoG-value histograms and Local Directional Pattern (LDP) features; 3) Pre-registration of LM and EM images using the shift component given by the previous step. The rotation between LM and EM images can then be computed in two different ways: if there is a visible alignment of elements in the image, an axis can be fitted through these elements in both LM and EM and the angle between the axis is computed. Otherwise, the rotation angle is estimated through an exhaustive search using mutual information. The registration is finally completed by a refinement using mutual information and an affine geometric transformation to overlay both images. This approach is able to locate the LM-ROI in the EM image, or conversely the EM patch in the LM image, as validated by experiments performed on real 2D CLEM image sets supplied by Institut Curie. The overall method requires no parameter tuning and no specific knowledge to be used. The user has just to specify the bounding box of the ROI in the LM (or EM) image as input. We will also present preliminary results on 3D CLEM images
Détermination du mouvement apparent dans une séquence d'images: Extraction de primitives locales, structuration intermédiaire, estimation du champ des vitesses
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Une brève histoire du traitement d’image
No full textInternational audienceLe monde de l'image a connu des bouleversements liés à des avancées scientifiques majeures. Depuis des décennies, les images numériques ont peu à peu envahi les domaines scientifiques, industriels et sociétaux, et donc notre quotidien. Faisons le point sur cette révolution en marche
A maximum-likelihood framework for determining moving edges in image sequences
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A maximum-likelihood framework for determining moving edges in image sequences
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Detection and tracking of moving objects based on a statistical regularization method in space and time
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