23,246 research outputs found

    Spatiotemporal Barcodes for Image Sequence Analysis

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    Taking as input a time-varying sequence of two-dimensional (2D) binary images, we develop an algorithm for computing a spatiotemporal 0–barcode encoding lifetime of connected components on the image sequence over time. This information may not coincide with the one provided by the 0–barcode encoding the 0–persistent homology, since the latter does not respect the principle that it is not possible to move backwards in time. A cell complex K is computed from the given sequence, being the cells of K classified as spatial or temporal depending on whether they connect two consecutive frames or not. A spatiotemporal path is defined as a sequence of edges of K forming a path such that two edges of the path cannot connect the same two consecutive frames. In our algorithm, for each vertex v ∈ K, a spatiotemporal path from v to the “oldest” spatiotemporally-connected vertex is computed and the corresponding spatiotemporal 0–bar is added to the spatiotemporal 0–barcode.Junta de Andalucía FQM-369Ministerio de Economía y Competitividad MTM2012-3270

    Hierarchical morphological segmentation for image sequence coding

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    This paper deals with a hierarchical morphological segmentation algorithm for image sequence coding. Mathematical morphology is very attractive for this purpose because it efficiently deals with geometrical features such as size, shape, contrast, or connectivity that can be considered as segmentation-oriented features. The algorithm follows a top-down procedure. It first takes into account the global information and produces a coarse segmentation, that is, with a small number of regions. Then, the segmentation quality is improved by introducing regions corresponding to more local information. The algorithm, considering sequences as being functions on a 3-D space, directly segments 3-D regions. A 3-D approach is used to get a segmentation that is stable in time and to directly solve the region correspondence problem. Each segmentation stage relies on four basic steps: simplification, marker extraction, decision, and quality estimation. The simplification removes information from the sequence to make it easier to segment. Morphological filters based on partial reconstruction are proven to be very efficient for this purpose, especially in the case of sequences. The marker extraction identifies the presence of homogeneous 3-D regions. It is based on constrained flat region labeling and morphological contrast extraction. The goal of the decision is to precisely locate the contours of regions detected by the marker extraction. This decision is performed by a modified watershed algorithm. Finally, the quality estimation concentrates on the coding residue, all the information about the 3-D regions that have not been properly segmented and therefore coded. The procedure allows the introduction of the texture and contour coding schemes within the segmentation algorithm. The coding residue is transmitted to the next segmentation stage to improve the segmentation and coding quality. Finally, segmentation and coding examples are presented to show the validity and interest of the coding approach.Peer ReviewedPostprint (published version

    Recursive image sequence segmentation by hierarchical models

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    This paper addresses the problem of image sequence segmentation. A technique using a sequence model based on compound random fields is presented. This technique is recursive in the sense that frames are processed in the same cadency as they are produced. New regions appearing in the sequence are detected by a morphological procedure.Peer ReviewedPostprint (published version

    High compression image and image sequence coding

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    The digital representation of an image requires a very large number of bits. This number is even larger for an image sequence. The goal of image coding is to reduce this number, as much as possible, and reconstruct a faithful duplicate of the original picture or image sequence. Early efforts in image coding, solely guided by information theory, led to a plethora of methods. The compression ratio reached a plateau around 10:1 a couple of years ago. Recent progress in the study of the brain mechanism of vision and scene analysis has opened new vistas in picture coding. Directional sensitivity of the neurones in the visual pathway combined with the separate processing of contours and textures has led to a new class of coding methods capable of achieving compression ratios as high as 100:1 for images and around 300:1 for image sequences. Recent progress on some of the main avenues of object-based methods is presented. These second generation techniques make use of contour-texture modeling, new results in neurophysiology and psychophysics and scene analysis

    Traffic image sequence classification

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    Práce představuje obecný přehled postupů používaných v aplikacích pro monitorování provozu. Jsou zde popsány různé přístupy pro řešení jednotlivých kroků procesu detekce vozidel. Je provedena analýza těchto metod. Dále se tato práce zaměřuje na návrh a realizaci komplexního robustního algoritmu pro detekci vozidel v reálném čase. Je založen na analýze video-sekvence pořízené statickou kamerou umístěnou na komunikaci. Zpracování sestává z mnoha kroků. Výsledkem jsou statistiky monitorování dopravní situace, jako je průměrná rychlost, počet vozidel a stupeň provozu.The article introduces a general survey of concepts used in traffic monitoring applications. It describes different approaches for solving particular steps of vehicle detection process. Analysis of these methods was performed. Furthermore this work focuses on the design and realization of complex robust algorithm for real-time vehicle detection. It is based on analysis of video-sequence acquired from static camera situated on highway. Processing consists of many steps. It starts with background subtraction and ends with traffic monitoring results, i.e. average speed, number of cars, level of service etc.

    Ultrasound Image Sequence Segmentation

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    V tejto práci sa nachádza základný popis princípu ultrasonografie, prehľad využívaných zobrazovacích módov, princíp kontrastného zobrazovania a prehľad základných segmentačných techník. Z uvedených metód boli v programe Matlab implementované segmentačné metódy založené na hranovej detekcii a narastaní oblasti. Navrhnuté algoritmy boli následne otestované na umelých a fantómových obrazových dátach.This paper presents basic principles of ultrasonography, review of different modes of medical ultrasound imaging, principle of contrast-enhanced ultrasonography and review of basic techniques of image segmentation. The individual methods based on edge detection and region growing were implemented in Matlab. The performance of algorithms were tested in each category using synthetic and phantom image data.

    Construction of ATS Cloud Console Final Report

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    ATS cloud console for rapid analysis of cloud image sequence
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