The present study is devoted to analyze the compatibility of yttria-stabilized zirconia thin films prepared by pulsed laser
deposition technique for developing new silicon-based micro devices for micro solid oxide fuel cells applications. Yttriastabilized
zirconia free-standing membranes with thicknesses from 60 to 240 nm and surface areas between 50x50 μm2
and 820x820 μm2 were fabricated on micromachined Si/SiO2/Si3N4 substrates. Deposition process was optimized for
deposition temperatures from 200ºC to 800ºC. A complete mechanical study comprising thermomechanical stability,
residual stress of the membranes and annealing treatment as well as a preliminary electrical characterization of ionic
conductivity was performed in order to evaluate the best processing parameters for the yttria-stabilized zirconia
membranes.
The representation of video information in terms of its content is at
the foundation of many multimedia applications, such as broadcasting,
content-based information retrieval, interactive video, remote
surveillance and entertainment. In particular, object-based
representation consists in decomposing the video content into a
collection of meaningful objects. This approach offers a broad range
of capabilities in terms of access, manipulation and interaction with
the visual content. The basic difference when compared with
pixel-based procedures is that instead of processing individual
pixels, image objects are used in the representation. To exploit the
benefits of object-based representation, multimedia applications need
automatic techniques for extracting such objects from video data, a
problem that still remains largely unsolved. In this paper, we first
review the extraction techniques that enable the separation of
foreground objects from the background. Their field of applicability
and their limitations are discussed. Next, automatic tools for
evaluating their performances are introduced. The major applications
that benefit from an object-based approach are then analysed. Finally,
we discuss some open research issues in object-based video.
Shadow segmentation is a critical issue for systems aiming at
extracting, tracking or recognizing objects in a given scene. Shadows
can in fact modify the shape and colour of objects and therefore
affect scene analysis and interpretation systems in many applications,
such as video database search and retrieval, as well as video analysis
in applications such as video surveillance. We present a shadow
segmentation algorithm which includes two stages. The first stage
extracts moving cast shadows in each frame of the sequence. The second
stage tracks the extracted shadows in the subsequent frames. Tentative
moving shadow regions are first identified based on spectral and
geometrical properties of shadows. In order to confirm this tentative
identification, shadow regions are then tracked over time. This second
stage aims at exploiting the prior knowledge of a shadow detected in
previous frames by evaluating its temporal behaviour. Shadow tracking
is a difficult task, since colour, texture, and motion features in
shadow regions cannot be used for solving the correspondence
problem. Colour and texture change according to changes in the
background's characteristics. The measurement of motion cannot be
reliably computed for shadows. Therefore shadows may be described only
by a limited amount of information. The proposed tracking algorithm
makes use of this information and provides a reliability estimation of
shadow recognition results of the first stage over time. This temporal
analysis eliminates the possible ambiguities of the first stage and
improves the efficiency of the overall shadow detection algorithm. The
benefit of the proposed shadow segmentation and tracking algorithm is
evaluated on both indoor and outdoor scenes. The obtained results are
validated based on subjective as well as objective comparisons.
KEYWORDS: Video, Video surveillance, Visualization, Databases, Video coding, Multimedia, Cameras, Video compression, Video processing, Computer programming
We present an MPEG--7 compliant description of generic video sequences aiming at their scalable transmission and reconstruction. The proposed method allows efficient and flexible video coding while keeping the advantages of textual descriptions in database applications. Visual objects are described in terms of their shape, color, texture and motion; these features can be extracted automatically and are sufficient in a wide range of applications. To permit partial sequence reconstruction, at least one simple qualitative as well as a quantitative descriptor is provided for each feature. In addition, we propose a structure for the organization of the descriptors into objects and scenes and some possible applications for our method. Experimental results obtained with news and video surveillance sequences validate our method and highlight its main features.
This paper introduces a system for video object extraction useful for general applications where foreground objects move within a slow changing background. Surveillance of indoor and outdoor sequences is a typical example. The originality of the approach resides in two related components. First, the statistical change detection used in the system does not require any sophisticated parametric tuning as it is based on a probabilistic method. Second, the change is detected between a current instance of the scene and a reference that is updated continuously to take into account slow variation of the background. Simulation results show that the proposed scheme performs well in extracting video objects, with stability and good accuracy, while being of relative reduced complexity.
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