2,305 research outputs found
Interaction between high-level and low-level image analysis for semantic video object extraction
Authors of articles published in EURASIP Journal on Advances in Signal Processing are the copyright holders of their articles and have granted to any third party, in advance and in perpetuity, the right to use, reproduce or disseminate the article, according to the SpringerOpen copyright and license agreement (http://www.springeropen.com/authors/license)
Morphological operators for very low bit rate video coding
This paper deals with the use of some morphological tools for video coding at very low bit rates. Rather than describing a complete coding algorithm, the purpose of this paper is to focus on morphological connected operators and segmentation tools that have proved to be attractive for compression.Peer ReviewedPostprint (published version
Segmentation-based video coding:temporals links
This paper analyzes the main elements that a segmentation-based video coding approach should be based on so that it can address coding efficiency and content-based functionalities. Such elements can be defined as temporal linking and rate control. The basic features of such elements are discussed and, in both cases, a specific implementation is proposed.Peer ReviewedPostprint (published version
Image sequence analysis for emerging interactive multimedia services - The European COST 211 framework
Cataloged from PDF version of article.Flexibility and efficiency of coding, content extraction,
and content-based search are key research topics in
the field of interactive multimedia. Ongoing ISO MPEG-4 and
MPEG-7 activities are targeting standardization to facilitate such
services. European COST Telecommunications activities provide
a framework for research collaboration. COST 211bis and COST
211ter activities have been instrumental in the definition and
development of the ITU-T H.261 and H.263 standards for videoconferencing
over ISDN and videophony over regular phone
lines, respectively. The group has also contributed significantly
to the ISO MPEG-4 activities. At present a significant effort
of the COST 211ter group activities is dedicated toward image
and video sequence analysis and segmentation—an important
technological aspect for the success of emerging object-based
MPEG-4 and MPEG-7 multimedia applications. The current
work of COST 211 is centered around the test model, called
the Analysis Model (AM). The essential feature of the AM is
its ability to fuse information from different sources to achieve
a high-quality object segmentation. The current information
sources are the intermediate results from frame-based (still) color
segmentation, motion vector based segmentation, and changedetection-based
segmentation. Motion vectors, which form the
basis for the motion vector based intermediate segmentation, are
estimated from consecutive frames. A recursive shortest spanning
tree (RSST) algorithm is used to obtain intermediate color and
motion vector based segmentation results. A rule-based region
processor fuses the intermediate results; a postprocessor further
refines the final segmentation output. The results of the current
AM are satisfactory; it is expected that there will be further
improvements of the AM within the COST 211 project
Unsupervised Object Discovery and Tracking in Video Collections
This paper addresses the problem of automatically localizing dominant objects
as spatio-temporal tubes in a noisy collection of videos with minimal or even
no supervision. We formulate the problem as a combination of two complementary
processes: discovery and tracking. The first one establishes correspondences
between prominent regions across videos, and the second one associates
successive similar object regions within the same video. Interestingly, our
algorithm also discovers the implicit topology of frames associated with
instances of the same object class across different videos, a role normally
left to supervisory information in the form of class labels in conventional
image and video understanding methods. Indeed, as demonstrated by our
experiments, our method can handle video collections featuring multiple object
classes, and substantially outperforms the state of the art in colocalization,
even though it tackles a broader problem with much less supervision
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