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Traditionally, scientific fields have defined boundaries, and
scientists work on research problems within those boundaries.
However, from time to time those boundaries get shifted or blurred
to evolve new fields. For instance, the original goal of computer
vision was to understand a single image of a scene, by identifying
objects, their structure, and spatial arrangements. This has been
referred to as image understanding. Recently, computer vision has
gradually been making the transition away from understanding single
images to analyzing image sequences, or video understanding. Video
understanding deals with understanding of video sequences, e. g.,
recognition of gestures, activities, facial expressions, etc. The
main shift in the classic paradigm has been from the recognition of
static objects in the scene to motion-based recognition of actions
and events. Video understanding has overlapping research problems
with other fields, therefore blurring the fixed boundaries.
Computer graphics, image processing, and video databases have
obvious overlap with computer vision. The main goal of computer
graphics is to gener ate and animate realistic looking images, and
videos. Researchers in computer graphics are increasingly employing
techniques from computer vision to gen erate the synthetic imagery.
A good example of this is image-based rendering and modeling
techniques, in which geometry, appearance, and lighting is de rived
from real images using computer vision techniques. Here the shift
is from synthesis to analysis followed by synthesis."
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Structural, Syntactic, and Statistical Pattern Recognition - Joint IAPR International Workshop, SSPR & SPR 2008, Orlando, USA, December 4-6, 2008. Proceedings (Paperback, 2008 ed.)
Niels da Vitoria Lobo, Takis Kasparis, Michael Georgiopoulos, Fabio Roli, James Kwok, …
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R3,187
Discovery Miles 31 870
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Ships in 10 - 15 working days
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This volume in the Springer Lecture Notes in Computer Science
(LNCS) series contains 98 papers presented at the S+SSPR 2008
workshops. S+SSPR 2008 was the sixth time that the SPR and SSPR
workshops organized by Technical Committees, TC1 and TC2, of the
International Association for Pattern Rec- nition (IAPR) wereheld
as joint workshops. S+SSPR 2008was held in Orlando, Florida, the
family entertainment capital of the world, on the beautiful campus
of the University of Central Florida, one of the up and coming
metropolitan universities in the USA. S+SSPR 2008 was held during
December 4-6, 2008 only a few days before the 19th International
Conference on Pattern Recog- tion(ICPR2008), whichwasheldin Tampa,
onlytwo hoursawayfromOrlando, thus giving the opportunity of both
conferences to attendees to enjoy the many attractions o?ered by
two neighboring cities in the state of Florida. SPR 2008 and SSPR
2008 received a total of 175 paper submissions from many di?erent
countries around the world, thus giving the workshop an int-
national clout, as was the case for past workshops. This volume
contains 98 accepted papers: 56 for oral presentations and 42 for
poster presentations. In addition to parallel oral sessions for SPR
and SSPR, there was also one joint oral session with papers of
interest to both the SPR and SSPR communities. A recent trend that
has emerged in the pattern recognition and machine lea- ing
research communities is the study of graph-based methods that
integrate statistical andstructural approache
Traditionally, scientific fields have defined boundaries, and
scientists work on research problems within those boundaries.
However, from time to time those boundaries get shifted or blurred
to evolve new fields. For instance, the original goal of computer
vision was to understand a single image of a scene, by identifying
objects, their structure, and spatial arrangements. This has been
referred to as image understanding. Recently, computer vision has
gradually been making the transition away from understanding single
images to analyzing image sequences, or video understanding. Video
understanding deals with understanding of video sequences, e. g.,
recognition of gestures, activities, facial expressions, etc. The
main shift in the classic paradigm has been from the recognition of
static objects in the scene to motion-based recognition of actions
and events. Video understanding has overlapping research problems
with other fields, therefore blurring the fixed boundaries.
Computer graphics, image processing, and video databases have
obvious overlap with computer vision. The main goal of computer
graphics is to gener ate and animate realistic looking images, and
videos. Researchers in computer graphics are increasingly employing
techniques from computer vision to gen erate the synthetic imagery.
A good example of this is image-based rendering and modeling
techniques, in which geometry, appearance, and lighting is de rived
from real images using computer vision techniques. Here the shift
is from synthesis to analysis followed by synthesis."
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