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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 Video understanding
deals with understanding of video understanding. 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 obvi
ous overlap with computer vision. The main goal of computer
graphics is to generate and animate realistic looking images, and
videos. Re searchers in computer graphics are increasingly
employing techniques from computer vision to generate the synthetic
imagery. A good exam pIe of this is image-based rendering and
modeling techniques, in which geometry, appearance, and lighting is
derived from real images using computer vision techniques. Here the
shift is from synthesis to analy sis followed by synthesis. Image
processing has always overlapped with computer vision because they
both inherently work directly with images."
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 Video understanding
deals with understanding of video understanding. 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 obvi
ous overlap with computer vision. The main goal of computer
graphics is to generate and animate realistic looking images, and
videos. Re searchers in computer graphics are increasingly
employing techniques from computer vision to generate the synthetic
imagery. A good exam pIe of this is image-based rendering and
modeling techniques, in which geometry, appearance, and lighting is
derived from real images using computer vision techniques. Here the
shift is from synthesis to analy sis followed by synthesis. Image
processing has always overlapped with computer vision because they
both inherently work directly with images."
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