Kniha Video Mining Daniel Dementhon

Video Mining

Jazyk: Angličtina
Väzba: Brožovaná
Dostupnosť: Skladom u dodávateľa
Odosielame za 5-8 dní
149.54
Traditionally, scientific fields have defined boundaries, and scientists work on research problems w...

Informácie o knihe

Jazyk
Angličtina
Väzba
Kniha - Brožovaná
Vydalo
2010
Stránok
340
EAN
9781441953834
ISBN
1441953833
Enbook ID
06794576
Hmotnosť
545
Rozmery
155 x 235 x 20

Kompletný popis

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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