Video Mining:
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...
Gespeichert in:
Weitere Verfasser: | , , |
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Format: | Elektronisch E-Book |
Sprache: | English |
Veröffentlicht: |
New York, NY
Springer US
2003
|
Ausgabe: | 1st ed. 2003 |
Schriftenreihe: | The International Series in Video Computing
6 |
Schlagworte: | |
Online-Zugang: | UBY01 Volltext |
Zusammenfassung: | 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 |
Beschreibung: | 1 Online-Ressource (IX, 340 p) |
ISBN: | 9781475769289 |
DOI: | 10.1007/978-1-4757-6928-9 |
Internformat
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520 | |a 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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dewey-full | 005.73 |
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discipline | Informatik |
discipline_str_mv | Informatik |
doi_str_mv | 10.1007/978-1-4757-6928-9 |
edition | 1st ed. 2003 |
format | Electronic eBook |
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id | DE-604.BV047064372 |
illustrated | Not Illustrated |
index_date | 2024-07-03T16:12:22Z |
indexdate | 2024-07-10T09:01:34Z |
institution | BVB |
isbn | 9781475769289 |
language | English |
oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032471484 |
oclc_num | 1227480650 |
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owner | DE-706 |
owner_facet | DE-706 |
physical | 1 Online-Ressource (IX, 340 p) |
psigel | ZDB-2-SCS ZDB-2-SCS_2000/2004 ZDB-2-SCS ZDB-2-SCS_2000/2004 |
publishDate | 2003 |
publishDateSearch | 2003 |
publishDateSort | 2003 |
publisher | Springer US |
record_format | marc |
series2 | The International Series in Video Computing |
spelling | Video Mining edited by Azriel Rosenfeld, David Doermann, Daniel DeMenthon 1st ed. 2003 New York, NY Springer US 2003 1 Online-Ressource (IX, 340 p) txt rdacontent c rdamedia cr rdacarrier The International Series in Video Computing 6 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 Data Structures and Information Theory Multimedia Information Systems Electrical Engineering Data structures (Computer science) Multimedia information systems Electrical engineering Data Mining (DE-588)4428654-5 gnd rswk-swf Video (DE-588)4078895-7 gnd rswk-swf Video (DE-588)4078895-7 s Data Mining (DE-588)4428654-5 s DE-604 Rosenfeld, Azriel edt Doermann, David edt DeMenthon, Daniel edt Erscheint auch als Druck-Ausgabe 9781441953834 Erscheint auch als Druck-Ausgabe 9781402075490 Erscheint auch als Druck-Ausgabe 9781475769296 https://doi.org/10.1007/978-1-4757-6928-9 Verlag URL des Eerstveröffentlichers Volltext |
spellingShingle | Video Mining Data Structures and Information Theory Multimedia Information Systems Electrical Engineering Data structures (Computer science) Multimedia information systems Electrical engineering Data Mining (DE-588)4428654-5 gnd Video (DE-588)4078895-7 gnd |
subject_GND | (DE-588)4428654-5 (DE-588)4078895-7 |
title | Video Mining |
title_auth | Video Mining |
title_exact_search | Video Mining |
title_exact_search_txtP | Video Mining |
title_full | Video Mining edited by Azriel Rosenfeld, David Doermann, Daniel DeMenthon |
title_fullStr | Video Mining edited by Azriel Rosenfeld, David Doermann, Daniel DeMenthon |
title_full_unstemmed | Video Mining edited by Azriel Rosenfeld, David Doermann, Daniel DeMenthon |
title_short | Video Mining |
title_sort | video mining |
topic | Data Structures and Information Theory Multimedia Information Systems Electrical Engineering Data structures (Computer science) Multimedia information systems Electrical engineering Data Mining (DE-588)4428654-5 gnd Video (DE-588)4078895-7 gnd |
topic_facet | Data Structures and Information Theory Multimedia Information Systems Electrical Engineering Data structures (Computer science) Multimedia information systems Electrical engineering Data Mining Video |
url | https://doi.org/10.1007/978-1-4757-6928-9 |
work_keys_str_mv | AT rosenfeldazriel videomining AT doermanndavid videomining AT dementhondaniel videomining |