| 词条 | Dynamic texture |
| 释义 |
Dynamic texture ( sometimes referred to as temporal texture) is the texture with motion which is can found in videos of sea-waves, fire, smoke, wavy trees, etc.[1][2] Dynamic texture has a spatially repetitive pattern with time-varying visual pattern.[3] Modeling and analyzing dynamic texture is a topic of images processing and pattern recognition in computer vision. Extracting features that describe the dynamic texture can be utilized for tasks of images sequences classification, segmentation, recognition and retrieval.Comparing with texture found within static images, analyzing dynamic texture is a challenging problem.[2] It is important that the extracted features from dynamic texture combine motion and appearance description, and also be invariance to some transformation such as rotation,translation and illumination.[2] Analysis methods of dynamic textureThe methods of dynamic texture recognition can categorized as follows:[3]
Applications- Segmenting the sequence images of natural scenes.[7] This helps on differentiate between streets and grass alongside these streets which could be used in the application of navigations.- Motion detection : Dynamic texture features extracted from footage videos can be exploited to detect abnormal crowd activities.[8]- Video classification: video of natural scenes or other scenes that exhibit dynamic textures.- Video retrieval : Dynamic textures can be employed as a feature retrieve videos that contain, for example, sea-waves, smoke, clouds, wavy trees.References1. ^{{Cite web|url=https://ieeexplore.ieee.org/document/560871|title=Temporal texture modeling - IEEE Conference Publication|website=ieeexplore.ieee.org|access-date=2018-12-14}} 2. ^1 2 {{Cite web|url=https://ieeexplore.ieee.org/document/4160945|title=Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions - IEEE Journals & Magazine|website=ieeexplore.ieee.org|access-date=2018-12-14}} 3. ^1 {{Citation|last=Péteri|first=Renaud|title=A Brief Survey of Dynamic Texture Description and Recognition|date=2005|work=Computer Recognition Systems|pages=17–26|series=Advances in Soft Computing|publisher=Springer, Berlin, Heidelberg|doi=10.1007/3-540-32390-2_2|isbn=9783540250548|last2=Chetverikov|first2=Dmitry|citeseerx=10.1.1.64.4707}} 4. ^{{Cite web|url=https://ieeexplore.ieee.org/abstract/document/711871|title=Feature extraction of temporal texture based on spatiotemporal motion trajectory - IEEE Conference Publication|website=ieeexplore.ieee.org|access-date=2018-12-14}} 5. ^{{Cite book|last=Bergen|first=James R.|last2=Wildes|first2=Richard P.|date=2000-06-26|title=Qualitative Spatiotemporal Analysis Using an Oriented Energy Representation|journal=Computer Vision — ECCV 2000|series=Lecture Notes in Computer Science|publisher=Springer, Berlin, Heidelberg|pages=768–784|doi=10.1007/3-540-45053-X_49|isbn=9783540676867|citeseerx=10.1.1.189.3015}} 6. ^{{Cite web|url=https://ieeexplore.ieee.org/abstract/document/1039981|title=Video texture indexing using spatio-temporal wavelets - IEEE Conference Publication|website=ieeexplore.ieee.org|access-date=2018-12-14}} 7. ^{{Cite book|last=Doretto|last2=Cremers|last3=Favaro|last4=Soatto|date=October 2003|title=Dynamic texture segmentation|url=https://ieeexplore.ieee.org/abstract/document/1238632/|journal=Proceedings Ninth IEEE International Conference on Computer Vision|pages=1236–1242 vol.2|doi=10.1109/ICCV.2003.1238632|isbn=978-0-7695-1950-0|citeseerx=10.1.1.324.456}} 8. ^{{Cite journal|last=Moore|first=Simon C.|last2=Marshall|first2=David|last3=Rosin|first3=Paul L.|last4=Lloyd|first4=Kaelon|date=2017-05-01|title=Detecting violent and abnormal crowd activity using temporal analysis of grey level co-occurrence matrix (GLCM)-based texture measures|journal=Machine Vision and Applications|volume=28|issue=3–4|pages=361–371|doi=10.1007/s00138-017-0830-x|issn=1432-1769}} 3 : Image processing|Computer vision|Pattern recognition |
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