Vote for your favorite mineral in #MinCup25! - Sanidine vs. Hematite
It's a pair of often-overlooked classics as potassium feldspar sanidine competes with iron ore hematite.
Log InRegister
Quick Links : The Mindat ManualThe Rock H. Currier Digital LibraryMindat Newsletter [Free Download]
Home PageAbout MindatThe Mindat ManualHistory of MindatCopyright StatusWho We AreContact UsAdvertise on Mindat
Donate to MindatCorporate SponsorshipSponsor a PageSponsored PagesMindat AdvertisersAdvertise on Mindat
Learning CenterWhat is a mineral?The most common minerals on earthInformation for EducatorsMindat ArticlesThe ElementsThe Rock H. Currier Digital LibraryGeologic Time
Minerals by PropertiesMinerals by ChemistryAdvanced Locality SearchRandom MineralRandom LocalitySearch by minIDLocalities Near MeSearch ArticlesSearch GlossaryMore Search Options
Search For:
Mineral Name:
Locality Name:
Keyword(s):
 
The Mindat ManualAdd a New PhotoRate PhotosLocality Edit ReportCoordinate Completion ReportAdd Glossary Item
Mining CompaniesStatisticsUsersMineral MuseumsClubs & OrganizationsMineral Shows & EventsThe Mindat DirectoryDevice SettingsThe Mineral Quiz
Photo SearchPhoto GalleriesSearch by ColorNew Photos TodayNew Photos YesterdayMembers' Photo GalleriesPast Photo of the Day GalleryPhotography

Sun, Qianru, Liu, Hong, Harada, Tatsuya (2017) Online growing neural gas for anomaly detection in changing surveillance scenes. Pattern Recognition, 64. 187-201 doi:10.1016/j.patcog.2016.09.016

Advanced
   -   Only viewable:
Reference TypeJournal (article/letter/editorial)
TitleOnline growing neural gas for anomaly detection in changing surveillance scenes
JournalPattern Recognition
AuthorsSun, QianruAuthor
Liu, HongAuthor
Harada, TatsuyaAuthor
Year2017 (April)Volume64
PublisherElsevier BV
DOIdoi:10.1016/j.patcog.2016.09.016Search in ResearchGate
Generate Citation Formats
Mindat Ref. ID9366828Long-form Identifiermindat:1:5:9366828:0
GUID0
Full ReferenceSun, Qianru, Liu, Hong, Harada, Tatsuya (2017) Online growing neural gas for anomaly detection in changing surveillance scenes. Pattern Recognition, 64. 187-201 doi:10.1016/j.patcog.2016.09.016
Plain TextSun, Qianru, Liu, Hong, Harada, Tatsuya (2017) Online growing neural gas for anomaly detection in changing surveillance scenes. Pattern Recognition, 64. 187-201 doi:10.1016/j.patcog.2016.09.016
In(2017) Pattern Recognition Vol. 64. Elsevier BV

See Also

These are possibly similar items as determined by title/reference text matching only.

Doshi, Keval, Yilmaz, Yasin (2021) Online anomaly detection in surveillance videos with asymptotic bound on false alarm rate. Pattern Recognition, 114. 107865pp. doi:10.1016/j.patcog.2021.107865
Li, Xudong, Ye, Mao, Liu, Yiguang, Zhang, Feng, Liu, Dan, Tang, Song (2017) Accurate object detection using memory-based models in surveillance scenes. Pattern Recognition, 67. 73-84 doi:10.1016/j.patcog.2017.01.030
Dong, LI, LIU, Shulin, ZHANG, Hongli (2017) A method of anomaly detection and fault diagnosis with online adaptive learning under small training samples. Pattern Recognition, 64. 374-385 doi:10.1016/j.patcog.2016.11.026
Su, Bolan, Lu, Shijian (2017) Accurate recognition of words in scenes without character segmentation using recurrent neural network. Pattern Recognition, 63. 397-405 doi:10.1016/j.patcog.2016.10.016
Wu, Peng, Liu, Jing, Li, Mingming, Sun, Yujia, Shen, Fang (2020) Fast sparse coding networks for anomaly detection in videos. Pattern Recognition, 107. 107515pp. doi:10.1016/j.patcog.2020.107515
Cong, Yang, Yuan, Junsong, Liu, Ji (2013) Abnormal event detection in crowded scenes using sparse representation. Pattern Recognition, 46 (7). 1851-1864 doi:10.1016/j.patcog.2012.11.021
Carrera, Diego, Rossi, Beatrice, Fragneto, Pasqualina, Boracchi, Giacomo (2019) Online anomaly detection for long-term ECG monitoring using wearable devices. Pattern Recognition, 88. 482-492 doi:10.1016/j.patcog.2018.11.019
Zhu, Xiaobin, Liu, Jing, Wang, Jinqiao, Li, Changsheng, Lu, Hanqing (2014) Sparse representation for robust abnormality detection in crowded scenes. Pattern Recognition, 47 (5). 1791-1799 doi:10.1016/j.patcog.2013.11.018
Passalis, Nikolaos, Tefas, Anastasios (2017) Neural Bag-of-Features learning. Pattern Recognition, 64. 277-294 doi:10.1016/j.patcog.2016.11.014
Tu, Jiankai, Liu, Huan, Li, Chunguang (2024) Weighted subspace anomaly detection in high-dimensional space. Pattern Recognition, 146. 110056 doi:10.1016/j.patcog.2023.110056
Yao, Shun; Cheng, Yongmei; Yang, Fei; Mozerov, Mikhail G. (2025) Implicit neural representation model for camera relocalization in multiple scenes. Pattern Recognition, 168. doi:10.1016/j.patcog.2025.111791
 
and/or  
Mindat.org is an outreach project of the Hudson Institute of Mineralogy, a 501(c)(3) not-for-profit organization.
Copyright © mindat.org and the Hudson Institute of Mineralogy 1993-2025, except where stated. Most political location boundaries are Β© OpenStreetMap contributors. Mindat.org relies on the contributions of thousands of members and supporters. Founded in 2000 by Jolyon Ralph.
To cite: Ralph, J., Von Bargen, D., Martynov, P., Zhang, J., Que, X., Prabhu, A., Morrison, S. M., Li, W., Chen, W., & Ma, X. (2025). Mindat.org: The open access mineralogy database to accelerate data-intensive geoscience research. American Mineralogist, 110(6), 833–844. doi:10.2138/am-2024-9486.
Privacy Policy - Terms & Conditions - Contact Us / DMCA issues - Report a bug/vulnerability Current server date and time: September 7, 2025 12:09:30
Go to top of page