DynTex: A comprehensive database of dynamic textures. We present the DynTex database of high-quality dynamic texture videos. It consists of over 650 sequences of dynamic textures, mostly in everyday surroundings. Additionally, we propose a scheme for the manual annotation of the sequences based on a detailed analysis of the physical processes underlying the dynamic textures. Using this scheme we describe the texture sequences in terms of both visual structure and semantic content. The videos and annotations are made publicly available for scientific research.
Keywords for this software
References in zbMATH (referenced in 6 articles )
Showing results 1 to 6 of 6.
- Tiwari, Deepshikha; Tyagi, Vipin: Dynamic texture recognition based on completed volume local binary pattern (2016) ioport
- Heikkilä, Janne; Rahtu, Esa; Ojansivu, Ville: Local phase quantization for blur insensitive texture description (2014) ioport
- Chaudhry, Rizwan; Hager, Gregory; Vidal, René: Dynamic template tracking and recognition (2013)
- Ćulibrk, Dubravko; Mancas, Matei; Ćrnojevic, Vladimir: Dynamic texture recognition based on compression artifacts (2013) ioport
- Qiao, Yu-Long; Song, Chun-Yan; Wang, Fu-Shan: Wavelet-based dynamic texture classification using Gumbel distribution (2013) ioport
- Fazekas, Sándor; Amiaz, Tomer; Chetverikov, Dmitry; Kiryati, Nahum: Dynamic texture detection based on motion analysis (2009) ioport
Further publications can be found at: http://projects.cwi.nl/dyntex/reference.html