Cargando…
Sample Training Based Wildfire Segmentation by 2D Histogram θ-Division with Minimum Error
A novel wildfire segmentation algorithm is proposed with the help of sample training based 2D histogram θ-division and minimum error. Based on minimum error principle and 2D color histogram, the θ-division methods were presented recently, but application of prior knowledge on them has not been explo...
Autores principales: | Zhao, Jianhui, Dong, Erqian, Sun, Mingui, Jia, Wenyan, Zhang, Dengyi, Yuan, Zhiyong |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2013
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3710650/ https://www.ncbi.nlm.nih.gov/pubmed/23878526 http://dx.doi.org/10.1155/2013/572393 |
Ejemplares similares
-
Variable bin size selection for periestimulus time histograms (PSTH) with minimum mean square error criteria
por: Heidarieh, SM, et al.
Publicado: (2015) -
Improved Algorithm for Gradient Vector Flow Based Active Contour Model Using Global and Local Information
por: Zhao, Jianhui, et al.
Publicado: (2013) -
Identification of Synaptic DGKθ Interactors That Stimulate DGKθ Activity
por: Barber, Casey N., et al.
Publicado: (2022) -
On θ(ω) continuity
por: Al Ghour, Samer, et al.
Publicado: (2020) -
Calculating minimum safety distance against wildfires at the wildland-urban interface in Chile and Spain
por: Castillo Soto, Miguel E., et al.
Publicado: (2022)