GMANet: Gradient Mask Attention Network for Finding Clearest Human Fecal Microscopic Image in Autofocus Process
The intelligent recognition of formed elements in microscopic images is a research hotspot. Whether the microscopic image is clear or blurred is the key factor affecting the recognition accuracy. Microscopic images of human feces contain numerous items, such as undigested food, epithelium, bacteria...
Guardado en:
Autores principales: | Xiangzhou Wang, Lin Liu, Xiaohui Du, Jing Zhang, Guangming Ni, Juanxiu Liu |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
MDPI AG
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/3d3d8aa63f3b4a5d9b7d998a57841612 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Improved Sensitivity of Dual-Axis Micro-Mechanical Probe for Friction Force Microscope
por: Hiroaki Amakawa, et al.
Publicado: (2008) -
Fish Segmentation in Sonar Images by Mask R-CNN on Feature Maps of Conditional Random Fields
por: Chin-Chun Chang, et al.
Publicado: (2021) -
Vertical-Objective-Based Ellipsometric Microscope for Real-Time Observation of nm-Thick Lubricant Films
por: Liu Qingqing, et al.
Publicado: (2012) -
Relations fecal coliforms/ fecal Streptococci as indicators of the origin of fecal pollution in urban and rural water bodies of Temuco, Chile
por: Rivera,Reinaldo, et al.
Publicado: (2010) -
Media Adaptation of Mask Making in Malang: Study of Functional and Process for Making Fiber Masks
por: Izam Ismail
Publicado: (2021)