Classifying black and white spruce pollen using layered machine learning.
about
Visual Recognition Software for Binary Classification and Its Application to Spruce Pollen IdentificationTowards a morphological metric of assemblage dynamics in the fossil record: a test case using planktonic foraminiferaAccuracy and consistency of grass pollen identification by human analysts using electron micrographs of surface ornamentationA neotropical Miocene pollen database employing image-based search and semantic modelingClassification of grass pollen through the quantitative analysis of surface ornamentation and texturePrinciples and methods for automated palynology.Separating morphologically similar pollen types using basic shape features from digital images: A preliminary study(1.).Comparative performance of airyscan and structured illumination superresolution microscopy in the study of the surface texture and 3D shape of pollen.Three-dimensional label-free imaging and analysis of Pinus pollen grains using optical diffraction tomography.Looking forward through the past: identification of 50 priority research questions in palaeoecologyIdentifying the pollen of an extinct spruce species in the Late Quaternary sediments of the Tunica Hills region, south-eastern United StatesImproving classification of pollen grain images of the POLEN23E dataset through three different applications of deep learning convolutional neural networksRecent methodological advances in Quaternary palaeoecological proxies
P2860
Q28600944-110C318C-4D47-418C-B55A-789CCBE145ACQ28601773-7CDD63AC-5B79-4BD3-9769-FCBB52344272Q28654338-0F22B648-A299-4D5B-BB4E-20E947620094Q28654341-DA82B0B3-7358-4C14-A32E-C452822578ADQ28680510-A8D6D613-3B04-47AB-9C1D-976AAB7E9060Q38245461-D96A3D09-5B6F-47AD-BD6F-E400667889D6Q40220879-77C0273C-B479-4B7F-94D6-6FC3E4223A3CQ40394224-3B4B4F5A-E8AC-4D7D-A378-BBE45367875AQ49167362-12F94884-CA21-4228-96DD-02BD590AF85AQ56271192-72AEC111-C102-4A12-B8AD-B18F9172EA49Q57198291-AC77E367-8002-4770-B1DB-2950C68EEBDEQ58741335-92C5F5F5-1741-4C36-90F7-5C3C525EED50Q58861454-35BDE696-954D-49BD-89DF-F396A88265BB
P2860
Classifying black and white spruce pollen using layered machine learning.
description
2012 nî lūn-bûn
@nan
2012年の論文
@ja
2012年学术文章
@wuu
2012年学术文章
@zh
2012年学术文章
@zh-cn
2012年学术文章
@zh-hans
2012年学术文章
@zh-my
2012年学术文章
@zh-sg
2012年學術文章
@yue
2012年學術文章
@zh-hant
name
Classifying black and white spruce pollen using layered machine learning.
@en
Classifying black and white spruce pollen using layered machine learning.
@nl
type
label
Classifying black and white spruce pollen using layered machine learning.
@en
Classifying black and white spruce pollen using layered machine learning.
@nl
prefLabel
Classifying black and white spruce pollen using layered machine learning.
@en
Classifying black and white spruce pollen using layered machine learning.
@nl
P2093
P1433
P1476
Classifying black and white spruce pollen using layered machine learning.
@en
P2093
Cassandra Wesseln
David K Tcheng
Pietra G Mueller
P304
P356
10.1111/J.1469-8137.2012.04291.X
P407
P577
2012-09-03T00:00:00Z