about
Data issues in the life sciencesLandmark-free geometric methods in biological shape analysisFully-automated identification of fish species based on otolith contour: using short-time Fourier transform and discriminant analysis (STFT-DA)Accuracy and consistency of grass pollen identification by human analysts using electron micrographs of surface ornamentationClassification of grass pollen through the quantitative analysis of surface ornamentation and textureLeaf morphology, taxonomy and geometric morphometrics: a simplified protocol for beginnersDynamic species classification of microorganisms across time, abiotic and biotic environments-A sliding window approach.Automated identification of insect vectors of Chagas disease in Brazil and Mexico: the Virtual Vector Lab.Looking inside the Ocean: Toward an Autonomous Imaging System for Monitoring Gelatinous Zooplankton.A morphometric analysis of vegetation patterns in dryland ecosystems.A semi-automated image analysis procedure for in situ plankton imaging systems.Semi-automated image analysis for the assessment of megafaunal densities at the Arctic deep-sea observatory HAUSGARTEN.Phenotypic plasticity of southern ocean diatoms: key to success in the sea ice habitat?New data-driven method from 3D confocal microscopy for calculating phytoplankton cell biovolume.A novel application of motion analysis for detecting stress responses in embryos at different stages of developmentToward an Automated Identification of Anastrepha Fruit Flies in the fraterculus group (Diptera, Tephritidae).DNA barcodes for bio-surveillance: regulated and economically important arthropod plant pests.Machine learning bandgaps of double perovskites.Accelerating materials property predictions using machine learning.Principles and methods for automated palynology.A new fully automated approach for aligning and comparing shapes.Automatic determination of NET (neutrophil extracellular traps) coverage in fluorescent microscopy images.Fostering the rebirth of natural history.Quantitative 3D-imaging for cell biology and ecology of environmental microbial eukaryotes.Minimum action principle and shape dynamics.Automatic plankton image classification combining multiple view features via multiple kernel learning.Ant genera identification using an ensemble of convolutional neural networks.Estimating the magnitude of morphoscapes: how to measure the morphological component of biodiversity in relation to habitats using geometric morphometrics.Citizen crowds and experts: observer variability in image-based plant phenotyping.Lack of well-maintained natural history collections and taxonomists in megadiverse developing countries hampers global biodiversity explorationPlant Species Identification Using Computer Vision Techniques: A Systematic Literature Review.Dead or alive? Viability assessment of micro- and mesoplanktonDELPHI—fast and adaptive computational laser point detection and visual footprint quantification for arbitrary underwater image collectionsRecoMIA—Recommendations for Marine Image Annotation: Lessons Learned and Future DirectionsDeriving image features for autonomous classification from time-series recurrence plotsTracking Fish Abundance by Underwater Image Recognition
P2860
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P2860
description
2010 nî lūn-bûn
@nan
2010 թուականի Սեպտեմբերին հրատարակուած գիտական յօդուած
@hyw
2010 թվականի սեպտեմբերին հրատարակված գիտական հոդված
@hy
2010年の論文
@ja
2010年論文
@yue
2010年論文
@zh-hant
2010年論文
@zh-hk
2010年論文
@zh-mo
2010年論文
@zh-tw
2010年论文
@wuu
name
Time to automate identification.
@ast
Time to automate identification.
@en
type
label
Time to automate identification.
@ast
Time to automate identification.
@en
prefLabel
Time to automate identification.
@ast
Time to automate identification.
@en
P2093
P356
P1433
P1476
Time to automate identification.
@en
P2093
Mark Benfield
Norman MacLeod
Phil Culverhouse
P2888
P304
P356
10.1038/467154A
P407
P577
2010-09-01T00:00:00Z
P5875
P6179
1020215414