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      Taxon and trait recognition from digitized herbarium specimens using deep convolutional neural networks

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          Most cited references 13

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          COMPARATIVE STUDIES OF LEAF FORM: ASSESSING THE RELATIVE ROLES OF SELECTIVE PRESSURES AND PHYLOGENETIC CONSTRAINTS

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            Is Open Access

            Deep machine learning provides state-of-the-art performance in image-based plant phenotyping

            Abstract In plant phenotyping, it has become important to be able to measure many features on large image sets in order to aid genetic discovery. The size of the datasets, now often captured robotically, often precludes manual inspection, hence the motivation for finding a fully automated approach. Deep learning is an emerging field that promises unparalleled results on many data analysis problems. Building on artificial neural networks, deep approaches have many more hidden layers in the network, and hence have greater discriminative and predictive power. We demonstrate the use of such approaches as part of a plant phenotyping pipeline. We show the success offered by such techniques when applied to the challenging problem of image-based plant phenotyping and demonstrate state-of-the-art results (>97% accuracy) for root and shoot feature identification and localization. We use fully automated trait identification using deep learning to identify quantitative trait loci in root architecture datasets. The majority (12 out of 14) of manually identified quantitative trait loci were also discovered using our automated approach based on deep learning detection to locate plant features. We have shown deep learning–based phenotyping to have very good detection and localization accuracy in validation and testing image sets. We have shown that such features can be used to derive meaningful biological traits, which in turn can be used in quantitative trait loci discovery pipelines. This process can be completely automated. We predict a paradigm shift in image-based phenotyping bought about by such deep learning approaches, given sufficient training sets.
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              Subject independent facial expression recognition with robust face detection using a convolutional neural network.

              Reliable detection of ordinary facial expressions (e.g. smile) despite the variability among individuals as well as face appearance is an important step toward the realization of perceptual user interface with autonomous perception of persons. We describe a rule-based algorithm for robust facial expression recognition combined with robust face detection using a convolutional neural network. In this study, we address the problem of subject independence as well as translation, rotation, and scale invariance in the recognition of facial expression. The result shows reliable detection of smiles with recognition rate of 97.6% for 5600 still images of more than 10 subjects. The proposed algorithm demonstrated the ability to discriminate smiling from talking based on the saliency score obtained from voting visual cues. To the best of our knowledge, it is the first facial expression recognition model with the property of subject independence combined with robustness to variability in facial appearance.
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                Author and article information

                Journal
                Botany Letters
                Botany Letters
                Informa UK Limited
                2381-8107
                2381-8115
                March 13 2018
                October 02 2018
                March 13 2018
                October 02 2018
                : 165
                : 3-4
                : 377-383
                Affiliations
                [1 ] Data and Modelling Centre, Senckenberg Biodiversity and Climate Research Centre (SBiK-F) , Frankfurt am Main, Germany
                [2 ] Department of Mathematics and Computer Science, University of Marburg , Marburg, Germany
                [3 ] Computer, Electrical and Mathematical Sciences and Engineering Division, Computational Bioscience Research Center, King Abdullah University of Science and Technology , Thuwal, Saudi Arabia
                [4 ] Department of Botany and Molecular Evolution, Senckenberg Research Institute and Natural History Museum Frankfurt , Frankfurt am Main, Germany
                [5 ] Department of Physical Geography, Goethe University , Frankfurt am Main, Germany
                [6 ] Palmengarten der Stadt Frankfurt am Main , Frankfurt am Main, Germany
                Article
                10.1080/23818107.2018.1446357
                ccb7bfea-686c-4c13-a084-176b979aa6ac
                © 2018

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