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Multi-modality

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    Multi-Modality Imagery Database for Plant Phenotyping

    Jeffrey A. Cruz, Xi Yin, Xiaoming Liu, Saif M. Imran, Daniel D. Morris, David M. Kramer, Jin Chen

    We have collected a multi-modality plant imagery database named “MSU-PID” including two types of plants: Arabidopsis and bean. It is captured using four types of imaging sensors:fluorescence, infrared(IR), RGB color, and depth. The imaging setup and the variety of manual labels allow MSU-PID to be used for a ...

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    Keywords: Plant Vision, Database, Application, Multi-Modality

2017

  • Missing Modalities Imputation via Cascaded Residual Autoencoder
    Luan Tran, Xiaoming Liu, Jiayu Zhou, Rong Jin
    In Proceeding of IEEE Computer Vision and Pattern Recognition (CVPR 2017), Honolulu, HI, Jul. 2017
    Bibtex | PDF | Project Webpage
  • @inproceedings{ missing-modalities-imputation-via-cascaded-residual-autoencoder,
      author = { Luan Tran and Xiaoming Liu and Jiayu Zhou and Rong Jin },
      title = { Missing Modalities Imputation via Cascaded Residual Autoencoder },
      booktitle = { In Proceeding of IEEE Computer Vision and Pattern Recognition },
      address = { Honolulu, HI },
      month = { July },
      year = { 2017 },
    }

2016

  • Multi-modality Imagery Database for Plant Phenotyping
    Jeffrey A. Cruz, Xi Yin, Xiaoming Liu, Saif M. Imran, Daniel D. Morris, David M. Kramer, Jin Chen
    Machine Vision and Applications, Vol. 7, pp.1-15, , Jul. 2016 (equal contribution by first two authors)
    Bibtex | PDF | Project Webpage
  • @article{ multi-modality-imagery-database-for-plant-phenotyping,
      author = { Jeffrey A. Cruz and Xi Yin and Xiaoming Liu and Saif M. Imran and Daniel D. Morris and David M. Kramer and Jin Chen },
      title = { Multi-modality Imagery Database for Plant Phenotyping },
      booktitle = { Machine Vision and Applications },
      volume = { 7 },
      month = { July },
      year = { 2016 },
      pages = { 1--15 },
    }