Browsing by Author "Cieslak, Mikolaj"

Now showing 1 - 3 of 3
  • Thumbnail Image
    ItemOpen Access
    RandQMC USER'S GUIDE
    (2002-12-18) Lemieux, Christiane; Cieslak, Mikolaj; Luttmer, Kristopher
    This package contains implementations for many quasi-Monte Carlo methods and their associated randomizations. It is designed so that the same program shell can be used for all methods, with only a different input file in each case. Some of the methods included in the package can deal with an infinite (random) dimension, and most of the others have a limit of 360 on the dimension. A program that correctly generates the input file is provided with the package, as well as some basic statistical tools.
  • Thumbnail Image
    ItemOpen Access
    Stochastic simulation of pattern formation: an application of l-systems
    (2006) Cieslak, Mikolaj; Prusinkiewicz, Przemyslaw; Lemieux, Christiane
  • Thumbnail Image
    ItemOpen Access
    The use of plant models in deep learning: an application to leaf counting in rosette plants
    (2018-01-18) Ubbens, Jordan; Cieslak, Mikolaj; Prusinkiewicz, Przemyslaw; Stavness, Ian
    Abstract Deep learning presents many opportunities for image-based plant phenotyping. Here we consider the capability of deep convolutional neural networks to perform the leaf counting task. Deep learning techniques typically require large and diverse datasets to learn generalizable models without providing a priori an engineered algorithm for performing the task. This requirement is challenging, however, for applications in the plant phenotyping field, where available datasets are often small and the costs associated with generating new data are high. In this work we propose a new method for augmenting plant phenotyping datasets using rendered images of synthetic plants. We demonstrate that the use of high-quality 3D synthetic plants to augment a dataset can improve performance on the leaf counting task. We also show that the ability of the model to generate an arbitrary distribution of phenotypes mitigates the problem of dataset shift when training and testing on different datasets. Finally, we show that real and synthetic plants are significantly interchangeable when training a neural network on the leaf counting task.