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Found 6 papers, 0 papers with code
Improving Deep Learning Predictions with Simulated Images, and Vice Versa
In this paper, we systematically vary amounts of real and synthetic images used for training in both maize and canola to better understand situations where synthetic images generated from L-systems can help prediction on real images.
A Novel Technique Combining Image Processing, Plant Development Properties, and the Hungarian Algorithm, to Improve Leaf Detection in Maize
This research aims to identify the position (and number) of leaves from a temporal sequence of high-quality indoor images consisting of multiple views, focussing in particular of images of maize.
DNA Methylation Data to Predict Suicidal and Non-Suicidal Deaths: A Machine Learning Approach
This research constitutes a baseline study for classifying suicidal and non-suicidal deaths from DNA methylation data.
Stochastic L-system Inference from Multiple String Sequence Inputs
Lindenmayer systems (L-systems) are a grammar system that consist of string rewriting rules.
Techniques for Inferring Context-Free Lindenmayer Systems With Genetic Algorithm
The inductive inference problem attempts to infer an L-system from such a sequence of strings generated by an unknown system; this can be thought of as an intermediate step to inferring from a sequence of images.
New Techniques for Inferring L-Systems Using Genetic Algorithm
Indeed, while existing approaches are limited to L-systems with a total sum of 20 combined symbols in the productions, PMIT can infer almost all L-systems tested where the total sum is 140 symbols.
