32 citations · 83 across the 6 of their papers we have counts for
6 papers
Plant Species Recognition with Optimized 3D Polynomial Neural Networks and Variably Overlapping Time-Coherent Sliding Window
Habib Ben Abdallah, Christopher J. Henry, Sheela Ramanna
Recently, the EAGL-I system was developed to rapidly create massive labeled datasets of plants intended to be commonly used by farmers and researchers to create AI-driven solutions…
Presenting an extensive lab- and field-image dataset of crops and weeds for computer vision tasks in agriculture
Michael A. Beck, Chen-Yi Liu, Christopher P. Bidinosti +3
We present two large datasets of labelled plant-images that are suited towards the training of machine learning and computer vision models. The first dataset encompasses as the day…
Polynomial degree reduction in the -norm on a symmetric interval for the canonical basis
Habib Ben Abdallah, Christopher J. Henry, Sheela Ramanna
In this paper, we develop a direct formula for determining the coefficients in the canonical basis of the best polynomial of degree that approximates a polynomial of degree $N>…
Fully Automated 2D and 3D Convolutional Neural Networks Pipeline for Video Segmentation and Myocardial Infarction Detection in Echocardiography
Oumaima Hamila, Sheela Ramanna, Christopher J. Henry +4
Cardiac imaging known as echocardiography is a non-invasive tool utilized to produce data including images and videos, which cardiologists use to diagnose cardiac abnormalities in…
1-Dimensional polynomial neural networks for audio signal related problems
Habib Ben Abdallah, Christopher J. Henry, Sheela Ramanna
In addition to being extremely non-linear, modern problems require millions if not billions of parameters to solve or at least to get a good approximation of the solution, and neur…
An embedded system for the automated generation of labeled plant images to enable machine learning applications in agriculture
Michael A. Beck, Chen-Yi Liu, Christopher P. Bidinosti +3
A lack of sufficient training data, both in terms of variety and quantity, is often the bottleneck in the development of machine learning (ML) applications in any domain. For agric…