Model-based machine learning methods incorporate domain knowledge from the physical forward model of an inverse problem to reduce the need for training data. In this research, we show how this can be used to address challenging limitations such as occlusion.
Optimization of freeform spectacle lenses based on high-order aberrations
Spectacle lenses are an important application of freeform manufacturing, with complex designs such as progressive lenses requiring nontraditional and specialized surface shapes. Such lenses also pose special challenges for optical design, as the eye’s gaze constantly changes relative to the
Calculus for Deep Learning, with Vectors, Matrices, and a few Tuples
This note derives the gradients for training a deep neural network while trying to use only matrix algebra and vector calculus. It isn’t quite enough so we have to add a little more structure based on ‘tuples’, basically a list
A first-order optimization method for learning to reconstruct opacity in computational imaging
Unknown self-occlusion in a scene with opaque objects causes the multiview reconstruction problem to become ill-posed and nonlinear. In this report we describe a scalable nonlinear optimization method for simultaneously reconstructing the object and occlusion. The approach uses a simple
S+SSPR 2020
I will be at S+SSPR 2020 (Statistical, Structural or Syntactic Pattern Recognition) on January 21-22 to present my research on partitioning of partial correlation networks. Update 4/10/2021: Conference proceedings Dillon K. (2021) Efficient Partitioning of Partial Correlation Networks. In:
Focus optimization in a Computational Confocal Microscope
In this report we consider the numerical optimization of performance for a computational extension of a confocal microscope. Using a system where the pinhole detector is replaced with a detector array, we seek to exploit this additional information for each
BI2020
I will be at Brain Informatics 2020 on September 19, 2020 presenting “The Resolution Matrix for Visualizing Functional Network Connectivity” (see preprint below). For pre-recorded video of presentation, click image:
Robust neural network for wavefront reconstruction using Zernike coefficients
Accurately measuring optical aberrations is an important process for several eyecare tasks. Managing the individual variations found in human eyes plays a large role in properly defining these aberrations. A common method to measure optical aberrations uses sensors to locally
The Resolution Matrix for Visualizing Functional Network Connectivity
The resolution matrix is a mathematical tool for analyzing inverse problems such as computational imaging systems. When treating network connectivity estimation as an inverse problem, the resolution matrix describes the degree to which network nodes and edges can be resolved.
Resolution-based spectral clustering for brain parcellation using functional MRI
Brain parcellation is important for exploiting neuroimaging data. Variability in physiology between individuals has led to the need for data-driven approaches to parcellation, with recent research focusing on simultaneously estimating and partitioning the network structure of the brain. We view