Mathematical Surface Matching of Maps of the Human Torso

This report concerns with the collection and processing of data acquired from three-dimensional (3D) surface scans of scoliosis patients' backs. Two main issues were addressed: the reproducibility of the results, and stringent time constraints. In particular, user influence should be removed from each step of the data processing, and results should be obtained within three minutes of acquiring the scan. The report begins with a description of the data collection, followed by a description of the data processing required to align two back surfaces. A section is devoted to calculating the cosmetic score, a measure of deformity of the back. The paper concludes with a few suggestions for improvements on data collection and use

Style Memory: Making a Classifier Network Generative

Deep networks have shown great performance in classification tasks. However, the parameters learned by the classifier networks usually discard stylistic information of the input, in favour of information strictly relevant to classification. We introduce a network that has the capacity to do both classification and reconstruction by adding a "style memory" to the output layer of the network. We also show how to train such a neural network as a deep multi-layer autoencoder, jointly minimizing both classification and reconstruction losses. The generative capacity of our network demonstrates that the combination of style-memory neurons with the classifier neurons yield good reconstructions of the inputs when the classification is correct. We further investigate the nature of the style memory, and how it relates to composing digits and letters. Finally, we propose that this architecture enables the bidirectional flow of information used in predictive coding, and that such bidirectional networks can help mitigate against being fooled by ambiguous or adversarial input.Comment: 6 pages, 11 figure

Population coding in sparsely connected networks of noisy neurons

This study examines the relationship between population coding and spatial connection statistics in networks of noisy neurons. Encoding of sensory information in the neocortex is thought to require coordinated neural populations, because individual cortical neurons respond to a wide range of stimuli, and exhibit highly variable spiking in response to repeated stimuli. Population coding is rooted in network structure, because cortical neurons receive information only from other neurons, and because the information they encode must be decoded by other neurons, if it is to affect behavior. However, population coding theory has often ignored network structure, or assumed discrete, fully connected populations (in contrast with the sparsely connected, continuous sheet of the cortex). In this study, we modeled a sheet of cortical neurons with sparse, primarily local connections, and found that a network with this structure could encode multiple internal state variables with high signal-to-noise ratio. However, we were unable to create high-fidelity networks by instantiating connections at random according to spatial connection probabilities. In our models, high-fidelity networks required additional structure, with higher cluster factors and correlations between the inputs to nearby neurons

Efficient nonlocal-means denoising using the SVD

Nonlocal-means (NL-means) is an image denoising method that replaces each pixel by a weighted average of all the pixels in the image. Unfortunately, the method requires the computation of the weighting terms for all possible pairs of pixels, making it computationally expensive. Some short-cuts assign a weight of zero to any pixel pairs whose neighbourhood averages are too dissimliar. In this paper, we propose an alternative strategy that uses the SVD to more efficiently eliminate pixel pairs that are dissimilar. Experiments comparing this method against other NL-means speed-up strategies show that its refined discrimination between similar and dissimilar pixel neighbourhoods significantly improves the denoising effect. Index Terms — nonlocal-means, denoising, SVD 1

Plausibility of Image Reconstruction Using a Proposed Flexible and Portable CT Scanner

The very hot and power-hungry x-ray filaments in today's computed tomography (CT) scanners constrain their design to be big and stationary. What if we built a CT scanner that could be deployed at the scene of a car accident to acquire tomographic images before moving the victim? Recent developments in nanotechnology have shown that carbon nanotubes can produce x-rays at room temperature, and with relatively low power needs. We propose a design for a portable and flexible CT scanner made up of an addressable array of tiny x-ray emitters and detectors. In this paper, we outline a basic design, propose a strategy for reconstruction, and demonstrate the feasibility of reconstruction using experiments on a software simulation of the flexible scanner. These simulations show that reconstruction quality is stable over a wide range of scanner geometries, while progressively larger errors in the scanner geometry induce progressively larger errors. We also raise a number of issues that still need to be overcome to build such a scanner.This work was supported by funding from the Natural Sciences and Engineering Research Council of Canada (NSERC), the Canada Foundation for Innovation, and the Ontario Innovation Trust

Comparison of Foveated Downsampling Techniques in Image Recognition

Foveation is an important part of human vision, and a number of deep networks have also used foveation. However, there have been few systematic comparisons between foveating and non-foveating deep networks, and between different variable-resolution downsampling methods. Here we define several such methods, and compare their performance on ImageNet recognition with a custom Densenet network. The best variable-resolution method slightly outperforms uniform downsampling. Thus in our experiments, foveation does not substantially help or hinder object recognition in deep networks