Papers by L. Andrea Dunbar
Intelligent multispectral vision system for contactless water quality monitoring for wastewater
AI and Optical Data Sciences IV
Optical photonic crystal condensing device
A photonic crystal structure for condensing electromagnetic radiation comprising a modulated squa... more A photonic crystal structure for condensing electromagnetic radiation comprising a modulated square-shaped lattice and a corresponding condensing device are disclosed
Privacy-Preserving Image Acquisition for Neural Vision Systems
IEEE Transactions on Multimedia
GOLD MEMBRANES WITH LARGE ARRAYS OF SUB-µm HOLES FABRICATED BY WAFER-SCALE NANOSPHERE LITHOGRAPHY
Gold membranes with large arrays of sub-µm holes were fabricated and optically characterized. The... more Gold membranes with large arrays of sub-µm holes were fabricated and optically characterized. The fabrication is a combination of a bottom-up, self-assembly based patterning technique, Nanosphere Lithography (NSL), and standard microfabrication. This was achieved by 1) up-scaling of the deposition of close-packed bead monolayers to 4’’ wafer substrates, 2) controlled bead size reduction, 3) etching of high aspect-ratio Si pillar arrays, 4) using the pillar arrays as a lift-off template, and 5) releasing the membranes by dry-etching. In this way, millimeter-size, 200 nm thick gold membranes with dense, short-range ordered hole arrays were fabricated. The array periodicity was either 428 nm or 535 nm, depending on the initial bead size. The hole diameter was tuned in the range of 150 nm to 250 nm. Optical transmission spectroscopy showed surface plasmon mediated extraordinary optical transmission (EOT) with an enhancement factor greater than two
Real time eye gaze tracking for human machine interaction in the cockpit
AI and Optical Data Sciences III, 2022
The Aeronautics industry has pioneered safety from digital checklists to moving maps that improve... more The Aeronautics industry has pioneered safety from digital checklists to moving maps that improve pilot situational awareness and support safe ground movements. Today, pilots deal with increasingly complex cockpit environments and air traffic densification. Here we present an intelligent vision system, which allows real-time human-machine interaction in the cockpits to reduce pilot’s workload. The challenges for such a vision system include extreme change in background light intensity, large field-of-view and variable working distances. Adapted hardware, use of state-of-the-art computer vision techniques and machine learning algorithms in eye gaze detection allow a smooth, and accurate real-time feedback system. The current system has been over-specified to explore the optimized solutions for different use-cases. The algorithmic pipeline for eye gaze tracking was developed and iteratively optimized to obtain the speed and accuracy required for the aviation use cases. The pipeline, which is a combination of data-driven and analytics approaches, runs in real time at 60 fps with a latency of about 32ms. The eye gaze estimation error was evaluated in terms of the point of regard distance error with respect to the 3D point location. An average error of less than 1.1cm was achieved over 28 gaze points representing the cockpit instruments placed at about 80-110cm from the participants’ eyes. The angular gaze deviation goes down to less than 1° for the panels towards which an accurate eye gaze was required according to the use cases.
A fast simple-to-use and inexpensive multispectral camera to detect skin conditions (Conference Presentation)
Photonic Instrumentation Engineering VII, 2020
Versatile, intelligent multispectral imaging camera made with off-the-shelf components
Photonic Instrumentation Engineering V, 2018
Multispectral imagers collect a hypercube of data, where the spatial image is along two-dimension... more Multispectral imagers collect a hypercube of data, where the spatial image is along two-dimensions and the spectral information is in the third. Two main technologies are used for multispectral imaging: sweeping, where the hypercube is built by scanning through different wavelengths or spatial positions and snapshot multispectral spectral imaging, where the 3D cube of images is taken in one shot. Sweeping imaging systems tend to have more lines and better spectral resolutions whilst snapshot cameras are often used for dynamic analysis of scenes. A common method to obtain the hypercube in snapshot imagers is by pixel level filtering on the sensor chip. Pixel level filtering, where the filter is placed directly on the pixels are intergrated into the wafer-level making processing making them difficult to customize. Therefore, these sensors tend to aim for equally spaced spectral lines in order to cover many applications. This results in an often in an unnecessarily large data cube when...
A Bloch Wave Model that Describes the Dispersive Effects in Photonic Crystals
Tuneable planar photonic crystal devices
IEEE Transactions on Neural Networks and Learning Systems
Learning generative probabilistic models that can estimate the continuous density given a set of ... more Learning generative probabilistic models that can estimate the continuous density given a set of samples, and that can sample from that density, is one of the fundamental challenges in unsupervised machine learning. In this paper we introduce a new approach to obtain such models based on what we call denoising density estimators (DDEs). A DDE is a scalar function, parameterized by a neural network, that is efficiently trained to represent a kernel density estimator of the data. Leveraging DDEs, our main contribution is to develop a novel approach to obtain generative models that sample from given densities. We prove that our algorithms to obtain both DDEs and generative models are guaranteed to converge to the correct solutions. Advantages of our approach include that we do not require specific network architectures like in normalizing flows, ordinary differential equation solvers as in continuous normalizing flows, nor do we require adversarial training as in generative adversarial networks (GANs). Finally, we provide experimental results that demonstrate practical applications of our technique.
2022 9th Swiss Conference on Data Science (SDS)
Designing Deep Neural Networks (DNNs) running on edge hardware remains a challenge. Standard desi... more Designing Deep Neural Networks (DNNs) running on edge hardware remains a challenge. Standard designs have been adopted by the community to facilitate the deployment of Neural Network models. However, not much emphasis is put on adapting the network topology to fit hardware constraints. In this paper, we adapt one of the most widely used architectures for mobile hardware platforms, MobileNetV2, and study the impact of changing its topology and applying post-training quantization. We discuss the impact of the adaptations and the deployment of the model on an embedded hardware platform for face detection.
2021 International Conference on 3D Vision (3DV), 2021
The problem of estimating a surface shape from its observed reflectance properties still remains ... more The problem of estimating a surface shape from its observed reflectance properties still remains a challenging task in computer vision. The presence of global illumination effects such as inter-reflections or cast shadows makes the task particularly difficult for non-convex real-world surfaces. State-of-the-art methods for calibrated photometric stereo address these issues using convolutional neural networks (CNNs) that primarily aim to capture either the spatial context among adjacent pixels or the photometric one formed by illuminating a sample from adjacent directions. In this paper, we bridge these two objectives and introduce an efficient fully-convolutional architecture that can leverage both spatial and photometric context simultaneously. In contrast to existing approaches that rely on standard 2D CNNs and regress directly to surface normals, we argue that using separable 4D convolutions and regressing to 2D Gaussian heat-maps severely reduces the size of the network and makes inference more efficient. Our experimental results on a real-world photometric stereo benchmark show that the proposed approach outperforms the existing methods both in efficiency and accuracy.
Remote photoplethysmography combining color channels with SNR maximization for respiratory rate assessment
2020 14th International Symposium on Medical Information Communication Technology (ISMICT), 2020
Monitoring of breathing rate considerably improves healthcare quality by providing information ab... more Monitoring of breathing rate considerably improves healthcare quality by providing information about the patient’s state. Yet, the current methods used to assess the respiratory rate have some drawbacks that could be improved. Recent developments of the non-contact based measurement of the respiratory rate brought the remote Photoplethysmography (rPPG) technique. This technique allows physiological parameters monitoring with a camera and ambient light. It measures the light absorption variation by the blood to extract several physiological parameters. The main issue with the rPPG method is that its signal quality is lower compared with the contact-based methods. Several ways to improve this quality may be explored to obtain a robust rPPG pipeline. One of these is the combination of the multidimensional temporal traces generated by a camera into a single temporal trace. The literature methods aimed to extract a signal with the maximal information using smart estimation of the combination coefficients. These coefficients are computed using statistical or physiological/optical properties of the traces. In this paper, we developed an algorithm that estimates a better respiratory signal than with regular methods. The algorithm named Energy Variance Maximization (EVM) estimates the linear combination that maximizes the Signal Noise Ratio (SNR) of the output trace. We compare our contribution with three state-of-the-art method: CHROM, PBV and PVM. The results obtained with this method are better than the state of the art methods, with the Mean Absolute Error (MAE) being 0.87 rpm better than CHROM, the best of compared literature methods.
Novel micro-fabricated Fabry-Perot filters in infrared
We have designed, fabricated and tested narrow-band Fabry-Perot filters in the infrared using gol... more We have designed, fabricated and tested narrow-band Fabry-Perot filters in the infrared using gold porous mirrors and a silicon spacer layer. The filter peaks at 10 μm and 15 μm have approximately 10% transmission and a 1.5% linewidth. A Fabry-Perot structure with plane metal layers having a similar linewidth would have a transmission of only 0.2%. Thus, for the same linewidth we have improved the transmission by a factor of 50. Apart from the optical enhancements, these filters also have the advantage that they can be made inexpensively in a standard silicon MEMS technology and that their resonances can be finely tuned through post processing.
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2020
The respiratory rate is important information in the healthcare environment. Consequently, resear... more The respiratory rate is important information in the healthcare environment. Consequently, research is done to develop a device that could measure the respiratory rate continuously with non-contact devices. Various methods were tried, such as radio-based, thermal imaging or remote photoplethysmography (rPPG). The rPPG method uses a video recording of the skin in ambient light conditions. It measures the small variations of light reflection induced by the amount of blood in vessels. This method allows the extraction of physiological parameters such as the heart rate or respiratory rate without any contact with the skin. The main issue with the rPPG technique is the lower signal quality compared with contact-based methods. In this paper, we assess the performance of the respiratory rate estimation algorithms with rPPG signals. The tested algorithms were designed for contact-PPG signals input. The use of the algorithms designed for contact PPG on remote PPG signals can lead to respiratory rate estimations with a mean absolute error below 3 breaths-per-minute. We benchmark our results using this standard and some other metrics to interpret the quality of the assessment. Another non-contact method, called remote photoplethysmography (rPPG) uses a camera and the light reflection from the skin to extract blood volume pulse signals. This technique can be employed with a low-cost webcam and ambient light for the heart rate estimation [17, 18].
Machine Vision and Applications, 2021
Thanks to recent advancements in image processing and deep learning techniques, visual surface in... more Thanks to recent advancements in image processing and deep learning techniques, visual surface inspection in production lines has become an automated process as long as all the defects are visible in a single or a few images. However, it is often necessary to inspect parts under many different illumination conditions to capture all the defects. Training deep networks to perform this task requires large quantities of annotated data, which are rarely available and cumbersome to obtain. To alleviate this problem, we devised an original augmentation approach that, given a small image collection, generates rotated versions of the images while preserving illumination effects, something that random rotations cannot do. We introduce three real multi-illumination datasets, on which we demonstrate the effectiveness of our illumination preserving rotation approach. Training deep neural architectures with our approach delivers a performance increase of up to 51% in terms of AuPRC score over usi...
Biomedical Signal Processing and Control, 2021
Respiration is an important parameter in critical and pediatric care since its monitoring allows ... more Respiration is an important parameter in critical and pediatric care since its monitoring allows medical staff to detect many life-threatening diseases. One of the existing monitoring methods is based on remote photoplethysmography (rPPG). This technique consists of extracting a signal related to blood volume variations using a camera. This signal carries useful physiological information such as cardiac and respiratory rates. However, the quality of the signal is lower than regular contact-based methods and represents a major weakness of the rPPG method. In this paper, we propose an algorithm to explicitly maximize the respiratory signal quality by maximizing the Signal-to-Noise Ratio (SNR). Instead of using the regular Fast-Fourier-based energy ratio for the signal-to-noise-ratio estimation, we propose to use the continuous wavelet transform to deal with nonstationarities of the respiratory signal. The method, named Wavelet Variance Maximization (WVM), is based on the Generalized Eigen Value Decomposition (GEVD) algorithm and estimates the optimal combination of the temporal color traces to obtain a high-quality rPPG signal. Our method was tested on 12 healthy adult volunteers and the results confirm that the estimated signal has better quality than existing methods, with approximately 20% reduction of error compared to the best tested state-of-the-art method.
Nanostructured enhanced chemical sensing surfaces for mid-IR molecular absorption
Quantum Sensing and Nanophotonic Devices X, 2013
ABSTRACT
Photonic Instrumentation Engineering IV, 2017
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Papers by L. Andrea Dunbar