3,750 research outputs found
Recovering Dense Tissue Multispectral Signal from in vivo RGB Images
Hyperspectral/multispectral imaging (HSI/MSI) contains rich information
clinical applications, such as 1) narrow band imaging for vascular
visualisation; 2) oxygen saturation for intraoperative perfusion monitoring and
clinical decision making [1]; 3) tissue classification and identification of
pathology [2]. The current systems which provide pixel-level HSI/MSI signal can
be generally divided into two types: spatial scanning and spectral scanning.
However, the trade-off between spatial/spectral resolution, the acquisition
time, and the hardware complexity hampers implementation in real-world
applications, especially intra-operatively. Acquiring high resolution images in
real-time is important for HSI/MSI in intra-operative imaging, to alleviate the
side effect caused by breathing, heartbeat, and other sources of motion.
Therefore, we developed an algorithm to recover a pixel-level MSI stack using
only the captured snapshot RGB images from a normal camera. We refer to this
technique as "super-spectral-resolution". The proposed method enables recovery
of pixel-level-dense MSI signals with 24 spectral bands at ~11 frames per
second (FPS) on a GPU. Multispectral data captured from porcine bowel and
sheep/rabbit uteri in vivo has been used for training, and the algorithm has
been validated using unseen in vivo animal experiments.Comment: accepted by Hamlyn Symposium 201
Minimally-invasive surgical application of multispectral and polarization resolved imaging
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Quantifying Uncertainty
Many of us interact with automated agents every day (e.g., Microsoft\u27s Cortana, Apple’s Siri, Amazon’s Alexa, etc.), and decision-makers at all levels of organizations utilize automated systems that are designed to enable better, faster, and more effective decisions. Understanding the conditions under which humans trust and rely upon automated agents recommendations is important, as trust is one of the mechanisms that allows for humans to interact effectively with a variety of teammates. Reliance and trust in automated systems is changing the way we process information, make decisions, and perform tasks. We conducted an experiment to determine the conditions and personality characteristics that affect human-machine interactions. Our analysis focused on the use of an automated decision aid in conditions of uncertainty. We also looked to see how perceptions of an automated decision aid’s ability related to human trust. Last, we explored how extraversion, a broad factor that encompasses the tendency to be energetic, affiliative, and dominant, related to perceptions of trust in the automated agent. We observed that in conditions of uncertainty, human decision outcomes moved in accordance with the recommendation of the agent. In addition, we found a correlation between perceptions of ability and user trust in the automated agent
Illumination uniformity in endoscopic imaging
Standardised endoscopic digital images were taken and analysed using an image analysis software (National Instruments Vision Assistant version 7.1.1). The luminance plane was extracted and the pixel intensity distribution was determined along a horizontal line at the position of highest average intensity (centroid). The data was exported to MS Excel and the pixel intensity (y-axis) was plotted against pixel position (x-axis). A trendline using a 2nd order polynomial curve was fitted to each data set. The resultant equation for each curve was compared with equations obtained from other images taken under various illumination conditions and settings
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