Image reconstruction method based on a trained non-linear mapping

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Photometric stereo, a computer vision technique for estimating the 3D shape of objects through images captured under varying illumination conditions, has been a topic of research for nearly four decades. In its general formulation, photometric stereo is an ...

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Attribute-based representations help machine learning models perform tasks based on human understandable concepts, allowing a closer human-machine collaboration. However, learning attributes that accurately reflect the content of an image is not always str ...

There are many approaches to weakly-supervised training of networks to segment 2D images. By contrast, existing approaches to segmenting volumetric images rely on full-supervision of a subset of 2D slices of the 3D volume. We propose an approach to volume ...

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Image analysis workflows for Histology increasingly require the correlation and combination of measurements across several whole slide images. Indeed, for multiplexing, as well as multimodal imaging, it is indispensable that the same sample is imaged multi ...

The purpose of this Thesis is to develop, test, and characterize different models attempting to tackle the problem of measurement of galaxy shapes applied in interferometric observations. Shape measurement is a tool for estimating the underlying shear due ...

Human-centered scene understanding is the process of perceiving and analysing a dynamic scene observed through a network of sensors with emphasis on human-related activities. It includes the visual perception of human-related activities from either single ...

The acquisition of high quality maps of gene expression in the rodent brain is of fundamental importance to the neuroscience community. The generation of such datasets relies on registering individual gene expression images to a reference volume, a task en ...

Meaningful energy analysis of industrial processes requires the installation of energy monitoring systems. However, a lack of systematic methods for identifying the required measurement points, joint to scarce information on the related benefits, results i ...

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Near infrared optical tomography (NIROT) is an emerging modality that enables imaging the oxygenation of tissue, which is a biomarker of tremendous clinical relevance. Measuring in reflectance is usually required when NIROT is applied in clinical scenarios ...

A measurement of the Xi cc++ candidates are reconstructed via the decay modes Xi cc++->?c+K-pi+pi+ and Xi cc++->Xi c+pi+. The result, 3621.55 +/- 0.23 (stat) +/- 0.30 (syst) MeV/c(2), is the most precise measurement of the Xi cc++ mass to date. ...

In Mott polarimeters, the measurement of the spin polarization of an electron beam along two perpendicular quantization axes is commonly performed by collecting scattered electrons with four independent detectors placed azimuthally at 90 degrees from each ...

We propose a deep neural network based image-to-image translation for domain adaptation, which aims at finding translations between image domains. Despite recent GAN based methods showing promising results in image-to-image translation, they are prone to f ...

In quantum mechanics, the Heisenberg uncertainty principle places a fundamental limit in the measurement precision for certain pairs of physical quantities, such as position and momentum, time and energy or amplitude and phase. Due to the Heisenberg uncert ...

Image reconstruction method based on a trained non-linear mapping

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Weakly Supervised Volumetric Image Segmentation with Deformed Templates

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Amplitude and phase reconstruction for Low-Energy Electron Holography of individual proteins

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Human-Centered Scene Understanding via Crowd Counting

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Precision measurement of the Xi(++)(cc) mass

Improvement of Mott spin polarimeters: An overlooked redundancy

ExprADA: Adversarial domain adaptation for facial expression analysis

Quantum Measurement of Mechanical Motion close to the Standard Quantum Limit