Semantic-Driven Selection of Printer Color Rendering Intents

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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 ...

IntroductionThe current assessment and standardization of microsurgical skills are subjective, posing challenges in reliable skill evaluation. We aim to address these limitations by developing a quantitative and objective framework for accurately assessing ...

Modern power distribution systems are experiencing a large-scale integration of Converter-Interfaced Distributed Energy Resources (CIDERs). Their presence complicates the analysis and mitigation of harmonics, whose creation and propagation may be amplified ...

In the context of automatic visual inspection of infrastructures by drones, Deep Learning (DL) models are used to automatically process images for fault diagnostics. While explainable Artificial Intelligence (AI) algorithms can provide explanations to asse ...

Cross-resolution face recognition has become a challenging problem for modern deep face recognition systems. It aims at matching a low-resolution probe image with high-resolution gallery images registered in a database. Existing methods mainly leverage pri ...

This paper introduces TACOSS a text-image alignment approach that allows explainable land cover semantic segmentation by directly integrating semantic concepts encoded from texts. TACOSS combines convolutional neural networks for visual feature extraction ...

Simplicial Kuramoto models have emerged as a diverse and intriguing class of models describing oscillators on simplices rather than nodes. In this paper, we present a unified framework to describe different variants of these models, categorized into three ...

Car sharing systems (CSSs) are one of the environmentally beneficial solutions in urban transportation. However, the operators still struggle to make these systems profitable. One of the main contributors in operational cost is rebalancing operations. Ther ...

We present 3DHumanGAN, a 3D-aware generative adversarial network that synthesizes photo-like images of fullbody humans with consistent appearances under different view-angles and body-poses. To tackle the representational and computational challenges in sy ...

The construction industry is jeopardizing Earth’s environment; current mitigation efforts do not suffice; and additional sustainable strategies are required. One such strategy consists of reusing structural building elements over multiple service lives. In ...

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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 ...

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In this thesis, we reveal that supervised learning and inverse problems share similar mathematical foundations. Consequently, we are able to present a unified variational view of these tasks that we formulate as optimization problems posed over infinite-di ...

During the design phase of an aircraft manufacturing system, different industrial scenarios need to be evaluated according to key performance indicators to achieve the optimal system performance. It is a highly complex process involving multidisciplinary s ...

Wikipedia is the largest source of free encyclopedic knowledge and one of the most visited sites on the Web. To increase reader understanding of the article, Wikipedia editors add images within the text of the article's body. However, despite their widespr ...

Detecting people from 2D images and analyzing their motion in 3D have been long standing computer vision problems central to numerous applications such as autonomous driving and athletic training. Recently, with the availability of large amounts of trainin ...

We propose a new modelling approach for daily activity scheduling which integrates the different daily scheduling choice dimensions (activity participation, location, schedule, duration and transportation mode) into a single optimisation problem. The funda ...

State-of-the-art 2D image compression schemes rely on the power of convolutional neural networks (CNNs). Although CNNs offer promising perspectives for 2D image compression, extending such models to omnidirectional images is not straightforward. First, omn ...

Semantic-Driven Selection of Printer Color Rendering Intents

Chattez avec Graph Search

Aggregating Spatial and Photometric Context for Photometric Stereo

An objective skill assessment framework for microsurgical anastomosis based on ALI scores

Unified Harmonic Power-Flow and Stability Analysis of Power Grids with Converter-Interfaced Distributed Energy Resources

Exploiting Explanations to Detect Misclassifications of Deep Learning Models in Power Grid Visual Inspection

Cross-resolution Face Recognition via Identity-Preserving Network and Knowledge Distillation

Text as a Richer Source of Supervision in Semantic Segmentation Tasks

A unified framework for Simplicial Kuramoto models

A general framework to evaluate different rebalancing operations strategies in one-way car sharing systems

3DHumanGAN: 3D-Aware Human Image Generation with 3D Pose Mapping

Reuse of Load-Bearing Timber Elements - Case Study of a Lookout Tower in Lausanne, Switzerland

DIABLO: A Benchmark Suite for Blockchains

Attribute Prediction as Multiple Instance Learning

reference data for "A Critical Comparison of Annual Glare Simulation Methods"

Dynamic Probabilistic Pruning: A General Framework for Hardware-Constrained Pruning at Different Granularities

Optimization Over Banach Spaces: A Unified View on Supervised Learning and Inverse Problems

A semantic-driven tradespace framework to accelerate aircraft manufacturing system design

A large scale study of reader interactions with images on Wikipedia

Encoder-Decoder Models for Human Segmentation and Motion Analysis

Capturing trade-offs between daily scheduling choices

OSLO: On-the-Sphere Learning for Omnidirectional Images and Its Application to 360-Degree Image Compression