90 citations · 135 across the 24 of their papers we have counts for
26 papers
Parameter-free Maximum Likelihood Localization of a Network of Moving Agents from Ranges, Bearings and Velocity measurements
Filipa Valdeira, Cláudia Soares, João Gomes
Localization is a fundamental enabler technology for many applications, like vehicular networks, IoT, and even medicine. While Global Navigation Satellite Systems solutions offer g…
Accurate and Interpretable Solution of the Inverse Rig for Realistic Blendshape Models with Quadratic Corrective Terms
Stevo Racković, Cláudia Soares, Dušan Jakovetić +1
We propose a new model-based algorithm solving the inverse rig problem in facial animation retargeting, exhibiting higher accuracy of the fit and sparser, more interpretable weight…
High-fidelity Interpretable Inverse Rig: An Accurate and Sparse Solution Optimizing the Quartic Blendshape Model
Stevo Racković, Cláudia Soares, Dušan Jakovetić +1
We propose a method to fit arbitrarily accurate blendshape rig models by solving the inverse rig problem in realistic human face animation. The method considers blendshape models w…
Distributed Solution of the Inverse Rig Problem in Blendshape Facial Animation
Stevo Racković, Cláudia Soares, Dušan Jakovetić
The problem of rig inversion is central in facial animation as it allows for a realistic and appealing performance of avatars. With the increasing complexity of modern blendshape m…
ISEE.U: Distributed online active target localization with unpredictable targets
Miguel Vasques, Claudia Soares, João Gomes
This paper addresses target localization with an online active learning algorithm defined by distributed, simple and fast computations at each node, with no parameters to tune and…
A Majorization-Minimization Based Method for Nonconvex Inverse Rig Problems in Facial Animation: Algorithm Derivation
Stevo Racković, Cláudia Soares, Dušan Jakovetić +1
Automated methods for facial animation are a necessary tool in the modern industry since the standard blendshape head models consist of hundreds of controllers and a manual approac…
Probabilistic Registration for Gaussian Process 3D shape modelling in the presence of extensive missing data
Filipa Valdeira, Ricardo Ferreira, Alessandra Micheletti +1
We propose a shape fitting/registration method based on a Gaussian Processes formulation, suitable for shapes with extensive regions of missing data. Gaussian Processes are a prove…
Ranking with Confidence for Large Scale Comparison Data
Filipa Valdeira, Cláudia Soares
In this work, we leverage a generative data model considering comparison noise to develop a fast, precise, and informative ranking algorithm from pairwise comparisons that produces…
Decentralized EM to Learn Gaussian Mixtures from Datasets Distributed by Features
Pedro Valdeira, Cláudia Soares, João Xavier
Expectation Maximization (EM) is the standard method to learn Gaussian mixtures. Yet its classic, centralized form is often infeasible, due to privacy concerns and computational an…
COSMIC: fast closed-form identification from large-scale data for LTV systems
Maria Carvalho, Claudia Soares, Pedro Lourenço +1
We introduce a closed-form method for identification of discrete-time linear time-variant systems from data, formulating the learning problem as a regularized least squares problem…
Decision Support Models for Predicting and Explaining Airport Passenger Connectivity from Data
Marta Guimaraes, Claudia Soares, Rodrigo Ventura
Predicting if passengers in a connecting flight will lose their connection is paramount for airline profitability. We present novel machine learning-based decision support models f…
Clustering of the Blendshape Facial Model
Stevo Racković, Cláudia Soares, Dušan Jakovetić +2
Digital human animation relies on high-quality 3D models of the human face -- rigs. A face rig must be accurate and, at the same time, fast to compute. One of the most common riggi…
Range and Bearing Data Fusion for Precise Convex Network Localization
Claudia Soares, Filipa Valdeira, Joao Gomes
Hybrid localization in GNSS-challenged environments using measured ranges and angles is becoming increasingly popular, in particular with the advent of multimodal communication sys…
Robust Localization with Bounded Noise: Creating a Superset of the Possible Target Positions via Linear-Fractional Representations
João Domingos, Cláudia Soares, João Xavier
Locating a target is key in many applications, namely in high-stakes real-world scenarios, like detecting humans or obstacles in vehicular networks. In scenarios where precise stat…
STRONG: Synchronous and asynchronous RObust Network localization, under Non-Gaussian noise
Claudia Soares, João Gomes
Real-world network applications must cope with failing nodes, malicious attacks, or nodes facing corrupted data - data classified as outliers. Our work addresses these concerns in…
Accurate, Interpretable, and Fast Animation: An Iterative, Sparse, and Nonconvex Approach
Stevo Rackovic, Claudia Soares, Dusan Jakovetic +1
Digital human animation relies on high-quality 3D models of the human face: rigs. A face rig must be accurate and, at the same time, fast to compute. One of the most common rigging…
Conjunction Data Messages behave as a Poisson Process
Francisco Caldas, Claudia Soares, Cláudia Nunes +3
Space debris is a major problem in space exploration. International bodies continuously monitor a large database of orbiting objects and emit warnings in the form of conjunction da…
From noisy point clouds to complete ear shapes: unsupervised pipeline
Filipa Valdeira, Ricardo Ferreira, Alessandra Micheletti +1
Ears are a particularly difficult region of the human face to model, not only due to the non-rigid deformations existing between shapes but also to the challenges in processing the…
FLORIS and CLORIS: Hybrid Source and Network Localization Based on Ranges and Video
Beatriz Quintino Ferreira, João Gomes, Cláudia Soares +1
We propose hybrid methods for localization in wireless sensor networks fusing noisy range measurements with angular information (extracted from video). Compared with conventional m…
DIeSEL: DIstributed SElf-Localization of a network of underwater vehicles
Cláudia Soares, Pusheng Ji, João Gomes +1
How can teams of artificial agents localize and position themselves in GPS-denied environments? How can each agent determine its position from pairwise ranges, own velocity, and li…
LocDyn: Robust Distributed Localization for Mobile Underwater Networks
Cláudia Soares, João Gomes, Beatriz Ferreira +1
How to self-localize large teams of underwater nodes using only noisy range measurements? How to do it in a distributed way, and incorporating dynamics into the problem? How to rej…
Dealing with bad apples: Robust range-based network localization via distributed relaxation methods
Cláudia Soares, João Gomes
Real-world network applications must cope with failing nodes, malicious attacks, or, somehow, nodes facing corrupted data --- classified as outliers. One enabling application is th…
Robust dissimilarity measure for Network Localization
Cláudia Soares, João Gomes
In practice, network applications have to deal with failing nodes, malicious attacks, or, somehow, nodes facing highly corrupted data --- generally classified as outliers. This cal…
Simple and fast convex relaxation method for cooperative localization in sensor networks using range measurements
Cláudia Soares, João Xavier, João Gomes
We address the sensor network localization problem given noisy range measurements between pairs of nodes. We approach the non-convex maximum-likelihood formulation via a known simp…