@conference {cde-Mas-Gimenez25b, title = {Preview of a multimodal data set for robust and safe autonomous driving under adverse weather conditions}, booktitle = {IMEKO PhotoMet - International Symposium on Modern Photonic Metrology}, year = {2025}, month = {09/2025}, publisher = {11 IMEKO PhotoMet}, organization = {11 IMEKO PhotoMet}, address = {Modena, Italy}, abstract = {

Autonomous driving has made remarkable strides in recent years, yet adverse weather conditions such as fog, heavy rain, snow, and low-light environments remain major obstacles for perception systems. These conditions reduce visibility and introduce complex artifacts like scattering, reflection, and sensor saturation, which degrade the performance of traditional camera-LiDAR-based perception algorithms. Current state-of-theart autonomous driving data sets avoid these edge cases. To address this, we introduce a new multimodal data set specifically curated to support research in robust perception under challenging environmental conditions. The data set was collected using a multimodal platform installed on a custom prototype which includes a diverse suite of sensors: visible RGB cameras, a Short-Wave Infra-Red (SWIR) camera, long-wave infrared thermal camera, and polarimetric camera. In addition to these optical sensors, the system incorporates a high-resolution solid-state LiDAR and two automotive-grade radars. These modalities were selected for their complementary responses to different environmental challenges. The prototype has a [60{\textordmasculine},40{\textordmasculine}] of overlapped FOV with all modalities ensuring enough redundancy to provide a good understanding of the vehicle{\textquoteright}s surroundings. Additionally, a GNSS/INS system is mounted to provide with localization and vehicle odometry. All sensors are temporally paired using a best effort synchronization algorithm. They are also spatially registered using multimodal calibration boards, enabling accurate data fusion even at long distances. The data set covers a wide range of real-world driving scenarios with urban, road and highway at high-velocities scenes across various lighting and weather conditions. From clear sky, to heavy rain, snow and even fog. Each frame includes 2D and 3D annotations for object detection tasks. The data set is structured as a relational database with an architecture similar to the reference data set, nuScenes. To ease visualization and navigation and through the data set, an extended python developer kit tool will be published. This work offers a preview of a multimodal data set which offers a rich and unique resource to the research community, enabling the development and evaluation of algorithms designed to operate reliably in degraded visibility. The data set is expected to be open-sourced later this year.

}, keywords = {Adverse weather conditions, autonomous driving, Data fusion, LiDAR, Multimodal imaging}, url = {https://photomet.org/abstracts}, author = {Gerard de-Mas-Gim{\'e}nez and Adri{\`a} Subirana and Eduardo Bernal P{\'e}rez and Jordi Riu and Josep R Casas and Pablo Garc{\'\i}a-G{\'o}mez and S. Royo} } @article {aMarcello24, title = {Performance of Individual Tree Segmentation Algorithms in Forest Ecosystems Using UAV LiDAR Data}, journal = {Drones}, volume = {8}, year = {2024}, month = {12/2024}, abstract = {

Forests are crucial for biodiversity, climate regulation, and hydrological cycles, requiring sustainable management due to threats like deforestation and climate change. Traditional forest monitoring methods are labor-intensive and limited, whereas UAV LiDAR offers detailed three-dimensional data on forest structure and extensive coverage. This study primarily assesses individual tree segmentation algorithms in two forest ecosystems with different levels of complexity using high-density LiDAR data captured by the Zenmuse L1 sensor on a DJI Matrice 300RTK platform. The processing methodology for LiDAR data includes preliminary preprocessing steps to create Digital Elevation Models, Digital Surface Models, and Canopy Height Models. A comprehensive evaluation of the most effective techniques for classifying ground points in the LiDAR point cloud and deriving accurate models was performed, concluding that the Triangular Irregular Network method is a suitable choice. Subsequently, the segmentation step is applied to enable the analysis of forests at the individual tree level. Segmentation is crucial for monitoring forest health, estimating biomass, and understanding species composition and diversity. However, the selection of the most appropriate segmentation technique remains a hot research topic with a lack of consensus on the optimal approach and metrics to be employed. Therefore, after the review of the state of the art, a comparative assessment of four common segmentation algorithms (Dalponte2016, Silva2016, Watershed, and Li2012) was conducted. Results demonstrated that the Li2012 algorithm, applied to the normalized 3D point cloud, achieved the best performance with an F1-score of 91\% and an IoU of 83\%.

}, keywords = {CHM, Dalponte, DEM, forest, Li, LiDAR, Silva, tree segmentation, ULS, watershed}, issn = {2504-446X}, doi = {10.3390/drones8120772}, url = {https://www.mdpi.com/2504-446X/8/12/772}, author = {Javier Marcello and Mar{\'\i}a Sp{\'\i}nola and Laia Albors and Ferran Marques and Dionisio Rodr{\'\i}guez-Esparrag{\'o}n and Francisco Eugenio} } @article {aPlasencia23, title = {A Preliminary Study of Deep Learning Sensor Fusion for Pedestrian Detection}, journal = {Sensors}, volume = {23}, year = {2023}, month = {04/2023}, chapter = {4167}, abstract = {

Most pedestrian detection methods focus on bounding boxes based on fusing RGB with lidar. These methods do not relate to how the human eye perceives objects in the real world. Furthermore, lidar and vision can have difficulty detecting pedestrians in scattered environments, and radar can be used to overcome this problem. Therefore, the motivation of this work is to explore, as a preliminary step, the feasibility of fusing lidar, radar, and RGB for pedestrian detection that potentially can be used for autonomous driving that uses a fully connected convolutional neural network architecture for multimodal sensors. The core of the network is based on SegNet, a pixel-wise semantic segmentation network. In this context, lidar and radar were incorporated by transforming them from 3D pointclouds into 2D gray images with 16-bit depths, and RGB images were incorporated with three channels. The proposed architecture uses a single SegNet for each sensor reading, and the outputs are then applied to a fully connected neural network to fuse the three modalities of sensors. Afterwards, an up-sampling network is applied to recover the fused data. Additionally, a custom dataset of 60 images was proposed for training the architecture, with an additional 10 for evaluation and 10 for testing, giving a total of 80 images. The experiment results show a training mean pixel accuracy of 99.7\% and a training mean intersection over union of 99.5\%. Also, the testing mean of the IoU was 94.4\%, and the testing pixel accuracy was 96.2\%. These metric results have successfully demonstrated the effectiveness of using semantic segmentation for pedestrian detection under the modalities of three sensors. Despite some overfitting in the model during experimentation, it performed well in detecting people in test mode. Therefore, it is worth emphasizing that the focus of this work is to show that this method is feasible to be used, as it works regardless of the size of the dataset. Also, a bigger dataset would be necessary to achieve a more appropiate training. This method gives the advantage of detecting pedestrians as the human eye does, thereby resulting in less ambiguity. Additionally, this work has also proposed an extrinsic calibration matrix method for sensor alignment between radar and lidar based on singular value decomposition.

}, keywords = {autonomous driving, Convolutional Neural Networks, sensor calibration, sensor fusion}, doi = {10.3390/s23084167}, url = {https://www.mdpi.com/1424-8220/23/8/4167}, author = {Alfredo Ch{\'a}vez Plasencia and Pablo Garc{\'\i}a-G{\'o}mez and Eduardo Bernal P{\'e}rez and Gerard de-Mas-Gim{\'e}nez and Josep R Casas and S. Royo} } @conference {cPinazo22, title = {Perception in the era of Autonomous Vehicles}, booktitle = {Photonics 4 Smart Cities, SCEWC 2022}, year = {2022}, month = {11/2022}, publisher = {Photonics21}, organization = {Photonics21}, address = {Barcelona}, url = {https://www.fotonica21.org/photonics-4-smart-cities}, author = {Jorge Pinazo and Adolfo Ler{\'\i}n and Francesc Xavier de Gibert and {\'A}lvaro Moliner and Daniel Sevilla and Antonio Jurado and Iv{\'a}n R{\'\i}os and Rodrigo Jerez and Jaime Santiago and {\'A}lvaro Linuesa and Antonio Cano and Federico Dios and Adolfo Comer{\'o}n and Josep R Casas and Jos{\'e} A L{\'a}zaro} } @conference {cGeleta21, title = {PixInWav: Residual Steganography for Hiding Pixels in Audio}, booktitle = {ICASSP}, year = {2022}, month = {06/2021}, abstract = {

Steganography comprises the mechanics of hiding data in a host media that may be publicly available. While previous works focused on unimodal setups (e.g., hiding images in images, or hiding audio in audio), PixInWav targets the multimodal case of hiding images in audio. To this end, we propose a novel residual architecture operating on top of short-time discrete cosine transform (STDCT) audio spectrograms. Among our results, we find that the residual audio steganography setup we propose allows independent encoding of the hidden image from the host audio without compromising quality. Accordingly, while previous works require both host and hidden signals to hide a signal, PixInWav can encode images offline --- which can be later hidden, in a residual fashion, into any audio signal. Finally, we test our scheme in a lab setting to transmit images over airwaves from a loudspeaker to a microphone verifying our theoretical insights and obtaining promising results.

\ Deep Learning Barcelona Symposium 2022

Presentation from the early stages of the project (January 2021):

}, author = {Geleta, Margarita and Punt{\'\i}, Cristina and McGuinness, Kevin and Pons, Jordi and Cristian Canton-Ferrer and Xavier Gir{\'o}-i-Nieto} } @conference {cBartusiak, title = {Predicting Dog Phenotypes from Genotypes}, booktitle = {44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC{\textquoteright}22)}, year = {2022}, abstract = {

In this paper, we analyze dog genotypes - positions of DNA sequences that often vary between different dogs - in order to predict the corresponding phenotypes - unique characteristics that result from different genetic code. More specifically, given chromosome data from a dog, we aim to predict its breed category, height, and weight. We explore a variety of linear and non-linear classification and regression techniques to accomplish these three tasks. We show that linear methods generally outperform or match non-linear methods for breed classification. However, the reverse case is true for height and weight regression. We also evaluate the performance of all of these methods based on the number of input features used in the analysis. We conduct experiments using different fractions of the full genomic sequences and demonstrate that phenotypes can be predicted with as few as 0.5\% of the input features available for our analysis, and dog breeds can be classified with 50\% balanced accuracy with as few as 0.02\% of the features.

}, doi = {https://doi.org/10.1101/2022.04.13.488108 }, author = {Bartusiak, Emily and Barrab{\'e}s, M{\'\i}riam and Aigerim Rymbe and J{\'u}lia Gimbernat and Cayetana L{\'o}pez and Lorenzo Barberis and Mas-Montserrat, Daniel and Xavier Gir{\'o}-i-Nieto and Ioannidis, Alexander G.} } @conference {cPodlipnik21, title = {Personalized medicine in melanoma patients aided by artificial intelligence}, booktitle = {Clinical Translation of Medical Image Computing and Computer Assisted Interventions (CLINICCAI) Workshop at MICCAI}, year = {2021}, month = {09/2021}, abstract = {

The 8th Edition of the American Joint Committee on Cancer (AJCC) staging system1 is the current standard for classifying patients into prognostic and treatment groups. This classification is used to predict the evolution of the patient, and therefore treatment actions provided to the individual. However, patients at the same stage behave differently, indicating that the current classification system is often insufficient to provide a customized prognosis for each patient2. It is, therefore, necessary to improve patient classification into prognostic groups. Furthermore, patients{\textquoteright} systemic and surgical treatments often involve significant toxicities and morbidities that impact their quality of life (i.e., sentinel node biopsy is not needed for 80\% of the melanoma patients, 50\% of patients do not benefit from adjuvant treatment)3. Therefore, melanoma patients should benefit from a more precise risk estimation.

We create a survival dataset for melanoma risk estimation and train survival XGBoost algorithms4 to predict the mortality, relapse, and metastasis risk. We compare their performance to the AJCC 2018 risk stratification system. Furthermore, we train classifiers to predict the risk of a positive lymph node biopsy and distant metastasis on melanoma patients and compare the performance of the proposed system to the clinical practice.

}, author = {Sebastian Podlipnik and Carlos Hernandez and Anil Kiroglu and Sergio Garc{\'\i}a and Joan Ficapal and Julio Burgos and Neus Calbet and Susana Puig and Josep Malvehy and Ver{\'o}nica Vilaplana and Marc Combalia} } @conference {cNieto, title = {PiCoEDL: Discovery and Learning of Minecraft Navigation Goals from Pixels and Coordinates}, booktitle = {CVPR 2021 Embodied AI Workshop}, year = {2021}, month = {06/2021}, abstract = {

Defining a reward function in Reinforcement Learning (RL) is not always possible or very costly. For this reason, there is a great interest in training agents in a task-agnostic manner making use of intrinsic motivations and unsupervised techniques. Due to the complexity to learn useful behaviours in pixel-based domains, the results obtained in RL are still far from the remarkable results obtained in domains such as computer vision and natural language processing. We hypothesize that RL agents will also benefit from unsupervised pre-trainings with no extrinsic rewards, analogously to how humans mostly learn, especially in the early stages of life. Our main contribution is the deployment of the Explore, Discover and Learn (EDL) paradigm for unsupervised learning to the pixel space. In particular, our work focuses on the MineRL environment, where the observation of the agent is represented by: (a) its spatial coordinates in the Minecraft virtual world, and (b) an image from an egocentric viewpoint.

}, author = {Nieto, Juan Jos{\'e} and Creus, Roger and Xavier Gir{\'o}-i-Nieto} } @conference {cCreus, title = {PixelEDL: Unsupervised Skill Discovery and Learning from Pixels}, booktitle = {CVPR 2021 Embodied AI Workshop}, year = {2021}, month = {06/2021}, abstract = {

We tackle embodied visual navigation in a task-agnostic set-up by putting the focus on the unsupervised discovery of skills (or options) that provide a good coverage of states. Our approach intersects with empowerment: we address the reward-free skill discovery and learning tasks to discover {\textquotedblleft}what{\textquotedblright} can be done in an environment and {\textquotedblleft}how{\textquotedblright}. For this reason, we adopt the existing Explore, Discover and Learn (EDL) paradigm, tested only in toy example mazes, and extend it to pixel-based state representations available for embodied AI agents.

}, author = {Creus, Roger and Nieto, Juan Jos{\'e} and Xavier Gir{\'o}-i-Nieto} } @conference {cMayoral21b, title = {Prediction of amyloid pathology in cognitively unimpaired individuals using structural MRI}, booktitle = {Alzheimer{\textquoteright}s Association International Conference}, year = {2021}, month = {07/2021}, author = {Irene Cumplido-Mayoral and Silvia Ingala and Luigi Lorenzini and Alle Meije Wink and Sven Haller and Jose Luis Molinuevo and Robin Wolz and Alessandro Palombit and Adam J Schwarz and Ga{\"e}l Chetelat and Pierre Payoux and Pablo Martinez-Lage and Giovanni Frisoni and Nick C Fox and Craig W Ritchie and Joanna M Wardlaw and Adam Waldman and Frederik Barkhof and Ver{\'o}nica Vilaplana and Juan D. Gispert} } @mastersthesis {xPunti20, title = {PixInPix: Hidding Pixels in Pixels}, year = {2020}, abstract = {

PixInPix is an steganography hidding system of images within other images.\ The system designed is able to create, from an cover image and a message, a new steganography image. This new stego-image looks as similar as possible as the cover but has the message hidden in it.\ Our approach adopts the U-net architecture and combines two reconstruction losses to provide a simple yet effective approach tested in low resolution images from MNIST, CIFAR and ImageNet.

}, author = {Punt{\'\i}, Cristina}, editor = {McGuinness, Kevin and Cristian Canton-Ferrer and Xavier Gir{\'o}-i-Nieto} } @article {aCasamitjanac, title = {Projection to Latent Spaces disentangles pathological effects on brain morphology in the asymptomatic phase of Alzheimer{\textquoteright}s disease}, journal = {Frontiers in Neurology, section Applied Neuroimaging}, volume = {11}, year = {2020}, month = {07/2020}, chapter = {648}, abstract = {

Alzheimer{\textquoteright}s disease (AD) continuum is defined as a cascade of several neuropathological processes that can be measured using biomarkers such as cerebrospinal fluid (CSF) levels of Aβ, p-tau and t-tau. In parallel, brain anatomy can be characterized through imaging techniques such as magnetic resonance imaging (MRI). In this work, we relate both sets of measurements seeking associations between biomarkers and brain structure that can be indicative of AD progression. The goal is to uncover underlying multivariate effects of AD pathology on regional brain morphological information. For this purpose, we use the projection to latent structures (PLS)\ method. Using PLS, we find a low dimensional latent space that best describes the covariance between both sets of measurements on the same subjects. Possible confounder effects (age and sex) on brain morphology are included in the model and regressed out using an orthogonal PLS model. We look for statistically significant correlations between brain morphology and CSF biomarkers that explain part of the volumetric variance at each region-of-interest (ROI). Furthermore, we use a clustering technique to discover a small set of CSF-related patterns describing the AD continuum. We apply this technique to the study of subjects in the whole AD continuum from the preclinical asymptomatic stages all through to the symptomatic groups. Subsequent analyses involve splitting the course of the disease into diagnostic categories: cognitively unimpaired subjects (CU), mild cognitive impaired subjects (MCI) and dementia subjects (AD-dementia) where all symptoms are due to AD.

}, keywords = {Alzheimer{\textquoteright}s disease, brain morphology, CSF biomarkers, Latent model, PLS, preclinical AD}, author = {Adri{\`a} Casamitjana and Paula Petrone and Jose Luis Molinuevo and Juan D. Gispert and Ver{\'o}nica Vilaplana} } @conference {cMas, title = {Picking groups instead of samples: A close look at Static Pool-based Meta-Active Learning}, booktitle = {ICCV Workshop - MDALC 2019}, year = {2019}, month = {10/2019}, address = {Seoul, South Korea}, abstract = {

Active Learning techniques are used to tackle learning problems where obtaining training labels is costly. In this work we use Meta-Active Learning to learn to select a subset of samples from a pool of unsupervised input for further annotation. This scenario is called Static Pool-based Meta-Active Learning. We propose to extend existing approaches by performing the selection in a manner that, unlike previous works, can handle the selection of each sample based on the whole selected subset.

}, doi = {http://dx.doi.org/10.1109/ICCVW.2019.00171}, author = {Mas, Ignasi and Morros, J.R. and Ver{\'o}nica Vilaplana} } @article {xLLados19, title = {The pillars of the Computer Vision Catalan Alliance}, year = {2019}, abstract = {

Panel at the 6th Annual Catalan Meeting on Computer Vision.

$\#$ACM2019 @JosepLlados now introducing CVCatalliance with @xantallavina and experts from different sectors: @Vilynx @Satellogic @osalap @TheCollider_MWC @DocXavi pic.twitter.com/o5tVWW0gMQ

{\textemdash} CVC_UAB (@CVC_UAB) September 17, 2019
}, author = {LLad{\'o}s, Josep and Bou, Elisenda and Bressan, Marco and Sala, Oscar and Xavier Gir{\'o}-i-Nieto} } @unpublished {cRamon19, title = {Plug-and-Train Loss for Model-Based Single View 3D Reconstruction}, journal = {BMVA Technical Meeting: 3D vision with Deep Learning}, year = {2019}, month = {02/2019}, publisher = {UPC}, address = {London, UK}, abstract = {

Obtaining 3D geometry from images is a well studied problem by the computer vision community. In the concrete case of a single image, a considerable amount of prior knowledge is often required to obtain plausible reconstructions. Recently, deep neural networks in combination with 3D morphable models (3DMM) have been used in order to address the lack of scene information, leading to more accurate results. Nevertheless, the losses employed during the training process are usually a linear combination of terms where the coefficients, also called hyperparameters, must be carefully tuned for each dataset to obtain satisfactory results. In this work we propose a hyperparameters-free loss that exploits the geometry of the problem for learning 3D reconstruction from a single image. The proposed formulation is not dataset dependent, is robust against very large camera poses and jointly optimizes the shape of the object and the camera pose.

Plug and-train Loss for Single View 3D Reconstruction from Universitat Polit{\`e}cnica de Catalunya
}, author = {Ramon, Eduard and Villar, Jordi and Ruiz, Guillermo and Batard, Thomas and Xavier Gir{\'o}-i-Nieto} } @conference {cPareto, title = {Prediction of a second clinical event in CIS patients by combining lesion and brain features}, booktitle = {Congress of the European Comitee for Treatment and Research in Multiple Sclerosis (ECTRIMS 2019)}, year = {2019}, month = {09/2019}, abstract = {

Prediction of a second clinical event in clinically isolated syndrome (CIS) patients, leading to clinically definite multiple sclerosis (CDMS), is still a matter of investigation. The aim of the current study was to predict conversion to CDMS after a first attack, by means of a machine-learning approach and MRI-derived brain features. For that purpose, lesion-based features (volume and distribution within brain lobes) as well as cortical thickness (CT) and deep grey matter volumes (DGMV) were used. The final goal was to determine which features were more frequently found in the classification between converters and non-converters.

}, author = {Deborah Pareto and Pau Vidal and M. Alberich and Carlos Lopez and C. Auger and M. Tintor{\'e} and X. Montalban and J. Sastre-Garriga and Ver{\'o}nica Vilaplana and Alex Rovira} } @article {aPetrone, title = {Prediction of amyloid pathology in cognitively unimpaired individuals using voxelwise analysis of longitudinal structural brain MRI}, journal = {Alzheimer{\textquoteright}s Research \& Therapy}, volume = {11}, year = {2019}, month = {12/2019}, abstract = {

Background: Magnetic resonance imaging (MRI) has unveiled specific alterations at different stages of Alzheimer{\textquoteright}s disease (AD) pathophysiologic continuum constituting what has been established as {\textquoteleft}AD signature{\textquoteright}. To what extent MRI can detect amyloid-related cerebral changes from structural MRI in cognitively unimpaired individuals is still an area open for exploration.

Method: Longitudinal 3D-T1 MRI scans were acquired from a subset of the ADNI cohort comprising 403 subjects: 79 controls (Ctrls), 50 preclinical AD (PreAD), 274 MCI and dementia due to AD (MCI/AD). Amyloid CSF was used as gold-standard measure with established cut-offs (\<192pg/mL) to establish diagnostic categories. Cognitively unimpaired individuals were defined as Ctrls if were amyloid negative and PreAD otherwise. The MCI/AD group was amyloid positive. Only subjects with the same diagnostic category at baseline and follow-up visits were considered for the study. Longitudinal morphometric analysis was performed using SPM12 to calculate Jacobian determinant maps. Statistical analysis was carried out on these jacobian maps to identify structural changes that were significantly different between diagnostic categories. A machine learning classifier was applied on Jacobian determinant maps to predict the presence of abnormal amyloid levels in cognitively unimpaired individuals. The performance of this classifier was evaluated using receiver operating characteristic curve analysis and as a function of the follow-up time between MRI scans. We applied a cost function to assess the benefit of using this classifier in the triaging of individuals in a clinical trial-recruitment setting.

Results: The optimal follow-up time for classification of Ctrls vs PreAD was Δt\>2.5 years and hence, only subjects within this temporal span are used for evaluation (15 Ctrls, 10 PreAD). The longitudinal voxel-based classifier achieved an AUC=0.87 (95\%CI:0.72-0.97). The brain regions that showed the highest discriminative power to detect amyloid abnormalities were the medial, inferior and lateral temporal lobes, precuneus, caudate heads, basal forebrain and lateral ventricles.

Conclusions: Our work supports that machine learning applied to longitudinal brain volumetric changes can be used to predict, with high precision, presence of amyloid abnormalities in cognitively unimpaired subjects. Used as a triaging method to identify a fixed number of amyloid positive individuals, this longitudinal voxelwise classifier is expected to avoid 55\% of unnecessary CSF and/or PET scans and reduce economic cost by 40\%.

}, doi = {https://doi.org/10.1186/s13195-019-0526-8}, url = {https://link.springer.com/article/10.1186/s13195-019-0526-8}, author = {Paula Petrone and Adri{\`a} Casamitjana and Carles Falcon and Miguel Artigues C{\`a}naves and G. Operto and R. Cacciaglia and Jose Luis Molinuevo and Ver{\'o}nica Vilaplana and Juan D. Gispert} } @conference {cSalembier19, title = {Processing Radar Images with Hierarchical Region-Based Representations and Graph Signal Processing Tools}, booktitle = {Workshop on Digital Topology and Mathematical Morphology associated to the 21st IAPR International Conference on Discrete Geometry for Computer Imagery, Keynote speech}, year = {2019}, address = {Paris, France}, author = {Salembier, P.} } @conference {cAssensa, title = {PathGAN: Visual Scanpath Prediction with Generative Adversarial Networks}, booktitle = {ECCV 2018 Workshop on Egocentric Perception, Interaction and Compution (EPIC)}, year = {2018}, month = {07/2018}, publisher = {Springer}, organization = {Springer}, address = {Munich, Germany}, abstract = {

We introduce PathGAN, a deep neural network for visual scanpath prediction trained on adversarial examples. A visual scanpath is defined as the sequence of fixation points over an image defined by a human observer with its gaze. PathGAN is composed of two parts, the generator and the discriminator. Both parts extract features from images using off-the-shelf networks, and train recurrent layers to generate or discriminate scanpaths accordingly. In scanpath prediction, the stochastic nature of the data makes it very difficult to generate realistic predictions using supervised learning strategies, but we adopt adversarial training as a suitable alternative. Our experiments prove how PathGAN improves the state of the art of visual scanpath prediction on the Salient360! dataset.

This work obtained the\ 2nd award in Prediction of Head-gaze Scan-paths for Images, and the 2nd award in Prediction of Eye-gaze Scan-paths for Images at the IEEE ICME 2018 Salient360! Challenge.

}, doi = {10.1007/978-3-030-11021-5_25}, url = {https://doi.org/10.1007/978-3-030-11021-5_25}, author = {Assens, Marc and McGuinness, Kevin and Xavier Gir{\'o}-i-Nieto and O{\textquoteright}Connor, N.} } @mastersthesis {xCanaves18, title = {Prevention of Alzheimer{\textquoteright}s Disease: a contribution from MRI and machine learning}, year = {2018}, abstract = {

Alzheimer{\textquoteright}s disease (AD) is a neurodegenerative disease and the leading cause of dementia (50-70\% of cases). Despite worldwide efforts, there is no progress in developing a cure for AD and dementia. Machine learning, hand in hand with magnetic resonance imaging (MRI), come to the aid of disease diagnostics. In the scope of AD, many efforts have been dedicated to the automated detection of mild-cognitive impairment and dementia. In our research, instead we focus on the prediction of AD in its preclinical stage using machine learning classification. Another key innovation is that we will work with a longitudinal pipeline. In addition to classification, the project focuses on detecting the most relevant imaging voxels for classification, that is, to help us locate where AD-specific structural brain changes occur. We have improved classification performance i n comparison with results obtained with cross-sectional datasets in previous studies and we have identified possible regions of interest based on feature scores obtained from feature selection.

}, author = {Miguel Artigues C{\`a}naves}, editor = {Paula Petrone and Ver{\'o}nica Vilaplana} } @article {aCasamitjana18, title = { Projection to Latent Spaces Disentangles Specific Cerebral Morphometric Patterns Associated to Aging and Preclinical AD}, journal = {Alzheimer{\textquoteright}s \& Dementia: The Journal of the Alzheimer{\textquoteright}s Association }, volume = {14}, year = {2018}, month = {07/2018}, pages = {P869-P870}, doi = {10.1016/j.jalz.2018.06.1111}, author = {Adri{\`a} Casamitjana and P. Petrone and M. Artigues and J.L. Molinuevo and J.D. Gispert and Ver{\'o}nica Vilaplana} } @conference {cCasamitjanab, title = {Projection to Latent Spaces Disentangles Specific Cerebral Morphometric Patterns Associated to Aging and Preclinical AD}, booktitle = {Alzheimer{\textquoteright}s Association International Conference}, year = {2018}, month = {07/2018}, address = {Chicago, USA}, abstract = {

Partial Least Squares (PLS) is a mathematical technique that relates two sets of observable variables by means of a few latent explanatory factors. The aim of this study is to use PLS to discover the associations between CSF biomarkers and structural brain imaging in preclinical AD and to disentangle their specific contribution from confounding demographic factors.PLS is able to disentangle the cerebral morphometric patterns associated to preclinical AD stages from other demographic factors. Results with both cortical thickness and volumetric data present significant overlap, thus showing the robustness of this approach. Interestingly, volumetric data showed more significant correlations with CSF Abeta than cortical thickness.

}, author = {Adri{\`a} Casamitjana and P. Petrone and M. Artigues and J.L. Molinuevo and J.D. Gispert and Ver{\'o}nica Vilaplana} } @mastersthesis {xBozal, title = {Personalized Image Classi cation from EEG Signals using Deep Learning}, year = {2017}, abstract = {

This thesis explores the semantic classi cation of images based processing of electroencephalogram\ (EEG) signals generated by the viewer{\textquoteright}s brain. The work extends an existing solution by\ exploring the gains obtained when the parameters of the classi er are adapted to the user. Firstly,\ we developed an universal end-to-end model based on deep learning that extracts features from\ the EEG raw signals predicts the semantic content of the image between 40 possible classes from\ the ImageNet dataset. Our main contribution aims at adapting this universal model to new users,\ in order to build a personalized model based on the minimum feedback from the new user. We\ explored di fferent deep learning architectures and hyperparameters to obtain a better accuracy\ than the baseline by Spampinato et al (CVPR 2017). We achieve a result of 89.03 \% and 90.34\ \% of the universal and personalized model respectively.\ 

Personalized Image classification of EEG Signals using Deep Learning from Xavier Giro-i-Nieto
}, author = {Bozal, Alberto}, editor = {Xavier Gir{\'o}-i-Nieto} } @mastersthesis {xBernal, title = {Predicting emotion in movies: Recurrent and convolutional models applied to videos}, year = {2017}, abstract = {

This Thesis explores di erent approaches using deep learning techniques to predict emotions\ in videos.\ Working with videos implies a huge amount of data including visual frames and acoustic\ samples. The rst step of the project is basically to extract features to represent the videos in\ small sets of arrays. This procedure is done using pre-trained models based on Convolutional\ Networks, the state of the art in visual recognition. Firstly, visual features are extracted using\ 3D convolutions and acoustic features are extracted using VGG19, a pre-trained convolutional\ model for images fi ne-tuned to accept the audio inputs.\ Later, these features are fed into a Recurrent model capable of exploiting the temporal information.\ Emotions are measured in terms of valence and arousal, values between [-1, 1]. Additionally,\ the same techniques are also used to attempt to predict fear scenes. In consequence, this thesis\ deals with both regression and classi cation problems.\ Several architectures and di erent parameters have been tested in order to achieve the best\ performance. Finally, the results will be published in the MediaEval 2017 Challenge and compared\ to the state-of-the-art solutions.

Predicting Emotion in Movies from Xavier Giro-i-Nieto
}, author = {Bernal, Oriol}, editor = {Xavier Gir{\'o}-i-Nieto and Zaharieva, Maia} } @mastersthesis {xCardoner, title = {Predicting Media Interestingness}, year = {2017}, abstract = {

This thesis explores the application of a deep learning approach for the prediction of media\ interestingness. Two di erent models are investigated, one for the prediction of image and one\ for the prediction of video interestingness.\ For the prediction of image interestingness, the ResNet50 network is ne-tuned to obtain\ best results. First, some layers are added. Next, the model is trained and fine-tuned using data\ augmentation, dropout, class weights, and changing other hyper parameters.\ For the prediction of video interestingness, rst, features are extracted with a 3D convolutional\ network. Next a LSTM network is trained and fine-tuned with the features.\ The nal result is a binary label for each image/video: 1 for interesting, 0 for not interesting.\ Additionally, a con fidence value is provided for each prediction. Finally, the Mean Average\ Precision (MAP) is employed as evaluation metric to estimate the quality of the fi nal results.

Predicting Media Interestingness from Xavier Giro-i-Nieto
}, author = {Cardoner, LLuc}, editor = {Zaharieva, Maia and Xavier Gir{\'o}-i-Nieto} } @conference {cPerez-Pellitero, title = {PSyCo: Manifold Span Reduction for Super Resolution}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition}, year = {2016}, month = {06/2016}, address = {Las Vegas, Nevada, USA}, abstract = {

The main challenge in Super Resolution (SR) is to discover the mapping between the low- and high-resolution manifolds of image patches, a complex ill-posed problem which has recently been addressed through piecewise linear regression with promising results. In this paper we present a novel regression-based SR algorithm that benefits from an extended knowledge of the structure of both manifolds. We propose a transform that collapses the 16 variations induced from the dihedral group of transforms (i.e. rotations, vertical and horizontal reflections) and antipodality (i.e. di- ametrically opposed points in the unitary sphere) into a single primitive. The key idea of our transform is to study the different dihedral elements as a group of symmetries within the high-dimensional manifold. We obtain the respective set of mirror-symmetry axes by means of a frequency analysis of the dihedral elements, and we use them to collapse the redundant variability through a modified symmetry distance. The experimental validation of our algorithm shows the effectiveness of our approach, which obtains competitive quality with a dictionary of as little as 32 atoms (reducing other methods{\textquoteright} dictionaries by at least a factor of 32) and further pushing the state-of-the-art with a 1024 atoms dictionary.

}, url = {http://perezpellitero.github.io/}, author = {E. Perez-Pellitero and Salvador, J. and Ruiz-Hidalgo, J. and Rosenhahn, B.} } @mastersthesis {xGris-Sarabia, title = {Pyxel, una llibreria per a l{\textquoteright}anotació automàtica de fotografies}, year = {2015}, abstract = {

Tutor:\ Xavier Gir{\'o}-i-Nieto\ (UPC)\ 

Grau: Enginyeria en Sistemes Audiovisuals (4 anys) a l{\textquoteright}Escola d{\textquoteright}Enginyeria de Terrassa (UPC)

Donat que actualment la generació i difusió de contingut multimèdia ha incrementat molt, s{\textquoteright}està fent recerca per trobar eines d{\textquoteright}anotació automàtica o semiautomàtica d{\textquoteright}imatges, sons, vídeos, etc.

Aquest document recull la informació sobre el desenvolupament i funcionament de la llibreria Pyxel, orientada, concretament, a l{\textquoteright}anotació d{\textquoteright}imatges mitjançant la informació visual de la imatge i la informació textual que l{\textquoteright}acompanya, les metadades.

El Pyxel és un conjunt de classes desenvolupades en llenguatge de programació Python per dur a terme una cadena d{\textquoteright}anotació d{\textquoteright}imatges complerta, es a dir, permet l{\textquoteright}extracció de característiques visuals amb descriptors SIFT i l{\textquoteright}extracció de característiques textuals de metadades mitjançant l{\textquoteright}algoritme de processat de text TF-IDF, així com eines per crear ambdós vocabularis. També proporciona eines per l{\textquoteright}entrenament i la detecció d{\textquoteright}un classificador SVM.

Per tal de gestionar les dades de grans volums d{\textquoteright}imatges les eines del Pyxel estan pensades per fer processat d{\textquoteright}imatges en paral{\textperiodcentered}lel, molt útil per aprofitar d{\textquoteright}una manera optima els recursos d{\textquoteright}un servei de computació gestionat amb SLURM.

Qualificaci{\'o} final: A (9/10)

Thesis defence by Irene Gris Sarabia, with Pyxel developer team.

Defensa del Projecte de Fi de Grau de la Irene Gris Sarabia (23 de gener de 2015)
}, keywords = {image classification, python}, author = {Gris-Sarabia, Irene}, editor = {Xavier Gir{\'o}-i-Nieto} } @conference {cManchon-Vizuete, title = {Photo Clustering of Social Events by Extending PhotoTOC to a Rich Context}, booktitle = {ICMR 2014 Workshop on Social Events in Web Multimedia (SEWM)}, year = {2014}, month = {04/2014}, publisher = {ACM}, organization = {ACM}, address = {Glasgow, Scotland}, abstract = {

The popularisation of the storage of photos on the cloud has opened new opportunities and challenges for the organisation and extension of photo collections. This paper presents a light computational solution for the clustering of web photos based on social events. The proposal combines a first over-segmentation of the photo collections of each user based on temporal cues, as previously proposed in PhotoTOC. On a second stage, the resulting mini-clusters are merged based on contextual metadata such as geolocation, keywords and user IDs. Results indicate that, although temporal cues are very relevant for event clustering, robust solutions should also consider all these additional features.

[Conference website]

Photo Clustering of Social Events by Extending PhotoTOC to a Rich Context from Xavi Gir{\'o}

}, keywords = {Clustering, Event Detection, Photo Collections}, url = {http://hdl.handle.net/2117/23009}, author = {Manchon-Vizuete, Daniel and Gris-Sarabia, Irene and Xavier Gir{\'o}-i-Nieto} } @article {aAlonso-Gonzalez, title = {PolSAR Time Series Processing with Binary Partition Trees}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {52}, year = {2014}, month = {06/2014}, pages = {3553 {\textendash} 3567}, abstract = {

This paper deals with the processing of polarimetric synthetic aperture radar (SAR) time series. Different approaches to deal with the temporal dimension of the data are considered, which are derived from different target characterizations in this dimension. These approaches are the basis for defining two different binary partition tree (BPT) structures that are employed for SAR polarimetry (PolSAR) data processing. Once constructed, the BPT is processed by a tree pruning, producing a set of spatiotemporal homogeneous regions, and estimating the polarimetric response within them. It is demonstrated that the proposed technique preserves the PolSAR information in the spatial and the temporal domains without introducing bias nor distortion. Additionally, the evolution of the data in the temporal dimension is also analyzed, and techniques to obtain BPT-based scene change maps are defined. Finally, the proposed techniques are employed to process two real RADARSAT-2 data sets.

}, issn = {0196-2892}, doi = {10.1109/TGRS.2013.2273664}, author = {Alonso-Gonz{\'a}lez, A. and L{\'o}pez-Mart{\'\i}nez, C. and Salembier, P.} } @conference {cPalou14, title = {Precision-Recall-Classification Evaluation Framework: Application to Depth Estimation on Single Images}, booktitle = {European Conference on Computer Vision (ECCV)}, year = {2014}, month = {09/2014}, address = {Zurich}, abstract = {

Many computer vision applications involve algorithms that can be decomposed in two main steps. In a first step, \ events or objects are detected and, in a subsequent stage, detections are assigned to various classes. Examples of such {\textquoteleft}{\textquoteleft}detection plus classification{\textquoteright}{\textquoteright} problems can be found in human pose classification, object recognition or action classification among others. In this paper, we focus on a special case: depth ordering on single images. In this \ problem, the detection step consists of the image segmentation, and the classification stage assigns a depth gradient to each contour or a depth order to each region.

In this paper, we discuss the limitations of the classical Precision-Recall evaluation framework for these kind of problems and define an extended framework called {\textquoteleft}{\textquoteleft}Precision-Recall-Classfication{\textquoteright}{\textquoteright} (PRC). Then, we apply this framework to depth ordering problems and design two specific PRC

measures to evaluate both the local and the global depth consistencies. We use these measures to evaluate precisely state of the art depth ordering systems for monocular images. Based on this evaluation, we also propose an extension to the method of [Cal13] applying an optimal graph cut on a hierarchical segmentation structure such as ultrametric contour maps. The resulting system is proven to provide better results than state of the art algorithms.

}, author = {Palou, G. and Salembier, P.} } @phdthesis {dGallego13, title = {Parametric Region-Based Foreground Segmentation in Planar and Multi-View Sequences}, year = {2013}, school = {Universitat Polit{\`e}cnica de Catalunya (UPC)}, type = {phd}, abstract = {

Foreground segmentation in video sequences is an important area of the image processing that attracts great interest among the scientist community, since it makes possible the detection of the objects that appear in the sequences under analysis, and allows us to achieve a correct performance of high level applications which use foreground segmentation as an initial step. The current Ph.D. thesis entitled Parametric Region-Based Foreground Segmentation in Planar and Multi-View Sequences details, in the following pages, the research work carried out within this field. In this investigation, we propose to use parametric probabilistic models at pixel-wise and region level in order to model the different classes that are involved in the classification process of the different regions of the image: foreground, background and, in some sequences, shadow. The development is presented in the following chapters as a generalization of the techniques proposed for objects segmentation in 2D planar sequences to 3D multi-view environment, where we establish a cooperative relationship between all the sensors that are recording the scene. Hence, different scenarios have been analyzed in this thesis in order to improve the foreground segmentation techniques: In the first part of this research, we present segmentation methods appropriate for 2D planar scenarios. We start dealing with foreground segmentation in static camera sequences, where a system that combines pixel-wise background model with region-based foreground and shadow models is proposed in a Bayesian classification framework. The research continues with the application of this method to moving camera scenarios, where the Bayesian framework is developed between foreground and background classes, both characterized with region-based models, in order to obtain a robust foreground segmentation for this kind of sequences. The second stage of the research is devoted to apply these 2D techniques to multi-view acquisition setups, where several cameras are recording the scene at the same time. At the beginning of this section, we propose a foreground segmentation system for sequences recorded by means of color and depth sensors, which combines different probabilistic models created for the background and foreground classes in each one of the views, by taking into account the reliability that each sensor presents. The investigation goes ahead by proposing foreground segregation methods for multi-view smart room scenarios. In these sections, we design two systems where foreground segmentation and 3D reconstruction are combined in order to improve the results of each process. The proposals end with the presentation of a multi-view segmentation system where a foreground probabilistic model is proposed in the 3D space to gather all the object information that appears in the views. The results presented in each one of the proposals show that the foreground segmentation and also the 3D reconstruction can be improved, in these scenarios, by using parametric probabilistic models for modeling the objects to segment, thus introducing the information of the object in a Bayesian classification framework.

}, url = {http://hdl.handle.net/10803/130813}, author = {Gallego, J.}, editor = {M. Pard{\`a}s} } @conference {cAlonso-Gonzalez13, title = {PolSAR time series processing and analysis based on Binary Partition Trees}, booktitle = {PoLinSAR 2013 Workshop}, year = {2013}, address = {Frascati (Rome), Italy}, author = {Alonso-Gonz{\'a}lez, A. and L{\'o}pez-Mart{\'\i}nez, C. and Salembier, P.} } @article {aAlonso-Gonzalez13, title = {Processing Multidimensional SAR and Hyperspectral Images With Binary Partition Tree}, journal = {Proceedings of the IEEE}, volume = {101}, year = {2013}, month = {March, 2013}, pages = {723 - 747}, chapter = {723 - 747}, abstract = {

The current increase of spatial as well as spectral resolutions of modern remote sensing sensors represents a real opportunity for many practical applications but also generates important challenges in terms of image processing. In particular, the spatial correlation between pixels and/or the spectral correlation between spectral bands of a given pixel cannot be ignored. The traditional pixel-based representation of images does not facilitate the handling of these correlations.

In this paper, we discuss the interest of a particular hierarchical region-based representation of images based on binary partition tree (BPT). This representation approach is very flexible as it can be applied to any type of image. Here both optical and radar images will be discussed. Moreover, once the image representation is computed, it can be used for many different applications. Filtering, segmentation, and classification will be detailed in this paper. In all cases, the interest of the BPT representation over the classical pixel-based representation will be highlighted.

}, doi = {10.1109/JPROC.2012.2205209}, author = {Alonso-Gonz{\'a}lez, A. and Valero, S. and Chanussot, J. and L{\'o}pez-Mart{\'\i}nez, C. and Salembier, P.} } @phdthesis {dGiro-i-Nieto12, title = {Part-Based Object Retrieval With Binary Partition Trees}, volume = {Phd}, year = {2012}, month = {05/2012}, pages = {215}, school = {Universitat Polit{\`e}cnica de Catalunya (UPC)}, type = {phd}, address = {Barcelona, Catalonia}, abstract = {

This thesis addresses the problem of visual object retrieval, where a user formulates a query to an image database by providing one or multiple examples of an object of interest. The presented techniques aim both at finding those images in the database that contain the object as well as locating the object in the image and segmenting it from the background.

Every considered image, both the ones used as queries and the ones contained in the target database, is represented as a Binary Partition Tree (BPT), the hierarchy of regions previously proposed by Salembier and Garrido (2000). This data structure offers multiple opportunities and challenges when applied to the object retrieval problem.

One application of BPTs appears during the formulation of the query, when the user must interactively segment the query object from the background. Firstly, the BPT can assist in adjusting an initial marker, such as a scribble or bounding box, to the object contours. Secondly, BPT can also define a navigation path for the user to adjust an initial selection to the appropriate scale.

The hierarchical structure of the BPT is also exploited to extract a new type of visual words named Hierarchical Bag of Regions (HBoR). Each region defined in the BPT is char- acterized with a feature vector that combines a soft quantization on a visual codebook with an ecient bottom-up computation through the BPT. These features allow the definition of a novel feature space, the Parts Space, where each object is located according to the parts that compose it.

HBoR features have been applied to two scenarios for object retrieval, both of them solved by considering the decomposition of the objects in parts. In the first scenario, the query is formulated with a single object exemplar which is to be matched with each BPT in the target database. The matching problem is solved in two stages: an initial top-down one that assumes that the hierarchy from the query is respected in the target BPT, and a second bottom-up one that relaxes this condition and considers region merges which are not in the target BPT.

The second scenario where HBoR features are applied considers a query composed of several visual objects, such as a person, a bottle or a logo. In this case, the provided exemplars are considered as a training set to build a model of the query concept. This model is composed of two levels, a first one where each part is modelled and detected separately, and a second one\ that characterises the combinations of parts that describe the complete object. The analysis process exploits the hierarchical nature of the BPT by using a novel classifier that drives an efficient top-down analysis of the target BPTs.\ \ 

Xavier Gir{\'o}-i-Nieto, "Part-based Object Retrieval with Binary Partition Trees" from Xavi Gir{\'o}
}, url = {http://hdl.handle.net/10803/108909}, author = {Xavier Gir{\'o}-i-Nieto}, editor = {Marqu{\'e}s, F. and Chang, Shih-Fu} } @conference {cGiro-i-Nieto12, title = {Part-Based Object Retrieval With Binary Partition Trees}, booktitle = {Doctoral Consortium in Computer Vision and Pattern Recognition (CVPR)}, year = {2012}, month = {06/2012}, publisher = {IEEE Computer Society}, organization = {IEEE Computer Society}, address = {Providence (RI), USA}, abstract = {

This Phd thesis {\textquotedblleft}Part-based Object Retrieval with Binary Partition Trees{\textquotedblright} addresses the problem of\ visual object retrieval, where a user formulates a query to an image database by providing one or\ multiple examples of an object of interest. The developed techniques aim both at \ finding those\ images in the database that contain the object as well as locating the object in the image and\ segmenting it from the background. Every considered image, both the ones used as queries and the\ ones contained in the target database, is represented as a Binary Partition Tree (BPT), the hierarchy\ of regions previously proposed by Salembier and Garrido (2000). This data structure offers multiple\ opportunities and challenges when applied to the object retrieval problem.

One application of BPTs appears during the formulation of the query, when the user must\ interactively segment the query object from the background. Firstly, the BPT can assist in adjusting\ an initial marker, such as a scribble or bounding box, to the object contours. Secondly, BPT can also\ define a navigation path for the user to adjust an initial selection to the appropriate scale. The\ hierarchical structure of the BPT is also exploited to extract a new type of visual words named\ Hierarchical Bag of Regions (HBoR). Each region defined in the BPT is characterized with a\ feature vector that combines a soft quantization on a visual codebook with an efficient bottom-up\ computation through the BPT. These features allow the definition of a novel feature space, the Parts\ Space, where each object is located according to the parts that compose it.

HBoR features have been applied to two scenarios for object retrieval, both of them solved\ by considering the decomposition of the objects in parts. In the first scenario, the query is formulated\ with a single object exemplar which is to be matched with each BPT in the target database. The\ matching problem is solved in two stages: an initial top-down one that assumes that the hierarchy\ from the query is \ respected in the target BPT, and a second bottom-up one that relaxes \ this\ condition and considers region merges which are not in the target BPT. The second scenario where

HBoR features are applied considers a query composed of several visual objects. In this case, theprovided exemplars are considered as a training set to build a model of the query concept. This\ model is composed of two levels, a \ first one where each part is modelled and detected separately,\ and a second one that characterises the combinations of parts that describe the complete object. The\ analysis process exploits the hierarchical nature of the BPT by using a novel classifier that drives an\ efficient top-down analysis of the target BPTs.

}, author = {Xavier Gir{\'o}-i-Nieto} } @conference {cIrurueta12, title = {PROMEDS: An adaptive robust fundamental matrix estimation approach}, booktitle = {3DTV Conference}, year = {2012}, month = {09/2012}, publisher = {IEEE}, organization = {IEEE}, address = {Zurich, Switzerland}, abstract = {

Accurate fundamental matrix estimation from computed correspondences is hard to achieve depending on the constraints on computational time and available data (i.e. correspondences and quality scores). Several algorithms exist for this task, like the 8-points, the 7-points algorithm or robust methods such as RANSAC, MSAC or LMedS. Robust methods are capable of discriminating correspondence outliers, thus, obtaining better results. Additionally, some variations of the previous methods have been proposed. For instance PROSAC is an improvement of RANSAC which takes into account additional information of the quality of the matches to largely reduce the computational cost of the fundamental matrix estimation process. This work proposes a new robust method for fundamental matrix estimation that combines the benefits of PROSAC and LMedS algorithms, namely improved quality, reduced computational time and less parameters to adjust.

}, keywords = {Fundamental Matrix, LMedS, PROSAC}, doi = {https://doi.org/10.1109/3DTV.2012.6365452}, author = {Irurueta, A. and Morros, J.R.} } @conference {cAlonso-Gonzalez11, title = {PolSAR speckle filtering and segmentation based on binary partition tree representation}, booktitle = {5th International Workshop on Science and Applications of SAR Polarimetry and Polarimetric Interferometry, PolInSAR 2011}, year = {2011}, pages = {1{\textendash}19}, address = {Frascati (Rome), Italy}, url = {http://cataleg.upc.edu/record=b1233548~S1*cat}, author = {Alonso-Gonz{\'a}lez, A. and L{\'o}pez-Mart{\'\i}nez, C. and Salembier, P.} } @conference {cSalvador10a, title = {Photo-consistent surfaces from a sparse set of viewpoints}, booktitle = {2010 IEEE International Conference on Image Processing}, year = {2010}, pages = {4045{\textendash}4048}, isbn = {978-1-4244-5655-6}, doi = {10.1109/ICIP.2010.5649006}, url = {http://www.cic.unb.br/~mylene/PI_2010_2/ICIP10/pdfs/0004045.pdf}, author = {Salvador, J. and Josep R Casas} } @conference {cBragos10, title = {Proceso de inversi{\'o}n de competencias gen{\'e}ricas en los nuevos planes de estudios de grado de la ETSETB de acuerdo con el modelo CDIO}, booktitle = {IX Congreso de Tecnolog{\'\i}as Aplicadas a la Ense{\~n}anza de la Electr{\'o}nica}, year = {2010}, pages = {1{\textendash}9}, isbn = {978-84-96737-68-6}, url = {http://upcommons.upc.edu/e-prints/urlFiles?idDrac=4451103}, author = {Bragos, R. and Alarc{\'o}n, E. and Cabrera, M. and Calveras, A. and Comellas, J. and O{\textquoteright}Callaghan, J. and Pegueroles, J. and Prat, L. and Saez, G. and Sarda, J. and Elisa Sayrol} } @conference {cCalderero09b, title = {Performance evaluation of probability density estimators for unsupervised information theoretical region merging}, booktitle = {16th International Conference on Image Processing}, year = {2009}, pages = {4397{\textendash}4400}, isbn = {978-1-4244-5655-6}, doi = {10.1109/ICIP.2009.5413621}, url = {http://hdl.handle.net/2117/9510}, author = {Calderero, F. and Marqu{\'e}s, F. and Ortega, A.} } @inbook {bBernardin09, title = {Person Tracking}, booktitle = {Computers in the human interaction loop}, year = {2009}, pages = {11{\textendash}22}, publisher = {Springer}, organization = {Springer}, address = {London}, abstract = {

One of the most basic building blocks for the understanding of human actions and interactions is the accurate detection and tracking of persons in a scene. In constrained scenarios involving at most one subject, or in situations where persons can be confined to a controlled monitoring space or required to wear markers, sensors, or microphones, these tasks can be solved with relative ease. However, when accurate localization and tracking have to be performed in an unobtrusive or discreet fashion, using only distantly placed microphones and cameras, in a variety of natural and uncontrolled scenarios, the challenges posed are much greater. The problems faced by video analysis are those of poor or uneven illumination, low resolution, clutter or occlusion, unclean backgrounds, and multiple moving and uncooperative users that are not always easily distinguishable.

}, isbn = {978-1-84882-053-1}, doi = {10.1007/978-1-84882-054-8}, url = {http://www.springerlink.com/content/tj2x827107563616/}, author = {Bernardin, K. and Stiefelhagen, R. and Pnevmatikakis, A. and Lanz, O. and Brutti, A. and Josep R Casas and Potamianos, G.} } @inbook {bVilaplana09a, title = {Processament puntual}, booktitle = {Codificaci{\'o} del so i de la imatge}, year = {2009}, pages = {5{\textendash}65}, isbn = {9788469148365}, url = {http://ccuc.cbuc.cat:2082/record=b4149618~S23*cat}, author = {Ver{\'o}nica Vilaplana} } @conference {cCanton-Ferrer08a, title = {Particle Filtering and Sparse Sampling for Multi-Person 3D Tracking}, booktitle = {IEEE International Conference on Image Processing}, year = {2008}, pages = {2644{\textendash}2647}, isbn = {1-4244-1764-3}, doi = {10.1109/ICIP.2008.4712337}, author = {Cristian Canton-Ferrer and Sblendido, R. and Josep R Casas and M. Pard{\`a}s} } @conference {cDimiccoli07, title = {Perceptual filtering with connected operators and image inpainting}, booktitle = {Proc. of International Symposium on Mathematical Morphology, ISMM 2007}, year = {2007}, pages = {227{\textendash}238}, address = {Rio de Janeiro, Brazil}, isbn = {978-85-17-00032-4}, author = {Dimiccoli, M. and Salembier, P.} } @article {aDescampe07, title = {Prefetching and caching strategies for remote and interactive browsing of JPEG2000 images}, journal = {IEEE transactions on image processing}, volume = {16}, number = {5}, year = {2007}, pages = {1339{\textendash}1354}, issn = {1057-7149}, author = {Descampe, A. and Vleeschouwer, D. and Iregui, M. and Macq, B. and Marqu{\'e}s, F.} } @conference {cCabrera07, title = {Proyecto de Innovaci{\'o}n Docente COM@WEB (COMunicaciones en la WEB)}, booktitle = {VIII Simposio Nacional de Tecnolog{\'\i}as de la Informaci{\'o}n y las Comunicaciones en la Educaci{\'o}n. SINTICE{\textquoteright}07}, year = {2007}, month = {09/2007}, pages = {113{\textendash}120}, abstract = {

COMalaWEB significa Comunicaciones en la Web y es una plataforma de e-learning que ofrece diferentes recursos docentes on-line a los estudiantes universitarios con el objetivo de facilitar el estudio preferentemente en las tem{\'a}ticas de procesado de se{\~n}al y comunicaciones.

La plataforma es interactiva y se ha dise{\~n}ado para promover la experimentaci{\'o}n e-learning y adquisici{\'o}n de h{\'a}bitos de estudio basados en el uso de nuevas tecnolog{\'\i}as.

El sistema en un futuro a corto plazo podr{\'a} generar itinerarios autom{\'a}ticos de estudio al estudiante en funci{\'o}n del rendimiento obtenido en las pruebas de autoevaluaci{\'o}n y a partir de informaci{\'o}n empaquetada en metadatos e integrada en una base de datos de objetos de aprendizaje.

El objetivo del proyecto COMalaWEB a medio y largo plazo es constituir un punto de encuentro en la www para estudiantes, profesores y profesionales relacionados con el {\'a}rea de las telecomunicaciones y la docencia a nivel universitario.

Uno de los cursos integrados en COMalaWEB es el laboratorio virtual de comunicaciones anal{\'o}gicas y digitales LaViCAD, de libre difusi{\'o}n y acceso y basado en la simulaci{\'o}n de diferentes sistemas de comunicaciones que pueden ser utilizados tanto en docencia presencial como en educaci{\'o}n a distancia.\ 

Actualmente se puede consultar el contenido del proyecto en la plataforma: http://comweb.upc.edu/

}, isbn = {978-84-9732-597-4}, author = {Cabrera, M. and Closas, P. and Alfredo, L. and Xavier Gir{\'o}-i-Nieto and Rey, F.} } @conference {cDescampe06, title = {Pre-fetching strategies for remote and interactive browsing of JPEG2000 images}, booktitle = {International Conference on Image Processing}, year = {2006}, pages = {3203{\textendash}3206}, author = {Descampe, A. and De Vleeschouwer, C. and Iregui, M. and Macq, B. and Marqu{\'e}s, F.} } @inbook {bCanton-Ferrer06, title = {Projective Kalman Filter: Multiocular Tracking of 3D Locations Towards Scene Understanding}, booktitle = {Machine Learning for Multimodal Interaction}, volume = {3869}, year = {2006}, pages = {250{\textendash}261}, publisher = {Springer}, organization = {Springer}, address = {Berlin / Heidelberg}, abstract = {

This paper presents a novel approach to the problem of estimating and tracking 3D locations of multiple targets in a scene using measurements gathered from multiple calibrated cameras. Estimation and tracking is jointly achieved by a newly conceived computational process, the Projective Kalman filter (PKF), allowing the problem to be treated in a single, unified framework. The projective nature of observed data and information redundancy among views is exploited by PKF in order to overcome occlusions and spatial ambiguity. To demonstrate the effectiveness of the proposed algorithm, the authors present tracking results of people in a SmartRoom scenario and compare these results with existing methods as well.

}, isbn = {978-3-540-32549-9}, doi = {10.1007/11677482_22}, author = {Cristian Canton-Ferrer and Josep R Casas and Tekalp, M. and M. Pard{\`a}s} } @conference {cCanton-Ferrer05b, title = {Projective Kalman Filter: Multiocular Tracking of 3D Locations Towards Scene Understanding}, booktitle = {2nd Joint Workshop on Multimodal Interaction and Related Machine Learning Algorithms}, year = {2005}, abstract = {

This paper presents a novel approach to the problem of estimating and tracking 3D locations of multiple targets in a scene using measurements gathered from multiple calibrated cameras. Estimation and tracking is jointly achieved by a newly conceived computational process, the Projective Kalman {\textasciimacron}lter (PKF), allowing the problem to be treated in a single, uni{\textasciimacron}ed framework. The projective nature of observed data and information redundancy among views is exploited by PKF in order to overcome occlusions and spatial ambiguity. To demonstrate the e{\textregistered}ectiveness of the proposed algorithm, the authors present tracking results of people in a SmartRoom scenario and compare these results with existing methods as well.

}, author = {Cristian Canton-Ferrer and Cristian Canton-Ferrer and Josep R Casas and Tekalp, M. and M. Pard{\`a}s} } @conference {cMarcello04, title = {Precise upwelling and filaments automatic extraction from multisensorial imagery}, booktitle = {IEEE International Geoscience and Remote Sensing Symposium 2004}, year = {2004}, pages = {2018{\textendash}2021}, author = {Marcello, J. and F. Eugenio and Marqu{\'e}s, F.} } @conference {cSole04a, title = {Prediction design for discrete generalized lifting}, booktitle = {Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2004}, year = {2004}, pages = {78{\textendash}82}, address = {Brussels, Belgium}, isbn = {978-1-4244-3610-1}, author = {Sol{\'e}, J. and Salembier, P.} } @conference {cVentosa03, title = {Perceptual mask estimation from watermarked images}, booktitle = {SPIE Electronic Imaging 2003}, year = {2003}, pages = {10{\textendash}17}, isbn = {?}, author = {Ventosa, S. and Elisa Sayrol and Vidal, J.} } @conference {cEugenio01, title = {Pixel and sub-pixel accuracy in satellite image georeferencing using an automatic contour matching approach}, booktitle = {IEEE International Conference on Image Processing}, year = {2001}, isbn = {0-7803-6727-8}, author = {F. Eugenio and Marqu{\'e}s, F. and Marcello, J.} } @conference {cMarcotegui00, title = {Partition-based image representation as basis for user-assisted segmentation}, booktitle = {IEEE International Conference on Image Processing}, year = {2000}, isbn = {0-7803-6300-0}, author = {Marcotegui, B. and Marqu{\'e}s, F. and Wollborn, M.} } @article {pMarques99, title = {Partition Coding Method and Device}, number = {994004364}, year = {1999}, month = {02/1999}, type = {Invention patent}, address = {International}, issn = {994004364}, author = {Marqu{\'e}s, F.} } @article {pMarques99a, title = {Partition Decoding Method and Device}, number = {994017614}, year = {1999}, month = {07/1999}, type = {Invention patent}, address = {International}, issn = {994017614}, author = {Marqu{\'e}s, F. and Gomila, C. and Gasull, A.} } @conference {cMorros99, title = {A proposal for dependent optimization in scalabale region-based coding systems}, booktitle = {1999 IEEE International Conference on Image Processing}, year = {1999}, pages = {295{\textendash}299}, abstract = {

We address in this paper the problem of optimal coding in the framework of region-based video coding systems, with a special stress on content-basedfunctionalities. We present a coding system that can provide scaled layers (using PSNR or temporal content-based scalability) such that each one has an optimal partition with optimal bit allocation among the resulting regions. This coding system is based on a de- pendent optimization algorithm that can provide joint opti- mality for a group of layers or a group of frames.

}, isbn = {978-1-59593-733-9}, doi = {10.1109/ICIP.1999.819598}, author = {Morros, J.R. and Marqu{\'e}s, F.} } @article {aMarques98, title = {Prediction of image partitions using fourier descriptors : application to segmentation-based coding schemes}, journal = {IEEE transactions on image processing}, volume = {7}, number = {4}, year = {1998}, pages = {529{\textendash}542}, issn = {1057-7149}, author = {Marqu{\'e}s, F. and Llorens, B. and Gasull, A.} } @conference {cMarques96a, title = {Partition coding using multigrid chain code and motion compensation}, booktitle = {IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING}, year = {1996}, author = {Marqu{\'e}s, F. and Gasull, A.} } @conference {cPardas96, title = {Partition tree for a segmentation-based video coding system}, booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 1996}, year = {1996}, pages = {1982{\textendash}1985}, address = {Atlanta (GA), USA}, isbn = {-}, author = {M. Pard{\`a}s and Salembier, P. and Marqu{\'e}s, F. and Morros, J.R.} } @conference {cOliveras96b, title = {Practical extensions of connected operators}, booktitle = {International Symposium on Mathematical Morphology, ISMM 1996}, year = {1996}, pages = {97{\textendash}110}, address = {Atlanta (GA), USA}, author = {Salembier, P. and Albert Oliveras} } @conference {cTorres96, title = {Prediction error image coding using a modified stochastic vector quantization scheme}, booktitle = {IEEE International Conference on Image Processing}, year = {1996}, pages = {451{\textendash}454}, author = {Torres, L. and Josep R Casas} }