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Oriol Rodriguez-Leor; E. Fernandez-Nofrerias; J. Mauri; R. Villuendas; C. Garcia; V. Valle; Cristina Cañero; Petia Radeva |
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An empiric model for three-dimensional reconstruction of coronary vessels from X-ray angiography |
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2003 |
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European Heart Journal (IF: 5.997), ESC Congress 2003 |
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Vienna |
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MILAB |
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BCNPCL @ bcnpcl @ RMF2003b |
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409 |
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Oriol Rodriguez-Leor; J. Mauri; E. Fernandez-Nofrerias; J. Lopez; M. Gomez; V. Valle; David Rotger; Petia Radeva |
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Title |
Coronary arteries three-dimensional quantification using intravascular ultrasound and angiography |
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2003 |
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European Heart Journal (IF: 5.997), ESC Congress 2003 |
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MILAB |
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no |
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BCNPCL @ bcnpcl @ RMF2003d |
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411 |
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Oriol Rodriguez-Leor; J. Mauri; Eduard Fernandez-Nofrerias; C. Garcia; R. Villuendas; Vicente del Valle; Debora Gil; Petia Radeva |
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Title |
Reconstruction of a spatio-temporal model of the intima layer from intravascular ultrasound sequences |
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2003 |
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European Heart Journal |
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ESC Congress |
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IAM;MILAB |
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no |
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IAM @ iam @ RMF2003c |
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1641 |
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Florin Popescu; Stephane Ayache; Sergio Escalera; Xavier Baro; Cecile Capponi; Patrick Panciatici; Isabelle Guyon |
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Title |
From geospatial observations of ocean currents to causal predictors of spatio-economic activity using computer vision and machine learning |
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Conference Article |
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2016 |
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European Geosciences Union General Assembly |
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18 |
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The big data transformation currently revolutionizing science and industry forges novel possibilities in multimodal analysis scarcely imaginable only a decade ago. One of the important economic and industrial problems that stand to benefit from the recent expansion of data availability and computational prowess is the prediction of electricity demand and renewable energy generation. Both are correlates of human activity: spatiotemporal energy consumption patterns in society are a factor of both demand (weather dependent) and supply, which determine cost – a relation expected to strengthen along with increasing renewable energy dependence. One of the main drivers of European weather patterns is the activity of the Atlantic Ocean and in particular its dominant Northern Hemisphere current: the Gulf Stream. We choose this particular current as a test case in part due to larger amount of relevant data and scientific literature available for refinement of analysis techniques.
This data richness is due not only to its economic importance but also to its size being clearly visible in radar and infrared satellite imagery, which makes it easier to detect using Computer Vision (CV). The power of CV techniques makes basic analysis thus developed scalable to other smaller and less known, but still influential, currents, which are not just curves on a map, but complex, evolving, moving branching trees in 3D projected onto a 2D image.
We investigate means of extracting, from several image modalities (including recently available Copernicus radar and earlier Infrared satellites), a parameterized presentation of the state of the Gulf Stream and its environment that is useful as feature space representation in a machine learning context, in this case with the EC’s H2020-sponsored ‘See.4C’ project, in the context of which data scientists may find novel predictors of spatiotemporal energy flow. Although automated extractors of Gulf Stream position exist, they differ in methodology and result. We shall attempt to extract more complex feature representation including branching points, eddies and parameterized changes in transport and velocity. Other related predictive features will be similarly developed, such as inference of deep water flux long the current path and wider spatial scale features such as Hough transform, surface turbulence indicators and temperature gradient indexes along with multi-time scale analysis of ocean height and temperature dynamics. The geospatial imaging and ML community may therefore benefit from a baseline of open-source techniques useful and expandable to other related prediction and/or scientific analysis tasks. |
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Vienna; Austria; April 2016 |
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EGU |
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HuPBA;MV; |
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Admin @ si @ PAE2016 |
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2772 |
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Author |
Olivier Penacchio; Xavier Otazu; A. wilkins; J. Harris |
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Title |
Uncomfortable images prevent lateral interactions in the cortex from providing a sparse code |
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Conference Article |
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2015 |
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European Conference on Visual Perception ECVP2015 |
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Liverpool; uk; August 2015 |
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ECVP |
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NEUROBIT; |
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Admin @ si @ POW2015 |
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2633 |
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Author |
Arash Akbarinia; C. Alejandro Parraga |
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Title |
Biologically Plausible Colour Naming Model |
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2015 |
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European Conference on Visual Perception ECVP2015 |
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Poster |
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Liverpool; UK; August 2015 |
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NEUROBIT; 600.068 |
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Admin @ si @ AkP2015 |
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2660 |
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Author |
Matthias S. Keil; Jordi Vitria |
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Title |
Does the brain generate representations of smooth brightness gradients? A novel account for Mach bands, Chevreul’s illusion, and a variant of the Ehrenstein disk |
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Miscellaneous |
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2005 |
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European Conference on Visual Perception |
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OR;MV |
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BCNPCL @ bcnpcl @ KeV2005b |
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607 |
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Author |
Jordi Roca; C. Alejandro Parraga; Maria Vanrell |
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Title |
Categorical Focal Colours are Structurally Invariant Under Illuminant Changes |
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Conference Article |
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2011 |
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European Conference on Visual Perception |
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196 |
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The visual system perceives the colour of surfaces approximately constant under changes of illumination. In this work, we investigate how stable is the perception of categorical \“focal\” colours and their interrelations with varying illuminants and simple chromatic backgrounds. It has been proposed that best examples of colour categories across languages cluster in small regions of the colour space and are restricted to a set of 11 basic terms (Kay and Regier, 2003 Proceedings of the National Academy of Sciences of the USA 100 9085\–9089). Following this, we developed a psychophysical paradigm that exploits the ability of subjects to reliably reproduce the most representative examples of each category, adjusting multiple test patches embedded in a coloured Mondrian. The experiment was run on a CRT monitor (inside a dark room) under various simulated illuminants. We modelled the recorded data for each subject and adapted state as a 3D interconnected structure (graph) in Lab space. The graph nodes were the subject\’s focal colours at each adaptation state. The model allowed us to get a better distance measure between focal structures under different illuminants. We found that perceptual focal structures tend to be preserved better than the structures of the physical \“ideal\” colours under illuminant changes. |
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Perception 40 |
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ECVP |
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CIC |
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no |
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Admin @ si @ RPV2011 |
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1867 |
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Author |
Ivet Rafegas; Maria Vanrell |
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Title |
Colour Visual Coding in trained Deep Neural Networks |
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2016 |
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European Conference on Visual Perception |
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Barcelona; Spain; August 2016 |
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ECVP |
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CIC |
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no |
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Admin @ si @ RaV2016b |
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2895 |
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Author |
Arash Akbarinia; C. Alejandro Parraga |
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Title |
Dynamically Adjusted Surround Contrast Enhances Boundary Detection, European Conference on Visual Perception |
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2016 |
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European Conference on Visual Perception |
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Barcelona; Spain; August 2016 |
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ECVP |
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NEUROBIT |
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no |
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Admin @ si @ AkP2016b |
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2900 |
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Author |
Miguel Angel Bautista; Sergio Escalera; Xavier Baro; Oriol Pujol; Jordi Vitria; Petia Radeva |
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Compact Evolutive Design of Error-Correcting Output Codes. Supervised and Unsupervised Ensemble Methods and Applications |
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2010 |
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European Conference on Machine Learning |
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119-128 |
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ECML |
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MILAB; OR;HUPBA;MV |
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Admin @ si @ BEB2010 |
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1775 |
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Author |
Alejandro Ariza-Casabona; Bartlomiej Twardowski; Tri Kurniawan Wijaya |
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Title |
Exploiting Graph Structured Cross-Domain Representation for Multi-domain Recommendation |
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Conference Article |
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2023 |
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European Conference on Information Retrieval – ECIR 2023: Advances in Information Retrieval |
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13980 |
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49–65 |
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Multi-domain recommender systems benefit from cross-domain representation learning and positive knowledge transfer. Both can be achieved by introducing a specific modeling of input data (i.e. disjoint history) or trying dedicated training regimes. At the same time, treating domains as separate input sources becomes a limitation as it does not capture the interplay that naturally exists between domains. In this work, we efficiently learn multi-domain representation of sequential users’ interactions using graph neural networks. We use temporal intra- and inter-domain interactions as contextual information for our method called MAGRec (short for Multi-dom Ain Graph-based Recommender). To better capture all relations in a multi-domain setting, we learn two graph-based sequential representations simultaneously: domain-guided for recent user interest, and general for long-term interest. This approach helps to mitigate the negative knowledge transfer problem from multiple domains and improve overall representation. We perform experiments on publicly available datasets in different scenarios where MAGRec consistently outperforms state-of-the-art methods. Furthermore, we provide an ablation study and discuss further extensions of our method. |
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ECIR |
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LAMP |
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Admin @ si @ ATK2023 |
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3933 |
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Author |
Md. Mostafa Kamal Sarker; Hatem A. Rashwan; Hatem A. Rashwan; Estefania Talavera; Syeda Furruka Banu; Petia Radeva; Domenec Puig |
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Title |
MACNet: Multi-scale Atrous Convolution Networks for Food Places Classification in Egocentric Photo-streams |
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Conference Article |
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2018 |
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European Conference on Computer Vision workshops |
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423-433 |
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First-person (wearable) camera continually captures unscripted interactions of the camera user with objects, people, and scenes reflecting his personal and relational tendencies. One of the preferences of people is their interaction with food events. The regulation of food intake and its duration has a great importance to protect against diseases. Consequently, this work aims to develop a smart model that is able to determine the recurrences of a person on food places during a day. This model is based on a deep end-to-end model for automatic food places recognition by analyzing egocentric photo-streams. In this paper, we apply multi-scale Atrous convolution networks to extract the key features related to food places of the input images. The proposed model is evaluated on an in-house private dataset called “EgoFoodPlaces”. Experimental results shows promising results of food places classification recognition in egocentric photo-streams. |
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ECCVW |
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MILAB; no menciona |
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no |
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Admin @ si @ SRR2018b |
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3185 |
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Author |
Jose Manuel Alvarez; Felipe Lumbreras; Antonio Lopez; Theo Gevers |
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Title |
Understanding Road Scenes using Visual Cues |
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Miscellaneous |
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2012 |
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European Conference on Computer Vision |
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DEMO |
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Florence; Italy |
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Admin @ si @ ALL2012 |
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2795 |
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Gabriel Villalonga; Sebastian Ramos; German Ros; David Vazquez; Antonio Lopez |
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Title |
3d Pedestrian Detection via Random Forest |
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Miscellaneous |
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2014 |
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European Conference on Computer Vision |
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231-238 |
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Pedestrian Detection |
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Our demo focuses on showing the extraordinary performance of our novel 3D pedestrian detector along with its simplicity and real-time capabilities. This detector has been designed for autonomous driving applications, but it can also be applied in other scenarios that cover both outdoor and indoor applications.
Our pedestrian detector is based on the combination of a random forest classifier with HOG-LBP features and the inclusion of a preprocessing stage based on 3D scene information in order to precisely determinate the image regions where the detector should search for pedestrians. This approach ends up in a high accurate system that runs real-time as it is required by many computer vision and robotics applications. |
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Zurich; suiza; September 2014 |
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ECCV-Demo |
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ADAS; 600.076 |
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Admin @ si @ VRR2014 |
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2570 |
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