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M. Bressan, David Guillamet, & Jordi Vitria. (2000). Using an ICA representation of local color histograms for object recognition..
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Bogdan Raducanu, & Jordi Vitria. (2006). Aprendiendo a Aprender: de Maquinas Listas a Maquinas Inteligentes.
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Joan M. Nuñez, Jorge Bernal, Miquel Ferrer, & Fernando Vilariño. (2014). Impact of Keypoint Detection on Graph-based Characterization of Blood Vessels in Colonoscopy Videos. In CARE workshop.
Abstract: We explore the potential of the use of blood vessels as anatomical landmarks for developing image registration methods in colonoscopy images. An unequivocal representation of blood vessels could be used to guide follow-up methods to track lesions over different interventions. We propose a graph-based representation to characterize network structures, such as blood vessels, based on the use of intersections and endpoints. We present a study consisting of the assessment of the minimal performance a keypoint detector should achieve so that the structure can still be recognized. Experimental results prove that, even by achieving a loss of 35% of the keypoints, the descriptive power of the associated graphs to the vessel pattern is still high enough to recognize blood vessels.
Keywords: Colonoscopy; Graph Matching; Biometrics; Vessel; Intersection
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David Masip, & Jordi Vitria. (2004). Boosted Linear Projections for Discriminant Analysis.
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Sergio Escalera, & Petia Radeva. (2004). Fast greyscale road sign model matching and recognition.
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Robert Benavente, & Maria Vanrell. (2004). Fuzzy Colour Naming Based on Sigmoid Membership Functions..
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Xavier Otazu, & Maria Vanrell. (2004). Building Perceived Colour Images..
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Francesc Tous, Maria Vanrell, & Ramon Baldrich. (2004). Exploring Colour Constancy Solutions..
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E. Barakova, Maya Dimitrova, T. Lorents, & Petia Radeva. (2004). The Web as an “Autobiographical Agent”.
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Jun Wan, Sergio Escalera, Gholamreza Anbarjafari, Hugo Jair Escalante, Xavier Baro, Isabelle Guyon, et al. (2017). Results and Analysis of ChaLearn LAP Multi-modal Isolated and ContinuousGesture Recognition, and Real versus Fake Expressed Emotions Challenges. In Chalearn Workshop on Action, Gesture, and Emotion Recognition: Large Scale Multimodal Gesture Recognition and Real versus Fake expressed emotions at ICCV.
Abstract: We analyze the results of the 2017 ChaLearn Looking at People Challenge at ICCV. The challenge comprised three tracks: (1) large-scale isolated (2) continuous gesture recognition, and (3) real versus fake expressed emotions tracks. It is the second round for both gesture recognition challenges, which were held first in the context of the ICPR 2016 workshop on “multimedia challenges beyond visual analysis”. In this second round, more participants joined the competitions, and the performances considerably improved compared to the first round. Particularly, the best recognition accuracy of isolated gesture recognition has improved from 56.90% to 67.71% in the IsoGD test set, and Mean Jaccard Index (MJI) of continuous gesture recognition has improved from 0.2869 to 0.6103 in the ConGD test set. The third track is the first challenge on real versus fake expressed emotion classification, including six emotion categories, for which a novel database was introduced. The first place was shared between two teams who achieved 67.70% averaged recognition rate on the test set. The data of the three tracks, the participants' code and method descriptions are publicly available to allow researchers to keep making progress in the field.
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Yagmur Gucluturk, Umut Guclu, Marc Perez, Hugo Jair Escalante, Xavier Baro, Isabelle Guyon, et al. (2017). Visualizing Apparent Personality Analysis with Deep Residual Networks. In Chalearn Workshop on Action, Gesture, and Emotion Recognition: Large Scale Multimodal Gesture Recognition and Real versus Fake expressed emotions at ICCV (pp. 3101–3109).
Abstract: Automatic prediction of personality traits is a subjective task that has recently received much attention. Specifically, automatic apparent personality trait prediction from multimodal data has emerged as a hot topic within the filed of computer vision and, more particularly, the so called “looking
at people” sub-field. Considering “apparent” personality traits as opposed to real ones considerably reduces the subjectivity of the task. The real world applications are encountered in a wide range of domains, including entertainment, health, human computer interaction, recruitment and security. Predictive models of personality traits are useful for individuals in many scenarios (e.g., preparing for job interviews, preparing for public speaking). However, these predictions in and of themselves might be deemed to be untrustworthy without human understandable supportive evidence. Through a series of experiments on a recently released benchmark dataset for automatic apparent personality trait prediction, this paper characterizes the audio and
visual information that is used by a state-of-the-art model while making its predictions, so as to provide such supportive evidence by explaining predictions made. Additionally, the paper describes a new web application, which gives feedback on apparent personality traits of its users by combining
model predictions with their explanations.
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Maryam Asadi-Aghbolaghi, Hugo Bertiche, Vicent Roig, Shohreh Kasaei, & Sergio Escalera. (2017). Action Recognition from RGB-D Data: Comparison and Fusion of Spatio-temporal Handcrafted Features and Deep Strategies. In Chalearn Workshop on Action, Gesture, and Emotion Recognition: Large Scale Multimodal Gesture Recognition and Real versus Fake expressed emotions at ICCV.
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Albert Clapes, Tinne Tuytelaars, & Sergio Escalera. (2017). Darwintrees for action recognition. In Chalearn Workshop on Action, Gesture, and Emotion Recognition: Large Scale Multimodal Gesture Recognition and Real versus Fake expressed emotions at ICCV.
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Katerine Diaz, Konstantia Georgouli, Anastasios Koidis, & Jesus Martinez del Rincon. (2017). Incremental model learning for spectroscopy-based food analysis. CILS - Chemometrics and Intelligent Laboratory Systems, 167, 123–131.
Abstract: In this paper we propose the use of incremental learning for creating and improving multivariate analysis models in the field of chemometrics of spectral data. As main advantages, our proposed incremental subspace-based learning allows creating models faster, progressively improving previously created models and sharing them between laboratories and institutions without requiring transferring or disclosing individual spectra samples. In particular, our approach allows to improve the generalization and adaptability of previously generated models with a few new spectral samples to be applicable to real-world situations. The potential of our approach is demonstrated using vegetable oil type identification based on spectroscopic data as case study. Results show how incremental models maintain the accuracy of batch learning methodologies while reducing their computational cost and handicaps.
Keywords: Incremental model learning; IGDCV technique; Subspace based learning; IdentificationVegetable oils; FT-IR spectroscopy
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Marta Diez-Ferrer, Debora Gil, Elena Carreño, Susana Padrones, Samantha Aso, Vanesa Vicens, et al. (2016). Positive Airway Pressure-Enhanced CT to Improve Virtual Bronchoscopic Navigation. CHEST - Chest Journal, 150(4), 1003A.
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