Xavier Boix, Josep M. Gonfaus, Fahad Shahbaz Khan, Joost Van de Weijer, Andrew Bagdanov, Marco Pedersoli, et al. (2009). Combining local and global bag-of-word representations for semantic segmentation. In Workshop on The PASCAL Visual Object Classes Challenge.
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Xavier Boix. (2009). Learning Conditional Random Fields for Stereo (Vol. 136). Master's thesis, , Bellaterra, Barcelona.
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Xavier Baro, Sergio Escalera, Petia Radeva, & Jordi Vitria. (2009). Generic Object Recognition in Urban Image Databases. In 12th International Conference of the Catalan Association for Artificial Intelligence (Vol. 202, pp. 27–34).
Abstract: In this paper we propose the construction of a visual content layer which describes the visual appearance of geographic locations in a city. We captured, by means of a Mobile Mapping system, a huge set of georeferenced images (>500K) which cover the whole city of Barcelona. For each image, hundreds of region descriptions are computed off-line and described as a hash code. All this information is extracted without an object of reference, which allows to search for any type of objects using their visual appearance. A new Visual Content layer is built over Google Maps, allowing the object recognition information to be organized and fused with other content, like satellite images, street maps, and business locations.
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Xavier Baro, Sergio Escalera, Petia Radeva, & Jordi Vitria. (2009). Visual Content Layer for Scalable Recognition in Urban Image Databases, Internet Multimedia Search and Mining. In 10th IEEE International Conference on Multimedia and Expo (1616–1619).
Abstract: Rich online map interaction represents a useful tool to get multimedia information related to physical places. With this type of systems, users can automatically compute the optimal route for a trip or to look for entertainment places or hotels near their actual position. Standard maps are defined as a fusion of layers, where each one contains specific data such height, streets, or a particular business location. In this paper we propose the construction of a visual content layer which describes the visual appearance of geographic locations in a city. We captured, by means of a Mobile Mapping system, a huge set of georeferenced images (> 500K) which cover the whole city of Barcelona. For each image, hundreds of region descriptions are computed off-line and described as a hash code. This allows an efficient and scalable way of accessing maps by visual content.
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Xavier Baro, Sergio Escalera, Jordi Vitria, Oriol Pujol, & Petia Radeva. (2009). Traffic Sign Recognition Using Evolutionary Adaboost Detection and Forest-ECOC Classification. TITS - IEEE Transactions on Intelligent Transportation Systems, 10(1), 113–126.
Abstract: The high variability of sign appearance in uncontrolled environments has made the detection and classification of road signs a challenging problem in computer vision. In this paper, we introduce a novel approach for the detection and classification of traffic signs. Detection is based on a boosted detectors cascade, trained with a novel evolutionary version of Adaboost, which allows the use of large feature spaces. Classification is defined as a multiclass categorization problem. A battery of classifiers is trained to split classes in an Error-Correcting Output Code (ECOC) framework. We propose an ECOC design through a forest of optimal tree structures that are embedded in the ECOC matrix. The novel system offers high performance and better accuracy than the state-of-the-art strategies and is potentially better in terms of noise, affine deformation, partial occlusions, and reduced illumination.
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Xavier Baro. (2009). Probabilistic Darwin Machines: A New Approach to Develop Evolutionary Object Detection (Jordi Vitria, Ed.). Ph.D. thesis, Ediciones Graficas Rey, .
Abstract: Ever since computers were invented, we have wondered whether they might perform some of the human quotidian tasks. One of the most studied and still nowadays less understood problem is the capacity to learn from our experiences and how we generalize the knowledge that we acquire. One of that unaware tasks for the persons and that more interest is awakening in different scientific areas since the beginning, is the one that is known as pattern recognition. The creation of models that represent the world that surrounds us, help us for recognizing objects in our environment, to predict situations, to identify behaviors... All this information allows us to adapt ourselves and to interact with our environment. The capacity of adaptation of individuals to their environment has been related to the amount of patterns that are capable of identifying.
This thesis faces the pattern recognition problem from a Computer Vision point of view, taking one of the most paradigmatic and extended approaches to object detection as starting point. After studying this approach, two weak points are identified: The first makes reference to the description of the objects, and the second is a limitation of the learning algorithm, which hampers the utilization of best descriptors.
In order to address the learning limitations, we introduce evolutionary computation techniques to the classical object detection approach.
After testing the classical evolutionary approaches, such as genetic algorithms, we develop a new learning algorithm based on Probabilistic Darwin Machines, which better adapts to the learning problem. Once the learning limitation is avoided, we introduce a new feature set, which maintains the benefits of the classical feature set, adding the ability to describe non localities. This combination of evolutionary learning algorithm and features is tested on different public data sets, outperforming the results obtained by the classical approach.
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Wenjuan Gong. (2009). Action priors for human pose tracking by particle filter. Master's thesis, , Bellaterra, Barcelona.
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T. Widemann, & Xavier Otazu. (2009). Titanias radius and an upper limit on its atmosphere from the September 8, 2001 stellar occultation. International Journal of Solar System Studies, 199(2), 458–476.
Abstract: On September 8, 2001 around 2 h UT, the largest uranian moon, Titania, occulted Hipparcos star 106829 (alias SAO 164538, a V=7.2, K0 III star). This was the first-ever observed occultation by this satellite, a rare event as Titania subtends only 0.11 arcsec on the sky. The star's unusual brightness allowed many observers, both amateurs or professionals, to monitor this unique event, providing fifty-seven occultations chords over three continents, all reported here. Selecting the best 27 occultation chords, and assuming a circular limb, we derive Titania's radius: View the MathML source (1-σ error bar). This implies a density of View the MathML source using the value View the MathML source derived by Taylor [Taylor, D.B., 1998. Astron. Astrophys. 330, 362–374]. We do not detect any significant difference between equatorial and polar radii, in the limit View the MathML source, in agreement with Voyager limb image retrieval during the 1986 flyby. Titania's offset with respect to the DE405 + URA027 (based on GUST86 theory) ephemeris is derived: ΔαTcos(δT)=−108±13 mas and ΔδT=−62±7 mas (ICRF J2000.0 system). Most of this offset is attributable to a Uranus' barycentric offset with respect to DE405, that we estimate to be: View the MathML source and ΔδU=−85±25 mas at the moment of occultation. This offset is confirmed by another Titania stellar occultation observed on August 1st, 2003, which provides an offset of ΔαTcos(δT)=−127±20 mas and ΔδT=−97±13 mas for the satellite. The combined ingress and egress data do not show any significant hint for atmospheric refraction, allowing us to set surface pressure limits at the level of 10–20 nbar. More specifically, we find an upper limit of 13 nbar (1-σ level) at 70 K and 17 nbar at 80 K, for a putative isothermal CO2 atmosphere. We also provide an upper limit of 8 nbar for a possible CH4 atmosphere, and 22 nbar for pure N2, again at the 1-σ level. We finally constrain the stellar size using the time-resolved star disappearance and reappearance at ingress and egress. We find an angular diameter of 0.54±0.03 mas (corresponding to View the MathML source projected at Titania). With a distance of 170±25 parsecs, this corresponds to a radius of 9.8±0.2 solar radii for HIP 106829, typical of a K0 III giant.
Keywords: Occultations; Uranus, satellites; Satellites, shapes; Satellites, dynamics; Ices; Satellites, atmospheres
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Stefan Ameling, Stephan Wirth, Dietrich Paulus, Gerard Lacey, & Fernando Vilariño. (2009). Texture-based Polyp Detection in Colonoscopy. In Proc. BILDVERARBEITUNG FÜR DIE MEDIZIN.
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Shida Beigpour. (2009). Physics-based Reflectance Estimation Applied to Recoloring (Vol. 137). Master's thesis, , Bellaterra, Barcelona.
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Sergio Escalera, Xavier Baro, Jordi Vitria, & Petia Radeva. (2009). Text Detection in Urban Scenes (video sample). In 12th International Conference of the Catalan Association for Artificial Intelligence (Vol. 202, 35–44).
Abstract: Abstract. Text detection in urban scenes is a hard task due to the high variability of text appearance: different text fonts, changes in the point of view, or partial occlusion are just a few problems. Text detection can be specially suited for georeferencing business, navigation, tourist assistance, or to help visual impaired people. In this paper, we propose a general methodology to deal with the problem of text detection in outdoor scenes. The method is based on learning spatial information of gradient based features and Census Transform images using a cascade of classifiers. The method is applied in the context of Mobile Mapping systems, where a mobile vehicle captures urban image sequences. Moreover, a cover data set is presented and tested with the new methodology. The results show high accuracy when detecting multi-linear text regions with high variability of appearance, at same time that it preserves a low false alarm rate compared to classical approaches
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Sergio Escalera, R. M. Martinez, Jordi Vitria, Petia Radeva, & Maria Teresa Anguera. (2009). Dominance Detection in Face-to-face Conversations. In 2nd IEEE Workshop on CVPR for Human communicative Behavior analysis (97–102).
Abstract: Dominance is referred to the level of influence a person has in a conversation. Dominance is an important research area in social psychology, but the problem of its automatic estimation is a very recent topic in the contexts of social and wearable computing. In this paper, we focus on dominance detection from visual cues. We estimate the correlation among observers by categorizing the dominant people in a set of face-to-face conversations. Different dominance indicators from gestural communication are defined, manually annotated, and compared to the observers opinion. Moreover, the considered indicators are automatically extracted from video sequences and learnt by using binary classifiers. Results from the three analysis shows a high correlation and allows the categorization of dominant people in public discussion video sequences.
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Sergio Escalera, Oriol Pujol, Petia Radeva, & Jordi Vitria. (2009). Measuring Interest of Human Dyadic Interactions. In 12th International Conference of the Catalan Association for Artificial Intelligence (Vol. 202, pp. 45–54).
Abstract: In this paper, we argue that only using behavioural motion information, we are able to predict the interest of observers when looking at face-to-face interactions. We propose a set of movement-related features from body, face, and mouth activity in order to define a set of higher level interaction features, such as stress, activity, speaking engagement, and corporal engagement. Error-Correcting Output Codes framework with an Adaboost base classifier is used to learn to rank the perceived observer's interest in face-to-face interactions. The automatic system shows good correlation between the automatic categorization results and the manual ranking made by the observers. In particular, the learning system shows that stress features have a high predictive power for ranking interest of observers when looking at of face-to-face interactions.
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Sergio Escalera, Oriol Pujol, & Petia Radeva. (2009). Separability of Ternary Codes for Sparse Designs of Error-Correcting Output Codes. PRL - Pattern Recognition Letters, 30(3), 285–297.
Abstract: Error Correcting Output Codes (ECOC) represent a successful framework to deal with multi-class categorization problems based on combining binary classifiers. In this paper, we present a new formulation of the ternary ECOC distance and the error-correcting capabilities in the ternary ECOC framework. Based on the new measure, we stress on how to design coding matrices preventing codification ambiguity and propose a new Sparse Random coding matrix with ternary distance maximization. The results on the UCI Repository and in a real speed traffic categorization problem show that when the coding design satisfies the new ternary measures, significant performance improvement is obtained independently of the decoding strategy applied.
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Sergio Escalera, Oriol Pujol, & Petia Radeva. (2009). Recoding Error-Correcting Output Codes. In 8th International Workshop of Multiple Classifier Systems (Vol. 5519, 11–21). Springer Berlin Heidelberg.
Abstract: One of the most widely applied techniques to deal with multi- class categorization problems is the pairwise voting procedure. Recently, this classical approach has been embedded in the Error-Correcting Output Codes framework (ECOC). This framework is based on a coding step, where a set of binary problems are learnt and coded in a matrix, and a decoding step, where a new sample is tested and classified according to a comparison with the positions of the coded matrix. In this paper, we present a novel approach to redefine without retraining, in a problem-dependent way, the one-versus-one coding matrix so that the new coded information increases the generalization capability of the system. Moreover, the final classification can be tuned with the inclusion of a weighting matrix in the decoding step. The approach has been validated over several UCI Machine Learning repository data sets and two real multi-class problems: traffic sign and face categorization. The results show that performance improvements are obtained when comparing the new approach to one of the best ECOC designs (one-versus-one). Furthermore, the novel methodology obtains at least the same performance than the one-versus-one ECOC design.
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