Abstract: This paper presents a new approach for the simultaneous estimation of the 3D pose and specific shape of a previously unseen face from a single image. The face pose is not limited to a frontal view. We describe a holistic approach based on a deformable 3D model and a learned statistical facial texture model. Rather than obtaining a person-specific facial surface, the goal of this work is to compute person-specific 3D face shape in terms of a few control parameters that are used by many applications. The proposed holistic approach estimates the 3D pose parameters as well as the face shape control parameters by registering the warped texture to a statistical face texture, which is carried out by a stochastic and genetic optimizer. The proposed approach has several features that make it very attractive: (i) it uses a single grey-scale image, (ii) it is person-independent, (iii) it is featureless (no facial feature extraction is required), and (iv) its learning stage is easy. The proposed approach lends itself nicely to 3D face tracking and face gesture recognition in monocular videos. We describe extensive experiments that show the feasibility and robustness of the proposed approach.

Abstract: Intravascular ultrasound (IVUS) represents a powerful imaging technique to explore coronary vessels and to study their morphology and histologic properties. In this paper, we characterize different tissues based on radial frequency, texture-based, and combined features. To deal with the classification of multiple tissues, we require the use of robust multi-class learning techniques. In this sense, error-correcting output codes (ECOC) show to robustly combine binary classifiers to solve multi-class problems. In this context, we propose a strategy to model multi-class classification tasks using sub-classes information in the ECOC framework. The new strategy splits the classes into different sub-sets according to the applied base classifier. Complex IVUS data sets containing overlapping data are learnt by splitting the original set of classes into sub-classes, and embedding the binary problems in a problem-dependent ECOC design. The method automatically characterizes different tissues, showing performance improvements over the state-of-the-art ECOC techniques for different base classifiers. Furthermore, the combination of RF and texture-based features also shows improvements over the state-of-the-art approaches.

Abstract: One of the most widely used assumptions in supervised learning is that data is independent and identically distributed. This assumption does not hold true in many real cases. Sequential learning is the discipline of machine learning that deals with dependent data such that neighboring examples exhibit some kind of relationship. In the literature, there are different approaches that try to capture and exploit this correlation, by means of different methodologies. In this paper we focus on meta-learning strategies and, in particular, the stacked sequential learning approach. The main contribution of this work is two-fold: first, we generalize the stacked sequential learning. This generalization reflects the key role of neighboring interactions modeling. Second, we propose an effective and efficient way of capturing and exploiting sequential correlations that takes into account long-range interactions by means of a multi-scale pyramidal decomposition of the predicted labels. Additionally, this new method subsumes the standard stacked sequential learning approach. We tested the proposed method on two different classification tasks: text lines classification in a FAQ data set and image classification. Results on these tasks clearly show that our approach outperforms the standard stacked sequential learning. Moreover, we show that the proposed method allows to control the trade-off between the detail and the desired range of the interactions.

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.

Abstract: Usually, pattern recognition systems consist of two main parts. On the one hand, the data acquisition and, on the other hand, the classification of this data on a certain category. In order to recognize which category a certain query element belongs to, a set of pattern models must be provided beforehand. An off-line learning stage is needed to train the classifier and to offer a robust classification of the patterns. Within the pattern recognition field, we are interested in the recognition of graphics and, in particular, on the analysis of documents rich in graphical information. In this context, one of the main concerns is to see if the proposed systems remain scalable with respect to the data volume so as it can handle growing amounts of symbol models. In order to avoid to work with a database of reference symbols, symbol spotting and on-the-fly symbol recognition methods have been introduced in the past years.
Generally speaking, the symbol spotting problem can be defined as the identification of a set of regions of interest from a document image which are likely to contain an instance of a certain queriedn symbol without explicitly applying the whole pattern recognition scheme. Our application framework consists on indexing a collection of graphic-rich document images. This collection is
queried by example with a single instance of the symbol to look for and, by means of symbol spotting methods we retrieve the regions of interest where the symbol is likely to appear within the documents. This kind of applications are known as focused retrieval methods.
In order that the focused retrieval application can handle large collections of documents there is a need to provide an efficient access to the large volume of information that might be stored. We use indexing strategies in order to efficiently retrieve by similarity the locations where a certain part of the symbol appears. In that scenario, graphical patterns should be used as indices for accessing and navigating the collection of documents.
These indexing mechanism allow the user to search for similar elements using graphical information rather than textual queries.
Along this thesis we present a spotting architecture and different methods aiming to build a complete focused retrieval application dealing with a graphic-rich document collections. In addition, a protocol to evaluate the performance of symbol
spotting systems in terms of recognition abilities, location accuracy and scalability is proposed.

Abstract: Handwritten word-spotting (HWS) is the pattern analysis task that consists in finding keywords in handwritten document images. So far, HWS has been applied mostly to historical documents in order to build search engines for such image collections. This thesis addresses the problem of word-spotting for detecting important keywords in business documents. This is a first step towards the process of automatic routing of correspondence based on content.
However, the application of traditional HWS techniques fails for this type of documents. As opposed to historical documents, real business documents present a very high variability in terms of writing styles, spontaneous writing, crossed-out words, spelling mistakes, etc. The main goal of this thesis is the development of pattern recognition techniques that lead to a high-performance HWS system for this challenging type of data.
We develop a statistical framework in which word models are expressed in terms of hidden Markov models and the a priori information is encoded in a universal vocabulary of Gaussian codewords. This systems leads to a very robust performance in word-spotting task. We also find that by constraining the word models to the universal vocabulary, the a priori information of the problem of interest can be exploited for developing new contributions. These include a novel writer adaptation method, a system for searching handwritten words by generating typed text images, and a novel model-based similarity between feature vector sequences.