Sergio Escalera, Mercedes Torres-Torres, Brais Martinez, Xavier Baro, Hugo Jair Escalante, Isabelle Guyon, et al. (2016). ChaLearn Looking at People and Faces of the World: Face AnalysisWorkshop and Challenge 2016. In 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops.
Abstract: We present the 2016 ChaLearn Looking at People and Faces of the World Challenge and Workshop, which ran three competitions on the common theme of face analysis from still images. The first one, Looking at People, addressed age estimation, while the second and third competitions, Faces of the World, addressed accessory classification and smile and gender classification, respectively. We present two crowd-sourcing methodologies used to collect manual annotations. A custom-build application was used to collect and label data about the apparent age of people (as opposed to the real age). For the Faces of the World data, the citizen-science Zooniverse platform was used. This paper summarizes the three challenges and the data used, as well as the results achieved by the participants of the competitions. Details of the ChaLearn LAP FotW competitions can be found at http://gesture.chalearn.org.
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Antonio Esteban Lansaque, Carles Sanchez, Agnes Borras, Marta Diez-Ferrer, Antoni Rosell, & Debora Gil. (2016). Stable Airway Center Tracking for Bronchoscopic Navigation. In 28th Conference of the international Society for Medical Innovation and Technology.
Abstract: Bronchoscopists use X‐ray fluoroscopy to guide bronchoscopes to the lesion to be biopsied without any kind of incisions. Reducing exposure to X‐ray is important for both patients and doctors but alternatives like electromagnetic navigation require specific equipment and increase the cost of the clinical procedure. We propose a guiding system based on the extraction of airway centers from intra‐operative videos. Such anatomical landmarks could be
matched to the airway centerline extracted from a pre‐planned CT to indicate the best path to the lesion. We present an extraction of lumen centers
from intra‐operative videos based on tracking of maximal stable regions of energy maps.
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Sergio Escalera, Jordi Gonzalez, Xavier Baro, & Jamie Shotton. (2016). Guest Editor Introduction to the Special Issue on Multimodal Human Pose Recovery and Behavior Analysis. TPAMI - IEEE Transactions on Pattern Analysis and Machine Intelligence, 28, 1489–1491.
Abstract: The sixteen papers in this special section focus on human pose recovery and behavior analysis (HuPBA). This is one of the most challenging topics in computer vision, pattern analysis, and machine learning. It is of critical importance for application areas that include gaming, computer interaction, human robot interaction, security, commerce, assistive technologies and rehabilitation, sports, sign language recognition, and driver assistance technology, to mention just a few. In essence, HuPBA requires dealing with the articulated nature of the human body, changes in appearance due to clothing, and the inherent problems of clutter scenes, such as background artifacts, occlusions, and illumination changes. These papers represent the most recent research in this field, including new methods considering still images, image sequences, depth data, stereo vision, 3D vision, audio, and IMUs, among others.
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Sergio Escalera, Jordi Gonzalez, Xavier Baro, Fernando Alonso, & Martha Mackay. (2016). Care Respite: a remote monitoring eHealth system for improving ambient assisted living. In Human Motion Analysis for Healthcare Applications.
Abstract: Advances in technology that capture human motion have been quite remarkable during the last five years. New sensors have been developed, such as the Microsoft Kinect, Asus Xtion Pro live, PrimeSense Carmine and Leap Motion. Their main advantages are their non-intrusive nature, low cost and widely available support for developers offered by large corporations or Open Communities. Although they were originally developed for computer games, they have inspired numerous healthcare related ideas and projects in areas such as Medical Disorder Diagnosis, Assisted Living, Rehabilitation and Surgery.
In Assisted Living, human motion analysis allows continuous monitoring of elderly and vulnerable people and their activities to potentially detect life-threatening events such as falls. Human motion analysis in rehabilitation provides the opportunity for motivating patients through gamification, evaluating prescribed programmes of exercises and assessing patients’ progress. In operating theatres, surgeons may use a gesture-based interface to access medical information or control a tele-surgery system. Human motion analysis may also be used to diagnose a range of mental and physical diseases and conditions.
This event will discuss recent advances in human motion sensing and provide an application to healthcare for networking and exploring potential synergies and collaborations.
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Jose Ramirez Moreno, Juan R Revilla, Miguel Reyes, & Sergio Escalera. (2016). Validación del Software ADIBAS asociado al sensor Kinect de Microsoft para la evaluación de la posición corporal. In 4th Congreso WCPT-SAR.
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Marc Oliu, Ciprian Corneanu, Kamal Nasrollahi, Olegs Nikisins, Sergio Escalera, Yunlian Sun, et al. (2016). Improved RGB-D-T based Face Recognition. BIO - IET Biometrics, 5(4), 297–303.
Abstract: Reliable facial recognition systems are of crucial importance in various applications from entertainment to security. Thanks to the deep-learning concepts introduced in the field, a significant improvement in the performance of the unimodal facial recognition systems has been observed in the recent years. At the same time a multimodal facial recognition is a promising approach. This study combines the latest successes in both directions by applying deep learning convolutional neural networks (CNN) to the multimodal RGB, depth, and thermal (RGB-D-T) based facial recognition problem outperforming previously published results. Furthermore, a late fusion of the CNN-based recognition block with various hand-crafted features (local binary patterns, histograms of oriented gradients, Haar-like rectangular features, histograms of Gabor ordinal measures) is introduced, demonstrating even better recognition performance on a benchmark RGB-D-T database. The obtained results in this study show that the classical engineered features and CNN-based features can complement each other for recognition purposes.
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Fernando Alonso, Xavier Baro, Sergio Escalera, Jordi Gonzalez, Martha Mackay, & Anna Serrahima. (2016). CARE RESPITE: TAKING CARE OF THE CAREGIVERS, Theme 5 The Strategic use of Mobile and Digital Health and Care Solutions. In 16th International Conference for Integrated Care.
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Antonio Esteban Lansaque, Carles Sanchez, Agnes Borras, Marta Diez-Ferrer, Antoni Rosell, & Debora Gil. (2016). Stable Anatomical Structure Tracking for video-bronchoscopy Navigation. In 19th International Conference on Medical Image Computing and Computer Assisted Intervention Workshops.
Abstract: Bronchoscopy allows to examine the patient airways for detection of lesions and sampling of tissues without surgery. A main drawback in lung cancer diagnosis is the diculty to check whether the exploration is following the correct path to the nodule that has to be biopsied. The most extended guidance uses uoroscopy which implies repeated radiation of clinical sta and patients. Alternatives such as virtual bronchoscopy or electromagnetic navigation are very expensive and not completely robust to blood, mocus or deformations as to be extensively used. We propose a method that extracts and tracks stable lumen regions at di erent levels of the bronchial tree. The tracked regions are stored in a tree that encodes the anatomical structure of the scene which can be useful to retrieve the path to the lesion that the clinician should follow to do the biopsy. We present a multi-expert validation of our anatomical landmark extraction in 3 intra-operative ultrathin explorations.
Keywords: Lung cancer diagnosis; video-bronchoscopy; airway lumen detection; region tracking
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German Ros. (2016). Visual Scene Understanding for Autonomous Vehicles: Understanding Where and What (Angel Sappa, Julio Guerrero, & Antonio Lopez, Eds.). Ph.D. thesis, Ediciones Graficas Rey, .
Abstract: Making Ground Autonomous Vehicles (GAVs) a reality as a service for the society is one of the major scientific and technological challenges of this century. The potential benefits of autonomous vehicles include reducing accidents, improving traffic congestion and better usage of road infrastructures, among others. These vehicles must operate in our cities, towns and highways, dealing with many different types of situations while respecting traffic rules and protecting human lives. GAVs are expected to deal with all types of scenarios and situations, coping with an uncertain and chaotic world.
Therefore, in order to fulfill these demanding requirements GAVs need to be endowed with the capability of understanding their surrounding at many different levels, by means of affordable sensors and artificial intelligence. This capacity to understand the surroundings and the current situation that the vehicle is involved in is called scene understanding. In this work we investigate novel techniques to bring scene understanding to autonomous vehicles by combining the use of cameras as the main source of information—due to their versatility and affordability—and algorithms based on computer vision and machine learning. We investigate different degrees of understanding of the scene, starting from basic geometric knowledge about where is the vehicle within the scene. A robust and efficient estimation of the vehicle location and pose with respect to a map is one of the most fundamental steps towards autonomous driving. We study this problem from the point of view of robustness and computational efficiency, proposing key insights to improve current solutions. Then we advance to higher levels of abstraction to discover what is in the scene, by recognizing and parsing all the elements present on a driving scene, such as roads, sidewalks, pedestrians, etc. We investigate this problem known as semantic segmentation, proposing new approaches to improve recognition accuracy and computational efficiency. We cover these points by focusing on key aspects such as: (i) how to leverage computation moving semantics to an offline process, (ii) how to train compact architectures based on deconvolutional networks to achieve their maximum potential, (iii) how to use virtual worlds in combination with domain adaptation to produce accurate models in a cost-effective fashion, and (iv) how to use transfer learning techniques to prepare models to new situations. We finally extend the previous level of knowledge enabling systems to reasoning about what has change in a scene with respect to a previous visit, which in return allows for efficient and cost-effective map updating.
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Francisco Cruz. (2016). Probabilistic Graphical Models for Document Analysis (Oriol Ramos Terrades, Ed.). Ph.D. thesis, Ediciones Graficas Rey, .
Abstract: Latest advances in digitization techniques have fostered the interest in creating digital copies of collections of documents. Digitized documents permit an easy maintenance, loss-less storage, and efficient ways for transmission and to perform information retrieval processes. This situation has opened a new market niche to develop systems able to automatically extract and analyze information contained in these collections, specially in the ambit of the business activity.
Due to the great variety of types of documents this is not a trivial task. For instance, the automatic extraction of numerical data from invoices differs substantially from a task of text recognition in historical documents. However, in order to extract the information of interest, is always necessary to identify the area of the document where it is located. In the area of Document Analysis we refer to this process as layout analysis, which aims at identifying and categorizing the different entities that compose the document, such as text regions, pictures, text lines, or tables, among others. To perform this task it is usually necessary to incorporate a prior knowledge about the task into the analysis process, which can be modeled by defining a set of contextual relations between the different entities of the document. The use of context has proven to be useful to reinforce the recognition process and improve the results on many computer vision tasks. It presents two fundamental questions: What kind of contextual information is appropriate for a given task, and how to incorporate this information into the models.
In this thesis we study several ways to incorporate contextual information to the task of document layout analysis, and to the particular case of handwritten text line segmentation. We focus on the study of Probabilistic Graphical Models and other mechanisms for this purpose, and propose several solutions to these problems. First, we present a method for layout analysis based on Conditional Random Fields. With this model we encode local contextual relations between variables, such as pair-wise constraints. Besides, we encode a set of structural relations between different classes of regions at feature level. Second, we present a method based on 2D-Probabilistic Context-free Grammars to encode structural and hierarchical relations. We perform a comparative study between Probabilistic Graphical Models and this syntactic approach. Third, we propose a method for structured documents based on Bayesian Networks to represent the document structure, and an algorithm based in the Expectation-Maximization to find the best configuration of the page. We perform a thorough evaluation of the proposed methods on two particular collections of documents: a historical collection composed of ancient structured documents, and a collection of contemporary documents. In addition, we present a general method for the task of handwritten text line segmentation. We define a probabilistic framework where we combine the EM algorithm with variational approaches for computing inference and parameter learning on a Markov Random Field. We evaluate our method on several collections of documents, including a general dataset of annotated administrative documents. Results demonstrate the applicability of our method to real problems, and the contribution of the use of contextual information to this kind of problems.
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Lluis Gomez, & Dimosthenis Karatzas. (2016). A fast hierarchical method for multi‐script and arbitrary oriented scene text extraction. IJDAR - International Journal on Document Analysis and Recognition, 19(4), 335–349.
Abstract: Typography and layout lead to the hierarchical organisation of text in words, text lines, paragraphs. This inherent structure is a key property of text in any script and language, which has nonetheless been minimally leveraged by existing text detection methods. This paper addresses the problem of text
segmentation in natural scenes from a hierarchical perspective.
Contrary to existing methods, we make explicit use of text structure, aiming directly to the detection of region groupings corresponding to text within a hierarchy produced by an agglomerative similarity clustering process over individual regions. We propose an optimal way to construct such an hierarchy introducing a feature space designed to produce text group hypotheses with
high recall and a novel stopping rule combining a discriminative classifier and a probabilistic measure of group meaningfulness based in perceptual organization. Results obtained over four standard datasets, covering text in variable orientations and different languages, demonstrate that our algorithm, while being trained in a single mixed dataset, outperforms state of the art
methods in unconstrained scenarios.
Keywords: scene text; segmentation; detection; hierarchical grouping; perceptual organisation
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Lluis Gomez, & Dimosthenis Karatzas. (2016). A fine-grained approach to scene text script identification. In 12th IAPR Workshop on Document Analysis Systems (pp. 192–197).
Abstract: This paper focuses on the problem of script identification in unconstrained scenarios. Script identification is an important prerequisite to recognition, and an indispensable condition for automatic text understanding systems designed for multi-language environments. Although widely studied for document images and handwritten documents, it remains an almost unexplored territory for scene text images. We detail a novel method for script identification in natural images that combines convolutional features and the Naive-Bayes Nearest Neighbor classifier. The proposed framework efficiently exploits the discriminative power of small stroke-parts, in a fine-grained classification framework. In addition, we propose a new public benchmark dataset for the evaluation of joint text detection and script identification in natural scenes. Experiments done in this new dataset demonstrate that the proposed method yields state of the art results, while it generalizes well to different datasets and variable number of scripts. The evidence provided shows that multi-lingual scene text recognition in the wild is a viable proposition. Source code of the proposed method is made available online.
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Arash Akbarinia, & C. Alejandro Parraga. (2016). Biologically plausible boundary detection. In 27th British Machine Vision Conference.
Abstract: Edges are key components of any visual scene to the extent that we can recognise objects merely by their silhouettes. The human visual system captures edge information through neurons in the visual cortex that are sensitive to both intensity discontinuities and particular orientations. The “classical approach” assumes that these cells are only responsive to the stimulus present within their receptive fields, however, recent studies demonstrate that surrounding regions and inter-areal feedback connections influence their responses significantly. In this work we propose a biologically-inspired edge detection model in which orientation selective neurons are represented through the first derivative of a Gaussian function resembling double-opponent cells in the primary visual cortex (V1). In our model we account for four kinds of surround, i.e. full, far, iso- and orthogonal-orientation, whose contributions are contrast-dependant. The output signal from V1 is pooled in its perpendicular direction by larger V2 neurons employing a contrast-variant centre-surround kernel. We further introduce a feedback connection from higher-level visual areas to the lower ones. The results of our model on two benchmark datasets show a big improvement compared to the current non-learning and biologically-inspired state-of-the-art algorithms while being competitive to the learning-based methods.
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Azadeh S. Mozafari, David Vazquez, Mansour Jamzad, & Antonio Lopez. (2016). Node-Adapt, Path-Adapt and Tree-Adapt:Model-Transfer Domain Adaptation for Random Forest.
Abstract: Random Forest (RF) is a successful paradigm for learning classifiers due to its ability to learn from large feature spaces and seamlessly integrate multi-class classification, as well as the achieved accuracy and processing efficiency. However, as many other classifiers, RF requires domain adaptation (DA) provided that there is a mismatch between the training (source) and testing (target) domains which provokes classification degradation. Consequently, different RF-DA methods have been proposed, which not only require target-domain samples but revisiting the source-domain ones, too. As novelty, we propose three inherently different methods (Node-Adapt, Path-Adapt and Tree-Adapt) that only require the learned source-domain RF and a relatively few target-domain samples for DA, i.e. source-domain samples do not need to be available. To assess the performance of our proposals we focus on image-based object detection, using the pedestrian detection problem as challenging proof-of-concept. Moreover, we use the RF with expert nodes because it is a competitive patch-based pedestrian model. We test our Node-, Path- and Tree-Adapt methods in standard benchmarks, showing that DA is largely achieved.
Keywords: Domain Adaptation; Pedestrian detection; Random Forest
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Youssef El Rhabi, Simon Loic, Brun Luc, Josep Llados, & Felipe Lumbreras. (2016). Information Theoretic Rotationwise Robust Binary Descriptor Learning. In Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR) (pp. 368–378).
Abstract: In this paper, we propose a new data-driven approach for binary descriptor selection. In order to draw a clear analysis of common designs, we present a general information-theoretic selection paradigm. It encompasses several standard binary descriptor construction schemes, including a recent state-of-the-art one named BOLD. We pursue the same endeavor to increase the stability of the produced descriptors with respect to rotations. To achieve this goal, we have designed a novel offline selection criterion which is better adapted to the online matching procedure. The effectiveness of our approach is demonstrated on two standard datasets, where our descriptor is compared to BOLD and to several classical descriptors. In particular, it emerges that our approach can reproduce equivalent if not better performance as BOLD while relying on twice shorter descriptors. Such an improvement can be influential for real-time applications.
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