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Juan Ignacio Toledo, Alicia Fornes, Jordi Cucurull, & Josep Llados. (2016). Election Tally Sheets Processing System. In 12th IAPR Workshop on Document Analysis Systems (pp. 364–368).
Abstract: In paper based elections, manual tallies at polling station level produce myriads of documents. These documents share a common form-like structure and a reduced vocabulary worldwide. On the other hand, each tally sheet is filled by a different writer and on different countries, different scripts are used. We present a complete document analysis system for electoral tally sheet processing combining state of the art techniques with a new handwriting recognition subprocess based on unsupervised feature discovery with Variational Autoencoders and sequence classification with BLSTM neural networks. The whole system is designed to be script independent and allows a fast and reliable results consolidation process with reduced operational cost.
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Anders Hast, & Alicia Fornes. (2016). A Segmentation-free Handwritten Word Spotting Approach by Relaxed Feature Matching. In 12th IAPR Workshop on Document Analysis Systems (pp. 150–155).
Abstract: The automatic recognition of historical handwritten documents is still considered challenging task. For this reason, word spotting emerges as a good alternative for making the information contained in these documents available to the user. Word spotting is defined as the task of retrieving all instances of the query word in a document collection, becoming a useful tool for information retrieval. In this paper we propose a segmentation-free word spotting approach able to deal with large document collections. Our method is inspired on feature matching algorithms that have been applied to image matching and retrieval. Since handwritten words have different shape, there is no exact transformation to be obtained. However, the sufficient degree of relaxation is achieved by using a Fourier based descriptor and an alternative approach to RANSAC called PUMA. The proposed approach is evaluated on historical marriage records, achieving promising results.
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Dimosthenis Karatzas, V. Poulain d'Andecy, & Marçal Rusiñol. (2016). Human-Document Interaction – a new frontier for document image analysis. In 12th IAPR Workshop on Document Analysis Systems (pp. 369–374).
Abstract: All indications show that paper documents will not cede in favour of their digital counterparts, but will instead be used increasingly in conjunction with digital information. An open challenge is how to seamlessly link the physical with the digital – how to continue taking advantage of the important affordances of paper, without missing out on digital functionality. This paper
presents the authors’ experience with developing systems for Human-Document Interaction based on augmented document interfaces and examines new challenges and opportunities arising for the document image analysis field in this area. The system presented combines state of the art camera-based document
image analysis techniques with a range of complementary tech-nologies to offer fluid Human-Document Interaction. Both fixed and nomadic setups are discussed that have gone through user testing in real-life environments, and use cases are presented that span the spectrum from business to educational application
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Q. Bao, Marçal Rusiñol, M.Coustaty, Muhammad Muzzamil Luqman, C.D. Tran, & Jean-Marc Ogier. (2016). Delaunay triangulation-based features for Camera-based document image retrieval system. In 12th IAPR Workshop on Document Analysis Systems (pp. 1–6).
Abstract: In this paper, we propose a new feature vector, named DElaunay TRIangulation-based Features (DETRIF), for real-time camera-based document image retrieval. DETRIF is computed based on the geometrical constraints from each pair of adjacency triangles in delaunay triangulation which is constructed from centroids of connected components. Besides, we employ a hashing-based indexing system in order to evaluate the performance of DETRIF and to compare it with other systems such as LLAH and SRIF. The experimentation is carried out on two datasets comprising of 400 heterogeneous-content complex linguistic map images (huge size, 9800 X 11768 pixels resolution)and 700 textual document images.
Keywords: Camera-based Document Image Retrieval; Delaunay Triangulation; Feature descriptors; Indexing
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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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Albert Berenguel, Oriol Ramos Terrades, Josep Llados, & Cristina Cañero. (2016). Banknote counterfeit detection through background texture printing analysis. In 12th IAPR Workshop on Document Analysis Systems.
Abstract: This paper is focused on the detection of counterfeit photocopy banknotes. The main difficulty is to work on a real industrial scenario without any constraint about the acquisition device and with a single image. The main contributions of this paper are twofold: first the adaptation and performance evaluation of existing approaches to classify the genuine and photocopy banknotes using background texture printing analysis, which have not been applied into this context before. Second, a new dataset of Euro banknotes images acquired with several cameras under different luminance conditions to evaluate these methods. Experiments on the proposed algorithms show that mixing SIFT features and sparse coding dictionaries achieves quasi perfect classification using a linear SVM with the created dataset. Approaches using dictionaries to cover all possible texture variations have demonstrated to be robust and outperform the state-of-the-art methods using the proposed benchmark.
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Julio C. S. Jacques Junior, Xavier Baro, & Sergio Escalera. (2017). Exploiting feature representations through similarity learning and ranking aggregation for person re-identification. In 12th IEEE International Conference on Automatic Face and Gesture Recognition.
Abstract: Person re-identification has received special attentionby the human analysis community in the last few years.To address the challenges in this field, many researchers haveproposed different strategies, which basically exploit eithercross-view invariant features or cross-view robust metrics. Inthis work we propose to combine different feature representationsthrough ranking aggregation. Spatial information, whichpotentially benefits the person matching, is represented usinga 2D body model, from which color and texture informationare extracted and combined. We also consider contextualinformation (background and foreground data), automaticallyextracted via Deep Decompositional Network, and the usage ofConvolutional Neural Network (CNN) features. To describe thematching between images we use the polynomial feature map,also taking into account local and global information. Finally,the Stuart ranking aggregation method is employed to combinecomplementary ranking lists obtained from different featurerepresentations. Experimental results demonstrated that weimprove the state-of-the-art on VIPeR and PRID450s datasets,achieving 58.77% and 71.56% on top-1 rank recognitionrate, respectively, as well as obtaining competitive results onCUHK01 dataset.
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Iiris Lusi, Julio C. S. Jacques Junior, Jelena Gorbova, Xavier Baro, Sergio Escalera, Hasan Demirel, et al. (2017). Joint Challenge on Dominant and Complementary Emotion Recognition Using Micro Emotion Features and Head-Pose Estimation: Databases. In 12th IEEE International Conference on Automatic Face and Gesture Recognition.
Abstract: In this work two databases for the Joint Challenge on Dominant and Complementary Emotion Recognition Using Micro Emotion Features and Head-Pose Estimation1 are introduced. Head pose estimation paired with and detailed emotion recognition have become very important in relation to human-computer interaction. The 3D head pose database, SASE, is a 3D database acquired with Microsoft Kinect 2 camera, including RGB and depth information of different head poses which is composed by a total of 30000 frames with annotated markers, including 32 male and 18 female subjects. For the dominant and complementary emotion database, iCVMEFED, includes 31250 images with different emotions of 115 subjects whose gender distribution is almost uniform. For each subject there are 5 samples. The emotions are composed by 7 basic emotions plus neutral, being defined as complementary and dominant pairs. The emotion associated to the images were labeled with the support of psychologists.
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Chirster Loob, Pejman Rasti, Iiris Lusi, Julio C. S. Jacques Junior, Xavier Baro, Sergio Escalera, et al. (2017). Dominant and Complementary Multi-Emotional Facial Expression Recognition Using C-Support Vector Classification. In 12th IEEE International Conference on Automatic Face and Gesture Recognition.
Abstract: We are proposing a new facial expression recognition model which introduces 30+ detailed facial expressions recognisable by any artificial intelligence interacting with a human. Throughout this research, we introduce two categories for the emotions, namely, dominant emotions and complementary emotions. In this research paper the complementary emotion is recognised by using the eye region if the dominant emotion is angry, fearful or sad, and if the dominant emotion is disgust or happiness the complementary emotion is mainly conveyed by the mouth. In order to verify the tagged dominant and complementary emotions, randomly chosen people voted for the recognised multi-emotional facial expressions. The average results of voting are showing that 73.88% of the voters agree on the correctness of the recognised multi-emotional facial expressions.
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Meysam Madadi, Sergio Escalera, Alex Carruesco, Carlos Andujar, Xavier Baro, & Jordi Gonzalez. (2017). Occlusion Aware Hand Pose Recovery from Sequences of Depth Images. In 12th IEEE International Conference on Automatic Face and Gesture Recognition.
Abstract: State-of-the-art approaches on hand pose estimation from depth images have reported promising results under quite controlled considerations. In this paper we propose a two-step pipeline for recovering the hand pose from a sequence of depth images. The pipeline has been designed to deal with images taken from any viewpoint and exhibiting a high degree of finger occlusion. In a first step we initialize the hand pose using a part-based model, fitting a set of hand components in the depth images. In a second step we consider temporal data and estimate the parameters of a trained bilinear model consisting of shape and trajectory bases. Results on a synthetic, highly-occluded dataset demonstrate that the proposed method outperforms most recent pose recovering approaches, including those based on CNNs.
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Maryam Asadi-Aghbolaghi, Albert Clapes, Marco Bellantonio, Hugo Jair Escalante, Victor Ponce, Xavier Baro, et al. (2017). A survey on deep learning based approaches for action and gesture recognition in image sequences. In 12th IEEE International Conference on Automatic Face and Gesture Recognition.
Abstract: The interest in action and gesture recognition has grown considerably in the last years. In this paper, we present a survey on current deep learning methodologies for action and gesture recognition in image sequences. We introduce a taxonomy that summarizes important aspects of deep learning
for approaching both tasks. We review the details of the proposed architectures, fusion strategies, main datasets, and competitions.
We summarize and discuss the main works proposed so far with particular interest on how they treat the temporal dimension of data, discussing their main features and identify opportunities and challenges for future research.
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Eirikur Agustsson, Radu Timofte, Sergio Escalera, Xavier Baro, Isabelle Guyon, & Rasmus Rothe. (2017). Apparent and real age estimation in still images with deep residual regressors on APPA-REAL database. In 12th IEEE International Conference on Automatic Face and Gesture Recognition.
Abstract: After decades of research, the real (biological) age estimation from a single face image reached maturity thanks to the availability of large public face databases and impressive accuracies achieved by recently proposed methods.
The estimation of “apparent age” is a related task concerning the age perceived by human observers. Significant advances have been also made in this new research direction with the recent Looking At People challenges. In this paper we make several contributions to age estimation research. (i) We introduce APPA-REAL, a large face image database with both real and apparent age annotations. (ii) We study the relationship between real and apparent age. (iii) We develop a residual age regression method to further improve the performance. (iv) We show that real age estimation can be successfully tackled as an apparent age estimation followed by an apparent to real age residual regression. (v) We graphically reveal the facial regions on which the CNN focuses in order to perform apparent and real age estimation tasks.
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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, 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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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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