Ariel Amato, Felipe Lumbreras, & Angel Sappa. (2014). A General-purpose Crowdsourcing Platform for Mobile Devices. In 9th International Conference on Computer Vision Theory and Applications (Vol. 3, pp. 211–215).
Abstract: This paper presents details of a general purpose micro-task on-demand platform based on the crowdsourcing philosophy. This platform was specifically developed for mobile devices in order to exploit the strengths of such devices; namely: i) massivity, ii) ubiquity and iii) embedded sensors. The combined use of mobile platforms and the crowdsourcing model allows to tackle from the simplest to the most complex tasks. Users experience is the highlighted feature of this platform (this fact is extended to both task-proposer and tasksolver). Proper tools according with a specific task are provided to a task-solver in order to perform his/her job in a simpler, faster and appealing way. Moreover, a task can be easily submitted by just selecting predefined templates, which cover a wide range of possible applications. Examples of its usage in computer vision and computer games are provided illustrating the potentiality of the platform.
Keywords: Crowdsourcing Platform; Mobile Crowdsourcing
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Christophe Rigaud, Dimosthenis Karatzas, Jean-Christophe Burie, & Jean-Marc Ogier. (2014). Color descriptor for content-based drawing retrieval. In 11th IAPR International Workshop on Document Analysis and Systems (pp. 267–271).
Abstract: Human detection in computer vision field is an active field of research. Extending this to human-like drawings such as the main characters in comic book stories is not trivial. Comics analysis is a very recent field of research at the intersection of graphics, texts, objects and people recognition. The detection of the main comic characters is an essential step towards a fully automatic comic book understanding. This paper presents a color-based approach for comics character retrieval using content-based drawing retrieval and color palette.
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Clement Guerin, Christophe Rigaud, Karell Bertet, Jean-Christophe Burie, Arnaud Revel, & Jean-Marc Ogier. (2014). Réduction de l’espace de recherche pour les personnages de bandes dessinées. In 19th National Congress Reconnaissance de Formes et l'Intelligence Artificielle.
Abstract: Les bandes dessinées représentent un patrimoine culturel important dans de nombreux pays et leur numérisation massive offre la possibilité d'effectuer des recherches dans le contenu des images. À ce jour, ce sont principalement les structures des pages et leurs contenus textuels qui ont été étudiés, peu de travaux portent sur le contenu graphique. Nous proposons de nous appuyer sur des éléments déjà étudiés tels que la position des cases et des bulles, pour réduire l'espace de recherche et localiser les personnages en fonction de la queue des bulles. L'évaluation de nos différentes contributions à partir de la base eBDtheque montre un taux de détection des queues de bulle de 81.2%, de localisation des personnages allant jusqu'à 85% et un gain d'espace de recherche de plus de 50%.
Keywords: contextual search; document analysis; comics characters
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Christophe Rigaud, & Clement Guerin. (2014). Localisation contextuelle des personnages de bandes dessinées. In Colloque International Francophone sur l'Écrit et le Document.
Abstract: Les auteurs proposent une méthode de localisation des personnages dans des cases de bandes dessinées en s'appuyant sur les caractéristiques des bulles de dialogue. L'évaluation montre un taux de localisation des personnages allant jusqu'à 65%.
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Jon Almazan, Albert Gordo, Alicia Fornes, & Ernest Valveny. (2014). Word Spotting and Recognition with Embedded Attributes. TPAMI - IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(12), 2552–2566.
Abstract: This article addresses the problems of word spotting and word recognition on images. In word spotting, the goal is to find all instances of a query word in a dataset of images. In recognition, the goal is to recognize the content of the word image, usually aided by a dictionary or lexicon. We describe an approach in which both word images and text strings are embedded in a common vectorial subspace. This is achieved by a combination of label embedding and attributes learning, and a common subspace regression. In this subspace, images and strings that represent the same word are close together, allowing one to cast recognition and retrieval tasks as a nearest neighbor problem. Contrary to most other existing methods, our representation has a fixed length, is low dimensional, and is very fast to compute and, especially, to compare. We test our approach on four public datasets of both handwritten documents and natural images showing results comparable or better than the state-of-the-art on spotting and recognition tasks.
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Jon Almazan, Albert Gordo, Alicia Fornes, & Ernest Valveny. (2014). Segmentation-free Word Spotting with Exemplar SVMs. PR - Pattern Recognition, 47(12), 3967–3978.
Abstract: In this paper we propose an unsupervised segmentation-free method for word spotting in document images. Documents are represented with a grid of HOG descriptors, and a sliding-window approach is used to locate the document regions that are most similar to the query. We use the Exemplar SVM framework to produce a better representation of the query in an unsupervised way. Then, we use a more discriminative representation based on Fisher Vector to rerank the best regions retrieved, and the most promising ones are used to expand the Exemplar SVM training set and improve the query representation. Finally, the document descriptors are precomputed and compressed with Product Quantization. This offers two advantages: first, a large number of documents can be kept in RAM memory at the same time. Second, the sliding window becomes significantly faster since distances between quantized HOG descriptors can be precomputed. Our results significantly outperform other segmentation-free methods in the literature, both in accuracy and in speed and memory usage.
Keywords: Word spotting; Segmentation-free; Unsupervised learning; Reranking; Query expansion; Compression
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Carlo Gatta, Adriana Romero, & Joost Van de Weijer. (2014). Unrolling loopy top-down semantic feedback in convolutional deep networks. In Workshop on Deep Vision: Deep Learning for Computer Vision (pp. 498–505).
Abstract: In this paper, we propose a novel way to perform top-down semantic feedback in convolutional deep networks for efficient and accurate image parsing. We also show how to add global appearance/semantic features, which have shown to improve image parsing performance in state-of-the-art methods, and was not present in previous convolutional approaches. The proposed method is characterised by an efficient training and a sufficiently fast testing. We use the well known SIFTflow dataset to numerically show the advantages provided by our contributions, and to compare with state-of-the-art image parsing convolutional based approaches.
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Dimosthenis Karatzas, Sergi Robles, & Lluis Gomez. (2014). An on-line platform for ground truthing and performance evaluation of text extraction systems. In 11th IAPR International Workshop on Document Analysis and Systems (pp. 242–246).
Abstract: This paper presents a set of on-line software tools for creating ground truth and calculating performance evaluation metrics for text extraction tasks such as localization, segmentation and recognition. The platform supports the definition of comprehensive ground truth information at different text representation levels while it offers centralised management and quality control of the ground truthing effort. It implements a range of state of the art performance evaluation algorithms and offers functionality for the definition of evaluation scenarios, on-line calculation of various performance metrics and visualisation of the results. The
presented platform, which comprises the backbone of the ICDAR 2011 (challenge 1) and 2013 (challenges 1 and 2) Robust Reading competitions, is now made available for public use.
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Lluis Gomez, & Dimosthenis Karatzas. (2014). MSER-based Real-Time Text Detection and Tracking. In 22nd International Conference on Pattern Recognition (pp. 3110–3115).
Abstract: We present a hybrid algorithm for detection and tracking of text in natural scenes that goes beyond the fulldetection approaches in terms of time performance optimization.
A state-of-the-art scene text detection module based on Maximally Stable Extremal Regions (MSER) is used to detect text asynchronously, while on a separate thread detected text objects are tracked by MSER propagation. The cooperation of these two modules yields real time video processing at high frame rates even on low-resource devices.
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Alejandro Tabas, Emili Balaguer-Ballester, & Laura Igual. (2014). Spatial Discriminant ICA for RS-fMRI characterisation. In 4th International Workshop on Pattern Recognition in Neuroimaging (pp. 1–4).
Abstract: Resting-State fMRI (RS-fMRI) is a brain imaging technique useful for exploring functional connectivity. A major point of interest in RS-fMRI analysis is to isolate connectivity patterns characterising disorders such as for instance ADHD. Such characterisation is usually performed in two steps: first, all connectivity patterns in the data are extracted by means of Independent Component Analysis (ICA); second, standard statistical tests are performed over the extracted patterns to find differences between control and clinical groups. In this work we introduce a novel, single-step, approach for this problem termed Spatial Discriminant ICA. The algorithm can efficiently isolate networks of functional connectivity characterising a clinical group by combining ICA and a new variant of the Fisher’s Linear Discriminant also introduced in this work. As the characterisation is carried out in a single step, it potentially provides for a richer characterisation of inter-class differences. The algorithm is tested using synthetic and real fMRI data, showing promising results in both experiments.
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Oualid M. Benkarim, Petia Radeva, & Laura Igual. (2014). Label Consistent Multiclass Discriminative Dictionary Learning for MRI Segmentation. In 8th Conference on Articulated Motion and Deformable Objects (Vol. 8563, pp. 138–147). LNCS. Springer International Publishing.
Abstract: The automatic segmentation of multiple subcortical structures in brain Magnetic Resonance Images (MRI) still remains a challenging task. In this paper, we address this problem using sparse representation and discriminative dictionary learning, which have shown promising results in compression, image denoising and recently in MRI segmentation. Particularly, we use multiclass dictionaries learned from a set of brain atlases to simultaneously segment multiple subcortical structures.
We also impose dictionary atoms to be specialized in one given class using label consistent K-SVD, which can alleviate the bias produced by unbalanced libraries, present when dealing with small structures. The proposed method is compared with other state of the art approaches for the segmentation of the Basal Ganglia of 35 subjects of a public dataset.
The promising results of the segmentation method show the eciency of the multiclass discriminative dictionary learning algorithms in MRI segmentation problems.
Keywords: MRI segmentation; sparse representation; discriminative dic- tionary learning; multiclass classi cation
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Patricia Marquez, H. Kause, A. Fuster, Aura Hernandez-Sabate, L. Florack, Debora Gil, et al. (2014). Factors Affecting Optical Flow Performance in Tagging Magnetic Resonance Imaging. In 17th International Conference on Medical Image Computing and Computer Assisted Intervention (Vol. 8896, pp. 231–238). LNCS. Springer International Publishing.
Abstract: Changes in cardiac deformation patterns are correlated with cardiac pathologies. Deformation can be extracted from tagging Magnetic Resonance Imaging (tMRI) using Optical Flow (OF) techniques. For applications of OF in a clinical setting it is important to assess to what extent the performance of a particular OF method is stable across di erent clinical acquisition artifacts. This paper presents a statistical validation framework, based on ANOVA, to assess the motion and appearance factors that have the largest in uence on OF accuracy drop.
In order to validate this framework, we created a database of simulated tMRI data including the most common artifacts of MRI and test three di erent OF methods, including HARP.
Keywords: Optical flow; Performance Evaluation; Synthetic Database; ANOVA; Tagging Magnetic Resonance Imaging
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Hongxing Gao, Marçal Rusiñol, Dimosthenis Karatzas, & Josep Llados. (2014). Fast Structural Matching for Document Image Retrieval through Spatial Databases. In Document Recognition and Retrieval XXI (Vol. 9021).
Abstract: The structure of document images plays a signi cant role in document analysis thus considerable e orts have been made towards extracting and understanding document structure, usually in the form of layout analysis approaches. In this paper, we rst employ Distance Transform based MSER (DTMSER) to eciently extract stable document structural elements in terms of a dendrogram of key-regions. Then a fast structural matching method is proposed to query the structure of document (dendrogram) based on a spatial database which facilitates the formulation of advanced spatial queries. The experiments demonstrate a signi cant improvement in a document retrieval scenario when compared to the use of typical Bag of Words (BoW) and pyramidal BoW descriptors.
Keywords: Document image retrieval; distance transform; MSER; spatial database
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Hongxing Gao, Marçal Rusiñol, Dimosthenis Karatzas, & Josep Llados. (2014). Embedding Document Structure to Bag-of-Words through Pair-wise Stable Key-regions. In 22nd International Conference on Pattern Recognition (pp. 2903–2908).
Abstract: Since the document structure carries valuable discriminative information, plenty of efforts have been made for extracting and understanding document structure among which layout analysis approaches are the most commonly used. In this paper, Distance Transform based MSER (DTMSER) is employed to efficiently extract the document structure as a dendrogram of key-regions which roughly correspond to structural elements such as characters, words and paragraphs. Inspired by the Bag
of Words (BoW) framework, we propose an efficient method for structural document matching by representing the document image as a histogram of key-region pairs encoding structural relationships.
Applied to the scenario of document image retrieval, experimental results demonstrate a remarkable improvement when comparing the proposed method with typical BoW and pyramidal BoW methods.
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Victor Ponce, Sergio Escalera, Marc Perez, Oriol Janes, & Xavier Baro. (2015). Non-Verbal Communication Analysis in Victim-Offender Mediations. PRL - Pattern Recognition Letters, 67(1), 19–27.
Abstract: We present a non-invasive ambient intelligence framework for the semi-automatic analysis of non-verbal communication applied to the restorative justice field. We propose the use of computer vision and social signal processing technologies in real scenarios of Victim–Offender Mediations, applying feature extraction techniques to multi-modal audio-RGB-depth data. We compute a set of behavioral indicators that define communicative cues from the fields of psychology and observational methodology. We test our methodology on data captured in real Victim–Offender Mediation sessions in Catalonia. We define the ground truth based on expert opinions when annotating the observed social responses. Using different state of the art binary classification approaches, our system achieves recognition accuracies of 86% when predicting satisfaction, and 79% when predicting both agreement and receptivity. Applying a regression strategy, we obtain a mean deviation for the predictions between 0.5 and 0.7 in the range [1–5] for the computed social signals.
Keywords: Victim–Offender Mediation; Multi-modal human behavior analysis; Face and gesture recognition; Social signal processing; Computer vision; Machine learning
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