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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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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German Ros, Sebastian Ramos, Manuel Granados, Amir Bakhtiary, David Vazquez, & Antonio Lopez. (2015). Vision-based Offline-Online Perception Paradigm for Autonomous Driving. In IEEE Winter Conference on Applications of Computer Vision (pp. 231–238).
Abstract: Autonomous driving is a key factor for future mobility. Properly perceiving the environment of the vehicles is essential for a safe driving, which requires computing accurate geometric and semantic information in real-time. In this paper, we challenge state-of-the-art computer vision algorithms for building a perception system for autonomous driving. An inherent drawback in the computation of visual semantics is the trade-off between accuracy and computational cost. We propose to circumvent this problem by following an offline-online strategy. During the offline stage dense 3D semantic maps are created. In the online stage the current driving area is recognized in the maps via a re-localization process, which allows to retrieve the pre-computed accurate semantics and 3D geometry in realtime. Then, detecting the dynamic obstacles we obtain a rich understanding of the current scene. We evaluate quantitatively our proposal in the KITTI dataset and discuss the related open challenges for the computer vision community.
Keywords: Autonomous Driving; Scene Understanding; SLAM; Semantic Segmentation
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Jorge Bernal, Debora Gil, Carles Sanchez, & F. Javier Sanchez. (2014). Discarding Non Informative Regions for Efficient Colonoscopy Image Analysis. In 1st MICCAI Workshop on Computer-Assisted and Robotic Endoscopy (Vol. 8899, pp. 1–10). LNCS. Springer International Publishing.
Abstract: In this paper we present a novel polyp region segmentation method for colonoscopy videos. Our method uses valley information associated to polyp boundaries in order to provide an initial segmentation. This first segmentation is refined to eliminate boundary discontinuities caused by image artifacts or other elements of the scene. Experimental results over a publicly annotated database show that our method outperforms both general and specific segmentation methods by providing more accurate regions rich in polyp content. We also prove how image preprocessing is needed to improve final polyp region segmentation.
Keywords: Image Segmentation; Polyps, Colonoscopy; Valley Information; Energy Maps
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Joan M. Nuñez, Jorge Bernal, Miquel Ferrer, & Fernando Vilariño. (2014). Impact of Keypoint Detection on Graph-based Characterization of Blood Vessels in Colonoscopy Videos. In CARE workshop.
Abstract: We explore the potential of the use of blood vessels as anatomical landmarks for developing image registration methods in colonoscopy images. An unequivocal representation of blood vessels could be used to guide follow-up methods to track lesions over different interventions. We propose a graph-based representation to characterize network structures, such as blood vessels, based on the use of intersections and endpoints. We present a study consisting of the assessment of the minimal performance a keypoint detector should achieve so that the structure can still be recognized. Experimental results prove that, even by achieving a loss of 35% of the keypoints, the descriptive power of the associated graphs to the vessel pattern is still high enough to recognize blood vessels.
Keywords: Colonoscopy; Graph Matching; Biometrics; Vessel; Intersection
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Marc Serra, Olivier Penacchio, Robert Benavente, Maria Vanrell, & Dimitris Samaras. (2014). The Photometry of Intrinsic Images. In 27th IEEE Conference on Computer Vision and Pattern Recognition (pp. 1494–1501).
Abstract: Intrinsic characterization of scenes is often the best way to overcome the illumination variability artifacts that complicate most computer vision problems, from 3D reconstruction to object or material recognition. This paper examines the deficiency of existing intrinsic image models to accurately account for the effects of illuminant color and sensor characteristics in the estimation of intrinsic images and presents a generic framework which incorporates insights from color constancy research to the intrinsic image decomposition problem. The proposed mathematical formulation includes information about the color of the illuminant and the effects of the camera sensors, both of which modify the observed color of the reflectance of the objects in the scene during the acquisition process. By modeling these effects, we get a “truly intrinsic” reflectance image, which we call absolute reflectance, which is invariant to changes of illuminant or camera sensors. This model allows us to represent a wide range of intrinsic image decompositions depending on the specific assumptions on the geometric properties of the scene configuration and the spectral properties of the light source and the acquisition system, thus unifying previous models in a single general framework. We demonstrate that even partial information about sensors improves significantly the estimated reflectance images, thus making our method applicable for a wide range of sensors. We validate our general intrinsic image framework experimentally with both synthetic data and natural images.
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