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Ferran Diego, Joan Serrat, & Antonio Lopez. (2013). Joint spatio-temporal alignment of sequences. TMM - IEEE Transactions on Multimedia, 15(6), 1377–1387.
Abstract: Video alignment is important in different areas of computer vision such as wide baseline matching, action recognition, change detection, video copy detection and frame dropping prevention. Current video alignment methods usually deal with a relatively simple case of fixed or rigidly attached cameras or simultaneous acquisition. Therefore, in this paper we propose a joint video alignment for bringing two video sequences into a spatio-temporal alignment. Specifically, the novelty of the paper is to formulate the video alignment to fold the spatial and temporal alignment into a single alignment framework. This simultaneously satisfies a frame-correspondence and frame-alignment similarity; exploiting the knowledge among neighbor frames by a standard pairwise Markov random field (MRF). This new formulation is able to handle the alignment of sequences recorded at different times by independent moving cameras that follows a similar trajectory, and also generalizes the particular cases that of fixed geometric transformation and/or linear temporal mapping. We conduct experiments on different scenarios such as sequences recorded simultaneously or by moving cameras to validate the robustness of the proposed approach. The proposed method provides the highest video alignment accuracy compared to the state-of-the-art methods on sequences recorded from vehicles driving along the same track at different times.
Keywords: video alignment
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Alicia Fornes, Anjan Dutta, Albert Gordo, & Josep Llados. (2012). CVC-MUSCIMA: A Ground-Truth of Handwritten Music Score Images for Writer Identification and Staff Removal. IJDAR - International Journal on Document Analysis and Recognition, 15(3), 243–251.
Abstract: 0,405JCR
The analysis of music scores has been an active research field in the last decades. However, there are no publicly available databases of handwritten music scores for the research community. In this paper we present the CVC-MUSCIMA database and ground-truth of handwritten music score images. The dataset consists of 1,000 music sheets written by 50 different musicians. It has been especially designed for writer identification and staff removal tasks. In addition to the description of the dataset, ground-truth, partitioning and evaluation metrics, we also provide some base-line results for easing the comparison between different approaches.
Keywords: Music scores; Handwritten documents; Writer identification; Staff removal; Performance evaluation; Graphics recognition; Ground truths
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Partha Pratim Roy, Umapada Pal, & Josep Llados. (2012). Text line extraction in graphical documents using background and foreground. IJDAR - International Journal on Document Analysis and Recognition, 15(3), 227–241.
Abstract: 0,405 JCR
In graphical documents (e.g., maps, engineering drawings), artistic documents etc., the text lines are annotated in multiple orientations or curvilinear way to illustrate different locations or symbols. For the optical character recognition of such documents, individual text lines from the documents need to be extracted. In this paper, we propose a novel method to segment such text lines and the method is based on the foreground and background information of the text components. To effectively utilize the background information, a water reservoir concept is used here. In the proposed scheme, at first, individual components are detected and grouped into character clusters in a hierarchical way using size and positional information. Next, the clusters are extended in two extreme sides to determine potential candidate regions. Finally, with the help of these candidate regions,
individual lines are extracted. The experimental results are presented on different datasets of graphical documents, camera-based warped documents, noisy images containing seals, etc. The results demonstrate that our approach is robust and invariant to size and orientation of the text lines present in
the document.
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Jiaolong Xu, David Vazquez, Antonio Lopez, Javier Marin, & Daniel Ponsa. (2014). Learning a Part-based Pedestrian Detector in Virtual World. TITS - IEEE Transactions on Intelligent Transportation Systems, 15(5), 2121–2131.
Abstract: Detecting pedestrians with on-board vision systems is of paramount interest for assisting drivers to prevent vehicle-to-pedestrian accidents. The core of a pedestrian detector is its classification module, which aims at deciding if a given image window contains a pedestrian. Given the difficulty of this task, many classifiers have been proposed during the last fifteen years. Among them, the so-called (deformable) part-based classifiers including multi-view modeling are usually top ranked in accuracy. Training such classifiers is not trivial since a proper aspect clustering and spatial part alignment of the pedestrian training samples are crucial for obtaining an accurate classifier. In this paper, first we perform automatic aspect clustering and part alignment by using virtual-world pedestrians, i.e., human annotations are not required. Second, we use a mixture-of-parts approach that allows part sharing among different aspects. Third, these proposals are integrated in a learning framework which also allows to incorporate real-world training data to perform domain adaptation between virtual- and real-world cameras. Overall, the obtained results on four popular on-board datasets show that our proposal clearly outperforms the state-of-the-art deformable part-based detector known as latent SVM.
Keywords: Domain Adaptation; Pedestrian Detection; Virtual Worlds
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Jose Manuel Alvarez, Antonio Lopez, Theo Gevers, & Felipe Lumbreras. (2014). Combining Priors, Appearance and Context for Road Detection. TITS - IEEE Transactions on Intelligent Transportation Systems, 15(3), 1168–1178.
Abstract: Detecting the free road surface ahead of a moving vehicle is an important research topic in different areas of computer vision, such as autonomous driving or car collision warning.
Current vision-based road detection methods are usually based solely on low-level features. Furthermore, they generally assume structured roads, road homogeneity, and uniform lighting conditions, constraining their applicability in real-world scenarios. In this paper, road priors and contextual information are introduced for road detection. First, we propose an algorithm to estimate road priors online using geographical information, providing relevant initial information about the road location. Then, contextual cues, including horizon lines, vanishing points, lane markings, 3-D scene layout, and road geometry, are used in addition to low-level cues derived from the appearance of roads. Finally, a generative model is used to combine these cues and priors, leading to a road detection method that is, to a large degree, robust to varying imaging conditions, road types, and scenarios.
Keywords: Illuminant invariance; lane markings; road detection; road prior; road scene understanding; vanishing point; 3-D scene layout
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Mireia Sole, Joan Blanco, Debora Gil, G. Fonseka, Richard Frodsham, Francesca Vidal, et al. (2017). Noves perspectives en l estudi de la territorialitat cromosomica de cel·lules germinals masculines: estudis tridimensionals. JBR - Biologia de la Reproduccio, 73–78.
Abstract: In somatic cells, chromosomes occupy specific nuclear regions called chromosome territories which are involved in the
maintenance and regulation of the genome. Preliminary data in male germ cells also suggest the importance of chromosome
territoriality in cell functionality. Nevertheless, the specific characteristics of testicular tissue (presence of different
cell types with different morphological characteristics, in different stages of development and with different ploidy)
makes difficult to achieve conclusive results. In this study we have developed a methodology to approach the threedimensional
study of all chromosome territories in male germ cells from C57BL/6J mice (Mus musculus). The method
includes the following steps: i) Optimized cell fixation to obtain an optimal preservation of the three-dimensionality cell
morphology, ii) Chromosome identification by FISH (Chromoprobe Multiprobe® OctoChrome™ Murine System; Cytocell)
and confocal microscopy (TCS-SP5, Leica Microsystems), iii) Cell type identification by immunofluorescence
iv) Image analysis using Matlab scripts, v) Numerical data extraction related to chromosome features, chromosome
radial position and chromosome relative position. This methodology allows the unequivocally identification and the
analysis of the chromosome territories of all spermatogenic stages. Results will provide information about the features
that determine chromosomal position, preferred associations between chromosomes, and the relationship between chromosome
positioning and genome regulation.
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Mohamed Ali Souibgui, Asma Bensalah, Jialuo Chen, Alicia Fornes, & Michelle Waldispühl. (2023). A User Perspective on HTR methods for the Automatic Transcription of Rare Scripts: The Case of Codex Runicus Just Accepted. JOCCH - ACM Journal on Computing and Cultural Heritage, 15(4), 1–18.
Abstract: Recent breakthroughs in Artificial Intelligence, Deep Learning and Document Image Analysis and Recognition have significantly eased the creation of digital libraries and the transcription of historical documents. However, for documents in rare scripts with few labelled training data available, current Handwritten Text Recognition (HTR) systems are too constraint. Moreover, research on HTR often focuses on technical aspects only, and rarely puts emphasis on implementing software tools for scholars in Humanities. In this article, we describe, compare and analyse different transcription methods for rare scripts. We evaluate their performance in a real use case of a medieval manuscript written in the runic script (Codex Runicus) and discuss advantages and disadvantages of each method from the user perspective. From this exhaustive analysis and comparison with a fully manual transcription, we raise conclusions and provide recommendations to scholars interested in using automatic transcription tools.
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Guillermo Torres, & Debora Gil. (2020). A multi-shape loss function with adaptive class balancing for the segmentation of lung structures. IJCAR - International Journal of Computer Assisted Radiology and Surgery, 15(1), S154–55.
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Debora Gil, Oriol Ramos Terrades, & Raquel Perez. (2021). Topological Radiomics (TOPiomics): Early Detection of Genetic Abnormalities in Cancer Treatment Evolution. In Extended Abstracts GEOMVAP 2019, Trends in Mathematics 15 (Vol. 15, 89–93). Springer Nature.
Abstract: Abnormalities in radiomic measures correlate to genomic alterations prone to alter the outcome of personalized anti-cancer treatments. TOPiomics is a new method for the early detection of variations in tumor imaging phenotype from a topological structure in multi-view radiomic spaces.
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Yuhua Luo, Francisco Jose Perales, & Juan J. Villanueva. (1992). An automatic Rotoscopy System for Human Motion Based on a Biomedical Graphical Model. Computer & Graphics, 16(4), 355–362.
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A. Pujol, & Juan J. Villanueva. (2002). A supervised Modification of the Hausdorff distance for visual shape classification. International Journal of Pattern Recognition and Artificial Intelligence, 349–359.
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V. Kober, Mikhail Mozerov, J. Alvarez-Borrego, & I.A. Ovseyevich. (2006). Adaptive Correlation Filters for Pattern Recognition. Pattern Recognition and Image Analysis, 425–431.
Abstract: Adaptive correlation filters based on synthetic discriminant functions (SDFs) for reliable pattern recognition are proposed. A given value of discrimination capability can be achieved by adapting a SDF filter to the input scene. This can be done by iterative training. Computer simulation results obtained with the proposed filters are compared with those of various correlation filters in terms of recognition performance.
Keywords: Pattern recognition, Correlation filters, A adaptive filters
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Partha Pratim Roy, Umapada Pal, & Josep Llados. (2008). Recognition of Multi-oriented Touching Characters in Graphical Documents. In Computer Vision, Graphics & Image Processing, 2008. Sixth Indian Conference on, (Vol. 16, 297–304).
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Angel Sappa, Niki Aifanti, Sotiris Malassiotis, & Michael G. Strintzis. (2009). Prior Knowledge Based Motion Model Representation. In Horst Bunke, JuanJose Villanueva, & Gemma Sanchez (Eds.), Progress in Computer Vision and Image Analysis (Vol. 16).
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Pierluigi Casale, Oriol Pujol, & Petia Radeva. (2012). Personalization and User Verification in Wearable Systems using Biometric Walking Patterns. PUC - Personal and Ubiquitous Computing, 16(5), 563–580.
Abstract: In this article, a novel technique for user’s authentication and verification using gait as a biometric unobtrusive pattern is proposed. The method is based on a two stages pipeline. First, a general activity recognition classifier is personalized for an specific user using a small sample of her/his walking pattern. As a result, the system is much more selective with respect to the new walking pattern. A second stage verifies whether the user is an authorized one or not. This stage is defined as a one-class classification problem. In order to solve this problem, a four-layer architecture is built around the geometric concept of convex hull. This architecture allows to improve robustness to outliers, modeling non-convex shapes, and to take into account temporal coherence information. Two different scenarios are proposed as validation with two different wearable systems. First, a custom high-performance wearable system is built and used in a free environment. A second dataset is acquired from an Android-based commercial device in a ‘wild’ scenario with rough terrains, adversarial conditions, crowded places and obstacles. Results on both systems and datasets are very promising, reducing the verification error rates by an order of magnitude with respect to the state-of-the-art technologies.
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