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Author |
Hanne Kause; Aura Hernandez-Sabate; Patricia Marquez; Andrea Fuster; Luc Florack; Hans van Assen; Debora Gil |
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Title |
Confidence Measures for Assessing the HARP Algorithm in Tagged Magnetic Resonance Imaging |
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Book Chapter |
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Year |
2015 |
Publication |
Statistical Atlases and Computational Models of the Heart. Revised selected papers of Imaging and Modelling Challenges 6th International Workshop, STACOM 2015, Held in Conjunction with MICCAI 2015 |
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9534 |
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69-79 |
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Cardiac deformation and changes therein have been linked to pathologies. Both can be extracted in detail from tagged Magnetic Resonance Imaging (tMRI) using harmonic phase (HARP) images. Although point tracking algorithms have shown to have high accuracies on HARP images, these vary with position. Detecting and discarding areas with unreliable results is crucial for use in clinical support systems. This paper assesses the capability of two confidence measures (CMs), based on energy and image structure, for detecting locations with reduced accuracy in motion tracking results. These CMs were tested on a database of simulated tMRI images containing the most common artifacts that may affect tracking accuracy. CM performance is assessed based on its capability for HARP tracking error bounding and compared in terms of significant differences detected using a multi comparison analysis of variance that takes into account the most influential factors on HARP tracking performance. Results showed that the CM based on image structure was better suited to detect unreliable optical flow vectors. In addition, it was shown that CMs can be used to detect optical flow vectors with large errors in order to improve the optical flow obtained with the HARP tracking algorithm. |
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Munich; Germany; January 2015 |
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Springer International Publishing |
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0302-9743 |
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978-3-319-28711-9 |
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STACOM |
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ADAS; IAM; 600.075; 600.076; 600.060; 601.145 |
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Admin @ si @ KHM2015 |
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2734 |
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Author |
Jordina Torrents-Barrena; Aida Valls; Petia Radeva; Meritxell Arenas; Domenec Puig |
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Title |
Automatic Recognition of Molecular Subtypes of Breast Cancer in X-Ray images using Segmentation-based Fractal Texture Analysis |
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Book Chapter |
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2015 |
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Artificial Intelligence Research and Development |
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277 |
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247 - 256 |
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Breast cancer disease has recently been classified into four subtypes regarding the molecular properties of the affected tumor region. For each patient, an accurate diagnosis of the specific type is vital to decide the most appropriate therapy in order to enhance life prospects. Nowadays, advanced therapeutic diagnosis research is focused on gene selection methods, which are not robust enough. Hence, we hypothesize that computer vision algorithms can offer benefits to address the problem of discriminating among them through X-Ray images. In this paper, we propose a novel approach driven by texture feature descriptors and machine learning techniques. First, we segment the tumour part through an active contour technique and then, we perform a complete fractal analysis to collect qualitative information of the region of interest in the feature extraction stage. Finally, several supervised and unsupervised classifiers are used to perform multiclass classification of the aforementioned data. The experimental results presented in this paper support that it is possible to establish a relation between each tumor subtype and the extracted features of the patterns revealed on mammograms. |
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IOS Press |
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Frontiers in Artificial Intelligence and Applications |
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MILAB |
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Admin @ si @TVR2015 |
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2780 |
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Author |
E. Tavalera; Mariella Dimiccoli; Marc Bolaños; Maedeh Aghaei; Petia Radeva |
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Title |
Regularized Clustering for Egocentric Video Segmentation |
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Book Chapter |
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2015 |
Publication |
Pattern Recognition and Image Analysis |
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327-336 |
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Temporal video segmentation ; Egocentric videos ; Clustering |
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In this paper, we present a new method for egocentric video temporal segmentation based on integrating a statistical mean change detector and agglomerative clustering(AC) within an energyminimization framework. Given the tendency of most AC methods to oversegment video sequences when clustering their frames, we combine the clustering with a concept drift detection technique (ADWIN) that has rigorous guarantee of performances. ADWIN serves as a statistical upper bound for the clustering-based video segmentation. We integrate techniques in an energy-minimization framework that serves disambiguate the decision of both techniques and to complete the segmentation taking into account the temporal continuity of video frames We present experiments over egocentric sets of more than 13.000 images acquired with different wearable cameras, showing that our method outperforms state-of-the-art clustering methods. |
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Springer International Publishing |
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978-3-319-19390-8 |
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MILAB |
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Admin @ si @TDB2015a |
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2781 |
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Author |
Fernando Vilariño; Dimosthenis Karatzas; Marcos Catalan; Alberto Valcarcel |
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An horizon for the Public Library as a place for innovation and creativity. The Library Living Lab in Volpelleres |
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2015 |
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The White Book on Public Library Network from Diputació de Barcelona |
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MV; DAG;SIAI |
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Admin @ si @VKC2015 |
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2798 |
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Author |
Pedro Herruzo; Marc Bolaños; Petia Radeva |
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Title |
Can a CNN Recognize Catalan Diet? |
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Book Chapter |
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2016 |
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AIP Conference Proceedings |
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1773 |
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Abstract |
CoRR abs/1607.08811
Nowadays, we can find several diseases related to the unhealthy diet habits of the population, such as diabetes, obesity, anemia, bulimia and anorexia. In many cases, these diseases are related to the food consumption of people. Mediterranean diet is scientifically known as a healthy diet that helps to prevent many metabolic diseases. In particular, our work focuses on the recognition of Mediterranean food and dishes. The development of this methodology would allow to analise the daily habits of users with wearable cameras, within the topic of lifelogging. By using automatic mechanisms we could build an objective tool for the analysis of the patient’s behavior, allowing specialists to discover unhealthy food patterns and understand the user’s lifestyle.
With the aim to automatically recognize a complete diet, we introduce a challenging multi-labeled dataset related to Mediter-ranean diet called FoodCAT. The first type of label provided consists of 115 food classes with an average of 400 images per dish, and the second one consists of 12 food categories with an average of 3800 pictures per class. This dataset will serve as a basis for the development of automatic diet recognition. In this context, deep learning and more specifically, Convolutional Neural Networks (CNNs), currently are state-of-the-art methods for automatic food recognition. In our work, we compare several architectures for image classification, with the purpose of diet recognition. Applying the best model for recognising food categories, we achieve a top-1 accuracy of 72.29%, and top-5 of 97.07%. In a complete diet recognition of dishes from Mediterranean diet, enlarged with the Food-101 dataset for international dishes recognition, we achieve a top-1 accuracy of 68.07%, and top-5 of 89.53%, for a total of 115+101 food classes. |
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MILAB |
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no |
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Admin @ si @ HBR2016 |
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2837 |
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Author |
Antonio Lopez; Jiaolong Xu; Jose Luis Gomez; David Vazquez; German Ros |
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Title |
From Virtual to Real World Visual Perception using Domain Adaptation -- The DPM as Example |
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Book Chapter |
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Year |
2017 |
Publication |
Domain Adaptation in Computer Vision Applications |
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13 |
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243-258 |
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Domain Adaptation |
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Abstract |
Supervised learning tends to produce more accurate classifiers than unsupervised learning in general. This implies that training data is preferred with annotations. When addressing visual perception challenges, such as localizing certain object classes within an image, the learning of the involved classifiers turns out to be a practical bottleneck. The reason is that, at least, we have to frame object examples with bounding boxes in thousands of images. A priori, the more complex the model is regarding its number of parameters, the more annotated examples are required. This annotation task is performed by human oracles, which ends up in inaccuracies and errors in the annotations (aka ground truth) since the task is inherently very cumbersome and sometimes ambiguous. As an alternative we have pioneered the use of virtual worlds for collecting such annotations automatically and with high precision. However, since the models learned with virtual data must operate in the real world, we still need to perform domain adaptation (DA). In this chapter we revisit the DA of a deformable part-based model (DPM) as an exemplifying case of virtual- to-real-world DA. As a use case, we address the challenge of vehicle detection for driver assistance, using different publicly available virtual-world data. While doing so, we investigate questions such as: how does the domain gap behave due to virtual-vs-real data with respect to dominant object appearance per domain, as well as the role of photo-realism in the virtual world. |
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Springer |
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Editor |
Gabriela Csurka |
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ADAS; 600.085; 601.223; 600.076; 600.118 |
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ADAS @ adas @ LXG2017 |
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2872 |
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Author |
David Geronimo; David Vazquez; Arturo de la Escalera |
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Title |
Vision-Based Advanced Driver Assistance Systems |
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Book Chapter |
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2017 |
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Computer Vision in Vehicle Technology: Land, Sea, and Air |
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ADAS; Autonomous Driving |
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ADAS; 600.118 |
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ADAS @ adas @ GVE2017 |
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2881 |
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German Ros; Laura Sellart; Gabriel Villalonga; Elias Maidanik; Francisco Molero; Marc Garcia; Adriana Cedeño; Francisco Perez; Didier Ramirez; Eduardo Escobar; Jose Luis Gomez; David Vazquez; Antonio Lopez |
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Title |
Semantic Segmentation of Urban Scenes via Domain Adaptation of SYNTHIA |
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Book Chapter |
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2017 |
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Domain Adaptation in Computer Vision Applications |
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12 |
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227-241 |
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SYNTHIA; Virtual worlds; Autonomous Driving |
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Vision-based semantic segmentation in urban scenarios is a key functionality for autonomous driving. Recent revolutionary results of deep convolutional neural networks (DCNNs) foreshadow the advent of reliable classifiers to perform such visual tasks. However, DCNNs require learning of many parameters from raw images; thus, having a sufficient amount of diverse images with class annotations is needed. These annotations are obtained via cumbersome, human labour which is particularly challenging for semantic segmentation since pixel-level annotations are required. In this chapter, we propose to use a combination of a virtual world to automatically generate realistic synthetic images with pixel-level annotations, and domain adaptation to transfer the models learnt to correctly operate in real scenarios. We address the question of how useful synthetic data can be for semantic segmentation – in particular, when using a DCNN paradigm. In order to answer this question we have generated a synthetic collection of diverse urban images, named SYNTHIA, with automatically generated class annotations and object identifiers. We use SYNTHIA in combination with publicly available real-world urban images with manually provided annotations. Then, we conduct experiments with DCNNs that show that combining SYNTHIA with simple domain adaptation techniques in the training stage significantly improves performance on semantic segmentation. |
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Springer |
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Gabriela Csurka |
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ADAS; 600.085; 600.082; 600.076; 600.118 |
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ADAS @ adas @ RSV2017 |
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2882 |
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Author |
Marçal Rusiñol; Josep Llados |
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Title |
Flowchart Recognition in Patent Information Retrieval |
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2017 |
Publication |
Current Challenges in Patent Information Retrieval |
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37 |
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351-368 |
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Springer Berlin Heidelberg |
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M. Lupu; K. Mayer; N. Kando; A.J. Trippe |
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DAG; 600.097; 600.121 |
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Admin @ si @ RuL2017 |
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2896 |
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Author |
Joana Maria Pujadas-Mora; Alicia Fornes; Josep Llados; Anna Cabre |
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Bridging the gap between historical demography and computing: tools for computer-assisted transcription and the analysis of demographic sources |
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2016 |
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The future of historical demography. Upside down and inside out |
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127-131 |
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Acco Publishers |
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K.Matthijs; S.Hin; H.Matsuo; J.Kok |
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978-94-6292-722-3 |
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DAG; 600.097 |
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Admin @ si @ PFL2016 |
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2907 |
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Hana Jarraya; Muhammad Muzzamil Luqman; Jean-Yves Ramel |
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Improving Fuzzy Multilevel Graph Embedding Technique by Employing Topological Node Features: An Application to Graphics Recognition |
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2017 |
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Graphics Recognition. Current Trends and Challenges |
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9657 |
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Springer |
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B. Lamiroy; R Dueire Lins |
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GREC |
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DAG; 600.097; 600.121 |
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Admin @ si @ JLR2017 |
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2928 |
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H. Martin Kjer; Jens Fagertun; Sergio Vera; Debora Gil |
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Medial structure generation for registration of anatomical structures |
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2017 |
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Skeletonization, Theory, Methods and Applications |
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11 |
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IAM; 600.096; 600.075; 600.145 |
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Admin @ si @ MFV2017a |
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2935 |
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Author |
Pau Riba; Alicia Fornes; Josep Llados |
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Towards the Alignment of Handwritten Music Scores |
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2017 |
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International Workshop on Graphics Recognition. GREC 2015.Graphic Recognition. Current Trends and Challenges |
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9657 |
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103-116 |
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Optical Music Recognition; Handwritten Music Scores; Dynamic Time Warping alignment |
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It is very common to nd di erent versions of the same music work in archives of Opera Theaters. These di erences correspond to modi cations and annotations from the musicians. From the musicologist point of view, these variations are very interesting and deserve study.
This paper explores the alignment of music scores as a tool for automatically detecting the passages that contain such di erences. Given the diculties in the recognition of handwritten music scores, our goal is to align the music scores and at the same time, avoid the recognition of music elements as much as possible. After removing the sta lines, braces and ties, the bar lines are detected. Then, the bar units are described as a whole using the Blurred Shape Model. The bar units alignment is performed by using Dynamic Time Warping. The analysis of the alignment path is used to detect the variations in the music scores. The method has been evaluated on a subset of the CVC-MUSCIMA dataset, showing encouraging results. |
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Bart Lamiroy; R Dueire Lins |
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978-3-319-52158-9 |
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DAG; 600.097; 602.006; 600.121 |
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Admin @ si @ RFL2017 |
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2955 |
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Author |
Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades |
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Title |
Spotting Symbol over Graphical Documents Via Sparsity in Visual Vocabulary |
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Book Chapter |
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2016 |
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Recent Trends in Image Processing and Pattern Recognition |
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709 |
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RTIP2R |
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DAG |
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no |
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Admin @ si @ HTR2016 |
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2956 |
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Author |
Maryam Asadi-Aghbolaghi; Albert Clapes; Marco Bellantonio; Hugo Jair Escalante; Victor Ponce; Xavier Baro; Isabelle Guyon; Shohreh Kasaei; Sergio Escalera |
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Title |
Deep Learning for Action and Gesture Recognition in Image Sequences: A Survey |
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Book Chapter |
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2017 |
Publication |
Gesture Recognition |
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539-578 |
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Action recognition; Gesture recognition; Deep learning architectures; Fusion strategies |
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Abstract |
Interest in automatic action and gesture recognition has grown considerably in the last few years. This is due in part to the large number of application domains for this type of technology. As in many other computer vision areas, deep learning based methods have quickly become a reference methodology for obtaining state-of-the-art performance in both tasks. This chapter is a survey of current deep learning based methodologies for action and gesture recognition in sequences of images. The survey reviews both fundamental and cutting edge methodologies reported in the last few years. We introduce a taxonomy that summarizes important aspects of deep learning for approaching both tasks. Details of the proposed architectures, fusion strategies, main datasets, and competitions are reviewed. Also, we summarize and discuss the main works proposed so far with particular interest on how they treat the temporal dimension of data, their highlighting features, and opportunities and challenges for future research. To the best of our knowledge this is the first survey in the topic. We foresee this survey will become a reference in this ever dynamic field of research. |
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HUPBA; no proj |
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Admin @ si @ ACB2017a |
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2981 |
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