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Author |
Frederic Sampedro; Sergio Escalera; Anna Domenech; Ignasi Carrio |
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Title  |
Automatic Tumor Volume Segmentation in Whole-Body PET/CT Scans: A Supervised Learning Approach Source |
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Journal Article |
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Year |
2015 |
Publication |
Journal of Medical Imaging and Health Informatics |
Abbreviated Journal |
JMIHI |
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Volume |
5 |
Issue |
2 |
Pages |
192-201 |
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CONTEXTUAL CLASSIFICATION; PET/CT; SUPERVISED LEARNING; TUMOR SEGMENTATION; WHOLE BODY |
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Whole-body 3D PET/CT tumoral volume segmentation provides relevant diagnostic and prognostic information in clinical oncology and nuclear medicine. Carrying out this procedure manually by a medical expert is time consuming and suffers from inter- and intra-observer variabilities. In this paper, a completely automatic approach to this task is presented. First, the problem is stated and described both in clinical and technological terms. Then, a novel supervised learning segmentation framework is introduced. The segmentation by learning approach is defined within a Cascade of Adaboost classifiers and a 3D contextual proposal of Multiscale Stacked Sequential Learning. Segmentation accuracy results on 200 Breast Cancer whole body PET/CT volumes show mean 49% sensitivity, 99.993% specificity and 39% Jaccard overlap Index, which represent good performance results both at the clinical and technological level. |
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HuPBA;MILAB |
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no |
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Admin @ si @ SED2015 |
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2584 |
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Author |
Antonio Hernandez; Carlos Primo; Sergio Escalera |
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Title |
Automatic user interaction correction via Multi-label Graph cuts |
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2011 |
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In ICCV 2011 1st IEEE International Workshop on Human Interaction in Computer Vision HICV |
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1276-1281 |
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Most applications in image segmentation requires from user interaction in order to achieve accurate results. However, user wants to achieve the desired segmentation accuracy reducing effort of manual labelling. In this work, we extend standard multi-label α-expansion Graph Cut algorithm so that it analyzes the interaction of the user in order to modify the object model and improve final segmentation of objects. The approach is inspired in the fact that fast user interactions may introduce some pixel errors confusing object and background. Our results with different degrees of user interaction and input errors show high performance of the proposed approach on a multi-label human limb segmentation problem compared with classical α-expansion algorithm. |
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978-1-4673-0062-9 |
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HICV |
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MILAB; HuPBA |
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no |
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Admin @ si @ HPE2011 |
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1892 |
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Author |
Marçal Rusiñol; Dimosthenis Karatzas; Josep Llados |
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Title |
Automatic Verification of Properly Signed Multi-page Document Images |
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Conference Article |
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Year |
2015 |
Publication |
Proceedings of the Eleventh International Symposium on Visual Computing |
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9475 |
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327-336 |
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Document Image; Manual Inspection; Signature Verification; Rejection Criterion; Document Flow |
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In this paper we present an industrial application for the automatic screening of incoming multi-page documents in a banking workflow aimed at determining whether these documents are properly signed or not. The proposed method is divided in three main steps. First individual pages are classified in order to identify the pages that should contain a signature. In a second step, we segment within those key pages the location where the signatures should appear. The last step checks whether the signatures are present or not. Our method is tested in a real large-scale environment and we report the results when checking two different types of real multi-page contracts, having in total more than 14,500 pages. |
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Las Vegas, Nevada, USA; December 2015 |
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9475 |
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ISVC |
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DAG; 600.077 |
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no |
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Admin @ si @ |
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3189 |
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Author |
Antonio Hernandez; Carlo Gatta; Petia Radeva; Laura Igual; R. Letaz; Sergio Escalera |
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Title |
Automatic Vessel Segmentation For Angiography and CT Registration |
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Conference Article |
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2010 |
Publication |
Medical Image Computing in Catalunya: Graduate Student Workshop |
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1–2 |
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Girona |
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MICCAT |
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MILAB;HUPBA |
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no |
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BCNPCL @ bcnpcl @ HGR2010 |
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1474 |
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Author |
A. Pujol; Juan J. Villanueva; H. Wechsler |
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Title |
Automatic View Based Caricaturing. |
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Conference Article |
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2000 |
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15 th International Conference on Pattern Recognition |
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1 |
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1072-1075 |
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Barcelona. |
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ICPR |
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no |
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ISE @ ise @ PVW2000 |
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227 |
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Author |
Khalid El Asnaoui; Petia Radeva |
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Title |
Automatically Assess Day Similarity Using Visual Lifelogs |
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Journal Article |
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2020 |
Publication |
International Journal of Intelligent Systems |
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IJIS |
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29 |
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298–310 |
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Today, we witness the appearance of many lifelogging cameras that are able to capture the life of a person wearing the camera and which produce a large number of images everyday. Automatically characterizing the experience and extracting patterns of behavior of individuals from this huge collection of unlabeled and unstructured egocentric data present major challenges and require novel and efficient algorithmic solutions. The main goal of this work is to propose a new method to automatically assess day similarity from the lifelogging images of a person. We propose a technique to measure the similarity between images based on the Swain’s distance and generalize it to detect the similarity between daily visual data. To this purpose, we apply the dynamic time warping (DTW) combined with the Swain’s distance for final day similarity estimation. For validation, we apply our technique on the Egocentric Dataset of University of Barcelona (EDUB) of 4912 daily images acquired by four persons with preliminary encouraging results. |
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MILAB; no proj |
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no |
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AsR2020 |
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3409 |
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Author |
Joan Serrat; J. Argemi; Juan J. Villanueva |
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Title |
Automatization of TW2 method using a knowledge-based image analysis system. |
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Conference Article |
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1991 |
Publication |
VIth International Congress of Auxology. |
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Madrid |
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ADAS |
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no |
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ADAS @ adas @ SAV1991 |
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259 |
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Isabelle Guyon; Kristin Bennett; Gavin Cawley; Hugo Jair Escalante; Sergio Escalera; Tin Kam Ho; Nuria Macia; Bisakha Ray; Mehreen Saeed; Alexander Statnikov; Evelyne Viegas |
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Title |
AutoML Challenge 2015: Design and First Results |
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Conference Article |
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2015 |
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32nd International Conference on Machine Learning, ICML workshop, JMLR proceedings ICML15 |
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1-8 |
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AutoML Challenge; machine learning; model selection; meta-learning; repre- sentation learning; active learning |
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ChaLearn is organizing the Automatic Machine Learning (AutoML) contest 2015, which challenges participants to solve classi cation and regression problems without any human intervention. Participants' code is automatically run on the contest servers to train and test learning machines. However, there is no obligation to submit code; half of the prizes can be won by submitting prediction results only. Datasets of progressively increasing diculty are introduced throughout the six rounds of the challenge. (Participants can
enter the competition in any round.) The rounds alternate phases in which learners are tested on datasets participants have not seen (AutoML), and phases in which participants have limited time to tweak their algorithms on those datasets to improve performance (Tweakathon). This challenge will push the state of the art in fully automatic machine learning on a wide range of real-world problems. The platform will remain available beyond the termination of the challenge: http://codalab.org/AutoML. |
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Lille; France; July 2015 |
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ICML |
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HuPBA;MILAB |
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no |
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Admin @ si @ GBC2015c |
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2656 |
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Author |
Judit Martinez |
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Automotive sector and Machine Vision |
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Miscellaneous |
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2002 |
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European Integrated Machine Vision, Special Edition: Automative and Glass |
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Admin @ si @ 37316 |
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270 |
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Sergio Silva; Victor Campmany; Laura Sellart; Juan Carlos Moure; Antoni Espinosa; David Vazquez; Antonio Lopez |
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Autonomous GPU-based Driving |
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2015 |
Publication |
Programming and Tunning Massive Parallel Systems |
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PUMPS |
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Autonomous Driving; ADAS; CUDA |
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Human factors cause most driving accidents; this is why nowadays is common to hear about autonomous driving as an alternative. Autonomous driving will not only increase safety, but also will develop a system of cooperative self-driving cars that will reduce pollution and congestion. Furthermore, it will provide more freedom to handicapped people, elderly or kids.
Autonomous Driving requires perceiving and understanding the vehicle environment (e.g., road, traffic signs, pedestrians, vehicles) using sensors (e.g., cameras, lidars, sonars, and radars), selflocalization (requiring GPS, inertial sensors and visual localization in precise maps), controlling the vehicle and planning the routes. These algorithms require high computation capability, and thanks to NVIDIA GPU acceleration this starts to become feasible.
NVIDIA® is developing a new platform for boosting the Autonomous Driving capabilities that is able of managing the vehicle via CAN-Bus: the Drive™ PX. It has 8 ARM cores with dual accelerated Tegra® X1 chips. It has 12 synchronized camera inputs for 360º vehicle perception, 4G and Wi-Fi capabilities allowing vehicle communications and GPS and inertial sensors inputs for self-localization.
Our research group has been selected for testing Drive™ PX. Accordingly, we are developing a Drive™ PX based autonomous car. Currently, we are porting our previous CPU based algorithms (e.g., Lane Departure Warning, Collision Warning, Automatic Cruise Control, Pedestrian Protection, or Semantic Segmentation) for running in the GPU. |
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Barcelona; Spain |
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ADAS; 600.076; 600.082; 600.085 |
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ADAS @ adas @ SCS2015 |
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2645 |
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Author |
Hugo Berti; Angel Sappa; Osvaldo Agamennoni |
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Title |
Autonomous robot navigation with a global and asymptotic convergence |
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2007 |
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IEEE International Conference on Robotics and Automation |
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2712–2717 |
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Roma (Italy) |
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ICRA |
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ADAS |
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ADAS @ adas @ BSA2007 |
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796 |
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T. Alejandra Vidal; A. Sanfeliu; Juan Andrade |
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Autonomous Single Camera Exploration |
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2006 |
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Jornada de Recerca en Automatica, Visio i Robotica |
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Barcelona (Spain) |
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Admin @ si @ VSA2006c |
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680 |
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Pau Baiget; Xavier Roca; Jordi Gonzalez |
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Autonomous Virtual Agents for Performance Evaluation of Tracking Algorithms |
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2008 |
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Articulated Motion and Deformable Objects, 5th International Conference AMDO 2008, |
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5098 |
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299-308 |
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Port d'Andratx (Mallorca) |
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ISE |
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ISE @ ise @ BRG2008 |
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974 |
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Naila Murray; Luca Marchesotti; Florent Perronnin |
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AVA: A Large-Scale Database for Aesthetic Visual Analysis |
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2012 |
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25th IEEE Conference on Computer Vision and Pattern Recognition |
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2408-2415 |
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With the ever-expanding volume of visual content available, the ability to organize and navigate such content by aesthetic preference is becoming increasingly important. While still in its nascent stage, research into computational models of aesthetic preference already shows great potential. However, to advance research, realistic, diverse and challenging databases are needed. To this end, we introduce a new large-scale database for conducting Aesthetic Visual Analysis: AVA. It contains over 250,000 images along with a rich variety of meta-data including a large number of aesthetic scores for each image, semantic labels for over 60 categories as well as labels related to photographic style. We show the advantages of AVA with respect to existing databases in terms of scale, diversity, and heterogeneity of annotations. We then describe several key insights into aesthetic preference afforded by AVA. Finally, we demonstrate, through three applications, how the large scale of AVA can be leveraged to improve performance on existing preference tasks |
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Providence, Rhode Islan |
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IEEE Xplore |
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1063-6919 |
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978-1-4673-1226-4 |
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CIC |
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Admin @ si @ MMP2012a |
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2025 |
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Author |
Vincenzo Lomonaco; Lorenzo Pellegrini; Andrea Cossu; Antonio Carta; Gabriele Graffieti; Tyler L. Hayes; Matthias De Lange; Marc Masana; Jary Pomponi; Gido van de Ven; Martin Mundt; Qi She; Keiland Cooper; Jeremy Forest; Eden Belouadah; Simone Calderara; German I. Parisi; Fabio Cuzzolin; Andreas Tolias; Simone Scardapane; Luca Antiga; Subutai Amhad; Adrian Popescu; Christopher Kanan; Joost Van de Weijer; Tinne Tuytelaars; Davide Bacciu; Davide Maltoni |
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Title |
Avalanche: an End-to-End Library for Continual Learning |
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Conference Article |
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Year |
2021 |
Publication |
34th IEEE Conference on Computer Vision and Pattern Recognition Workshops |
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3595-3605 |
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Abstract |
Learning continually from non-stationary data streams is a long-standing goal and a challenging problem in machine learning. Recently, we have witnessed a renewed and fast-growing interest in continual learning, especially within the deep learning community. However, algorithmic solutions are often difficult to re-implement, evaluate and port across different settings, where even results on standard benchmarks are hard to reproduce. In this work, we propose Avalanche, an open-source end-to-end library for continual learning research based on PyTorch. Avalanche is designed to provide a shared and collaborative codebase for fast prototyping, training, and reproducible evaluation of continual learning algorithms. |
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Virtual; June 2021 |
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Notes |
LAMP; 600.120 |
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Call Number |
Admin @ si @ LPC2021 |
Serial |
3567 |
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