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Author |
Daniel Hernandez; Juan Carlos Moure; Toni Espinosa; Alejandro Chacon; David Vazquez; Antonio Lopez |
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Title |
Real-time 3D Reconstruction for Autonomous Driving via Semi-Global Matching |
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Conference Article |
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Year |
2016 |
Publication |
GPU Technology Conference |
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Stereo; Autonomous Driving; GPU; 3d reconstruction |
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Abstract |
Robust and dense computation of depth information from stereo-camera systems is a computationally demanding requirement for real-time autonomous driving. Semi-Global Matching (SGM) [1] approximates heavy-computation global algorithms results but with lower computational complexity, therefore it is a good candidate for a real-time implementation. SGM minimizes energy along several 1D paths across the image. The aim of this work is to provide a real-time system producing reliable results on energy-efficient hardware. Our design runs on a NVIDIA Titan X GPU at 104.62 FPS and on a NVIDIA Drive PX at 6.7 FPS, promising for real-time platforms |
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Silicon Valley; San Francisco; USA; April 2016 |
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GTC |
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ADAS; 600.085; 600.082; 600.076 |
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no |
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ADAS @ adas @ HME2016 |
Serial |
2738 |
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Author |
Victor Campmany; Sergio Silva; Juan Carlos Moure; Toni Espinosa; David Vazquez; Antonio Lopez |
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Title |
GPU-based pedestrian detection for autonomous driving |
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Conference Article |
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Year |
2016 |
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GPU Technology Conference |
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Pedestrian Detection; GPU |
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Pedestrian detection for autonomous driving is one of the hardest tasks within computer vision, and involves huge computational costs. Obtaining acceptable real-time performance, measured in frames per second (fps), for the most advanced algorithms is nowadays a hard challenge. Taking the work in [1] as our baseline, we propose a CUDA implementation of a pedestrian detection system that includes LBP and HOG as feature descriptors and SVM and Random forest as classifiers. We introduce significant algorithmic adjustments and optimizations to adapt the problem to the NVIDIA GPU architecture. The aim is to deploy a real-time system providing reliable results. |
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Silicon Valley; San Francisco; USA; April 2016 |
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ADAS; 600.085; 600.082; 600.076 |
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ADAS @ adas @ CSM2016 |
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2737 |
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Author |
Aleksandr Setkov; Fabio Martinez Carillo; Michele Gouiffes; Christian Jacquemin; Maria Vanrell; Ramon Baldrich |
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Title |
DAcImPro: A Novel Database of Acquired Image Projections and Its Application to Object Recognition |
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Conference Article |
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Year |
2015 |
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Advances in Visual Computing. Proceedings of 11th International Symposium, ISVC 2015 Part II |
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9475 |
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463-473 |
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Projector-camera systems; Feature descriptors; Object recognition |
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Projector-camera systems are designed to improve the projection quality by comparing original images with their captured projections, which is usually complicated due to high photometric and geometric variations. Many research works address this problem using their own test data which makes it extremely difficult to compare different proposals. This paper has two main contributions. Firstly, we introduce a new database of acquired image projections (DAcImPro) that, covering photometric and geometric conditions and providing data for ground-truth computation, can serve to evaluate different algorithms in projector-camera systems. Secondly, a new object recognition scenario from acquired projections is presented, which could be of a great interest in such domains, as home video projections and public presentations. We show that the task is more challenging than the classical recognition problem and thus requires additional pre-processing, such as color compensation or projection area selection. |
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Springer International Publishing |
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LNCS |
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0302-9743 |
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978-3-319-27862-9 |
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ISVC |
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CIC |
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Admin @ si @ SMG2015 |
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2736 |
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Author |
Mariella Dimiccoli; Jean-Pascal Jacob; Lionel Moisan |
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Title |
Particle detection and tracking in fluorescence time-lapse imaging: a contrario approach |
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Journal Article |
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Year |
2016 |
Publication |
Journal of Machine Vision and Applications |
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MVAP |
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Volume |
27 |
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511-527 |
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Keywords |
particle detection; particle tracking; a-contrario approach; time-lapse fluorescence imaging |
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Abstract |
In this work, we propose a probabilistic approach for the detection and the
tracking of particles on biological images. In presence of very noised and poor
quality data, particles and trajectories can be characterized by an a-contrario
model, that estimates the probability of observing the structures of interest
in random data. This approach, first introduced in the modeling of human visual
perception and then successfully applied in many image processing tasks, leads
to algorithms that do not require a previous learning stage, nor a tedious
parameter tuning and are very robust to noise. Comparative evaluations against
a well established baseline show that the proposed approach outperforms the
state of the art. |
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MILAB; |
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no |
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Admin @ si @ DJM2016 |
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2735 |
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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 |
Type |
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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Volume |
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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no |
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Admin @ si @ KHM2015 |
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2734 |
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Author |
Debora Gil; F. Javier Sanchez; Gloria Fernandez Esparrach; Jorge Bernal |
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Title |
3D Stable Spatio-temporal Polyp Localization in Colonoscopy Videos |
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Book Chapter |
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2015 |
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Computer-Assisted and Robotic Endoscopy. Revised selected papers of Second International Workshop, CARE 2015, Held in Conjunction with MICCAI 2015 |
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9515 |
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140-152 |
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Colonoscopy, Polyp Detection, Polyp Localization, Region Extraction, Watersheds |
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Computational intelligent systems could reduce polyp miss rate in colonoscopy for colon cancer diagnosis and, thus, increase the efficiency of the procedure. One of the main problems of existing polyp localization methods is a lack of spatio-temporal stability in their response. We propose to explore the response of a given polyp localization across temporal windows in order to select
those image regions presenting the highest stable spatio-temporal response.
Spatio-temporal stability is achieved by extracting 3D watershed regions on the
temporal window. Stability in localization response is statistically determined by analysis of the variance of the output of the localization method inside each 3D region. We have explored the benefits of considering spatio-temporal stability in two different tasks: polyp localization and polyp detection. Experimental results indicate an average improvement of 21:5% in polyp localization and 43:78% in polyp detection. |
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CARE |
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IAM; MV; 600.075 |
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Admin @ si @ GSF2015 |
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2733 |
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Gloria Fernandez Esparrach; Jorge Bernal; Cristina Rodriguez de Miguel; Debora Gil; Fernando Vilariño; Henry Cordova; Cristina Sanchez Montes; I.Araujo ; Maria Lopez Ceron; J.Llach; F. Javier Sanchez |
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Title |
Colonic polyps are correctly identified by a computer vision method using wm-dova energy maps |
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Conference Article |
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2015 |
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Proceedings of 23 United European- UEG Week 2015 |
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UEG |
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MV; IAM; 600.075;SIAI |
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no |
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Admin @ si @ FBR2015 |
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2732 |
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Aura Hernandez-Sabate; Meritxell Joanpere; Nuria Gorgorio; Lluis Albarracin |
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Title |
Mathematics learning opportunities when playing a Tower Defense Game |
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2015 |
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International Journal of Serious Games |
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IJSG |
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2 |
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4 |
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57-71 |
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Tower Defense game; learning opportunities; mathematics; problem solving; game design |
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A qualitative research study is presented herein with the purpose of identifying mathematics learning opportunities in students between 10 and 12 years old while playing a commercial version of a Tower Defense game. These learning opportunities are understood as mathematicisable moments of the game and involve the establishment of relationships between the game and mathematical problem solving. Based on the analysis of these mathematicisable moments, we conclude that the game can promote problem-solving processes and learning opportunities that can be associated with different mathematical contents that appears in mathematics curricula, thought it seems that teacher or new game elements might be needed to facilitate the processes. |
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ADAS; 600.076 |
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Admin @ si @ HJG2015 |
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2730 |
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Tadashi Araki; Sumit K. Banchhor; Narendra D. Londhe; Nobutaka Ikeda; Petia Radeva; Devarshi Shukla; Luca Saba; Antonella Balestrieri; Andrew Nicolaides; Shoaib Shafique; John R. Laird; Jasjit S. Suri |
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Title |
Reliable and Accurate Calcium Volume Measurement in Coronary Artery Using Intravascular Ultrasound Videos |
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Journal Article |
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2016 |
Publication |
Journal of Medical Systems |
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JMS |
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40 |
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3 |
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51:1-51:20 |
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Interventional cardiology; Atherosclerosis; Coronary arteries; IVUS; calcium volume; Soft computing; Performance Reliability; Accuracy |
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Quantitative assessment of calcified atherosclerotic volume within the coronary artery wall is vital for cardiac interventional procedures. The goal of this study is to automatically measure the calcium volume, given the borders of coronary vessel wall for all the frames of the intravascular ultrasound (IVUS) video. Three soft computing fuzzy classification techniques were adapted namely Fuzzy c-Means (FCM), K-means, and Hidden Markov Random Field (HMRF) for automated segmentation of calcium regions and volume computation. These methods were benchmarked against previously developed threshold-based method. IVUS image data sets (around 30,600 IVUS frames) from 15 patients were collected using 40 MHz IVUS catheter (Atlantis® SR Pro, Boston Scientific®, pullback speed of 0.5 mm/s). Calcium mean volume for FCM, K-means, HMRF and threshold-based method were 37.84 ± 17.38 mm3, 27.79 ± 10.94 mm3, 46.44 ± 19.13 mm3 and 35.92 ± 16.44 mm3 respectively. Cross-correlation, Jaccard Index and Dice Similarity were highest between FCM and threshold-based method: 0.99, 0.92 ± 0.02 and 0.95 + 0.02 respectively. Student’s t-test, z-test and Wilcoxon-test are also performed to demonstrate consistency, reliability and accuracy of the results. Given the vessel wall region, the system reliably and automatically measures the calcium volume in IVUS videos. Further, we validated our system against a trained expert using scoring: K-means showed the best performance with an accuracy of 92.80 %. Out procedure and protocol is along the line with method previously published clinically. |
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MILAB; |
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no |
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Admin @ si @ ABL2016 |
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2729 |
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Author |
Jiaolong Xu; David Vazquez; Krystian Mikolajczyk; Antonio Lopez |
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Hierarchical online domain adaptation of deformable part-based models |
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Conference Article |
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2016 |
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IEEE International Conference on Robotics and Automation |
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5536-5541 |
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Domain Adaptation; Pedestrian Detection |
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We propose an online domain adaptation method for the deformable part-based model (DPM). The online domain adaptation is based on a two-level hierarchical adaptation tree, which consists of instance detectors in the leaf nodes and a category detector at the root node. Moreover, combined with a multiple object tracking procedure (MOT), our proposal neither requires target-domain annotated data nor revisiting the source-domain data for performing the source-to-target domain adaptation of the DPM. From a practical point of view this means that, given a source-domain DPM and new video for training on a new domain without object annotations, our procedure outputs a new DPM adapted to the domain represented by the video. As proof-of-concept we apply our proposal to the challenging task of pedestrian detection. In this case, each instance detector is an exemplar classifier trained online with only one pedestrian per frame. The pedestrian instances are collected by MOT and the hierarchical model is constructed dynamically according to the pedestrian trajectories. Our experimental results show that the adapted detector achieves the accuracy of recent supervised domain adaptation methods (i.e., requiring manually annotated targetdomain data), and improves the source detector more than 10 percentage points. |
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Stockholm; Sweden; May 2016 |
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ICRA |
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ADAS; 600.085; 600.082; 600.076 |
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Admin @ si @ XVM2016 |
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2728 |
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Lluis Pere de las Heras; Oriol Ramos Terrades; Josep Llados |
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Attributed Graph Grammar for floor plan analysis |
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Conference Article |
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2015 |
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13th International Conference on Document Analysis and Recognition ICDAR2015 |
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726 - 730 |
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In this paper, we propose the use of an Attributed Graph Grammar as unique framework to model and recognize the structure of floor plans. This grammar represents a building as a hierarchical composition of structurally and semantically related elements, where common representations are learned stochastically from annotated data. Given an input image, the parsing consists on constructing that graph representation that better agrees with the probabilistic model defined by the grammar. The proposed method provides several advantages with respect to the traditional floor plan analysis techniques. It uses an unsupervised statistical approach for detecting walls that adapts to different graphical notations and relaxes strong structural assumptions such are straightness and orthogonality. Moreover, the independence between the knowledge model and the parsing implementation allows the method to learn automatically different building configurations and thus, to cope the existing variability. These advantages are clearly demonstrated by comparing it with the most recent floor plan interpretation techniques on 4 datasets of real floor plans with different notations. |
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Nancy; France; August 2015 |
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ICDAR |
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DAG; 600.077; 600.061 |
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Admin @ si @ HRL2015b |
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2727 |
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Lluis Pere de las Heras; Oriol Ramos Terrades; Josep Llados; David Fernandez; Cristina Cañero |
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Use case visual Bag-of-Words techniques for camera based identity document classification |
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Conference Article |
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2015 |
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13th International Conference on Document Analysis and Recognition ICDAR2015 |
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721 - 725 |
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Nowadays, automatic identity document recognition, including passport and driving license recognition, is at the core of many applications within the administrative and service sectors, such as police, hospitality, car renting, etc. In former years, the document information was manually extracted whereas today this data is recognized automatically from images obtained by flat-bed scanners. Yet, since these scanners tend to be expensive and voluminous, companies in the sector have recently turned their attention to cheaper, small and yet computationally powerful scanners: the mobile devices. The document identity recognition from mobile images enclose several new difficulties w.r.t traditional scanned images, such as the loss of a controlled background, perspective, blurring, etc. In this paper we present a real application for identity document classification of images taken from mobile devices. This classification process is of extreme importance since a prior knowledge of the document type and origin strongly facilitates the subsequent information extraction. The proposed method is based on a traditional Bagof-Words in which we have taken into consideration several key aspects to enhance recognition rate. The method performance has been studied on three datasets containing more than 2000 images from 129 different document classes. |
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Nancy; France; August 2015 |
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ICDAR |
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DAG; 600.077; 600.061; |
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Admin @ si @ HRL2015a |
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2726 |
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Author |
R. Bertrand; Oriol Ramos Terrades; P. Gomez-Kramer; P. Franco; Jean-Marc Ogier |
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A Conditional Random Field model for font forgery detection |
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Conference Article |
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2015 |
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13th International Conference on Document Analysis and Recognition ICDAR2015 |
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576 - 580 |
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Nowadays, document forgery is becoming a real issue. A large amount of documents that contain critical information as payment slips, invoices or contracts, are constantly subject to fraudster manipulation because of the lack of security regarding this kind of document. Previously, a system to detect fraudulent documents based on its intrinsic features has been presented. It was especially designed to retrieve copy-move forgery and imperfection due to fraudster manipulation. However, when a set of characters is not present in the original document, copy-move forgery is not feasible. Hence, the fraudster will use a text toolbox to add or modify information in the document by imitating the font or he will cut and paste characters from another document where the font properties are similar. This often results in font type errors. Thus, a clue to detect document forgery consists of finding characters, words or sentences in a document with font properties different from their surroundings. To this end, we present in this paper an automatic forgery detection method based on document font features. Using the Conditional Random Field a measurement of probability that a character belongs to a specific font is made by comparing the character font features to a knowledge database. Then, the character is classified as a genuine or a fake one by comparing its probability to belong to a certain font type with those of the neighboring characters. |
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Nancy; France; August 2015 |
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ICDAR |
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DAG; 600.077 |
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Admin @ si @ BRG2015 |
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2725 |
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M. Campos-Taberner; Adriana Romero; Carlo Gatta; Gustavo Camps-Valls |
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Title |
Shared feature representations of LiDAR and optical images: Trading sparsity for semantic discrimination |
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2015 |
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IEEE International Geoscience and Remote Sensing Symposium IGARSS2015 |
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4169 - 4172 |
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This paper studies the level of complementary information conveyed by extremely high resolution LiDAR and optical images. We pursue this goal following an indirect approach via unsupervised spatial-spectral feature extraction. We used a recently presented unsupervised convolutional neural network trained to enforce both population and lifetime spar-sity in the feature representation. We derived independent and joint feature representations, and analyzed the sparsity scores and the discriminative power. Interestingly, the obtained results revealed that the RGB+LiDAR representation is no longer sparse, and the derived basis functions merge color and elevation yielding a set of more expressive colored edge filters. The joint feature representation is also more discriminative when used for clustering and topological data visualization. |
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Milan; Italy; July 2015 |
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IGARSS |
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LAMP; 600.079;MILAB |
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Admin @ si @ CRG2015 |
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2724 |
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Adriana Romero; Carlo Gatta; Gustavo Camps-Valls |
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Title |
Unsupervised Deep Feature Extraction for Remote Sensing Image Classification |
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Journal Article |
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2016 |
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IEEE Transaction on Geoscience and Remote Sensing |
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TGRS |
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54 |
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3 |
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1349 - 1362 |
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This paper introduces the use of single-layer and deep convolutional networks for remote sensing data analysis. Direct application to multi- and hyperspectral imagery of supervised (shallow or deep) convolutional networks is very challenging given the high input data dimensionality and the relatively small amount of available labeled data. Therefore, we propose the use of greedy layerwise unsupervised pretraining coupled with a highly efficient algorithm for unsupervised learning of sparse features. The algorithm is rooted on sparse representations and enforces both population and lifetime sparsity of the extracted features, simultaneously. We successfully illustrate the expressive power of the extracted representations in several scenarios: classification of aerial scenes, as well as land-use classification in very high resolution or land-cover classification from multi- and hyperspectral images. The proposed algorithm clearly outperforms standard principal component analysis (PCA) and its kernel counterpart (kPCA), as well as current state-of-the-art algorithms of aerial classification, while being extremely computationally efficient at learning representations of data. Results show that single-layer convolutional networks can extract powerful discriminative features only when the receptive field accounts for neighboring pixels and are preferred when the classification requires high resolution and detailed results. However, deep architectures significantly outperform single-layer variants, capturing increasing levels of abstraction and complexity throughout the feature hierarchy. |
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0196-2892 |
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LAMP; 600.079;MILAB |
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Admin @ si @ RGC2016 |
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2723 |
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