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Author Jose Manuel Alvarez; Ferran Diego; Joan Serrat; Antonio Lopez
Title Automatic Ground-truthing using video registration for on-board detection algorithms Type Conference Article
Year 2009 Publication (up) 16th IEEE International Conference on Image Processing Abbreviated Journal
Volume Issue Pages 4389 - 4392
Keywords
Abstract Ground-truth data is essential for the objective evaluation of object detection methods in computer vision. Many works claim their method is robust but they support it with experiments which are not quantitatively assessed with regard some ground-truth. This is one of the main obstacles to properly evaluate and compare such methods. One of the main reasons is that creating an extensive and representative ground-truth is very time consuming, specially in the case of video sequences, where thousands of frames have to be labelled. Could such a ground-truth be generated, at least in part, automatically? Though it may seem a contradictory question, we show that this is possible for the case of video sequences recorded from a moving camera. The key idea is transferring existing frame segmentations from a reference sequence into another video sequence recorded at a different time on the same track, possibly under a different ambient lighting. We have carried out experiments on several video sequence pairs and quantitatively assessed the precision of the transformed ground-truth, which prove that our approach is not only feasible but also quite accurate.
Address Cairo, Egypt
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1522-4880 ISBN 978-1-4244-5653-6 Medium
Area Expedition Conference ICIP
Notes ADAS Approved no
Call Number ADAS @ adas @ ADS2009 Serial 1201
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Author Angel Sappa; Mohammad Rouhani
Title Efficient Distance Estimation for Fitting Implicit Quadric Surfaces Type Conference Article
Year 2009 Publication (up) 16th IEEE International Conference on Image Processing Abbreviated Journal
Volume Issue Pages 3521–3524
Keywords
Abstract This paper presents a novel approach for estimating the shortest Euclidean distance from a given point to the corresponding implicit quadric fitting surface. It first estimates the orthogonal orientation to the surface from the given point; then the shortest distance is directly estimated by intersecting the implicit surface with a line passing through the given point according to the estimated orthogonal orientation. The proposed orthogonal distance estimation is easily obtained without increasing computational complexity; hence it can be used in error minimization surface fitting frameworks. Comparisons of the proposed metric with previous approaches are provided to show both improvements in CPU time as well as in the accuracy of the obtained results. Surfaces fitted by using the proposed geometric distance estimation and state of the art metrics are presented to show the viability of the proposed approach.
Address Cairo, Egypt
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1522-4880 ISBN 978-1-4244-5653-6 Medium
Area Expedition Conference ICIP
Notes ADAS Approved no
Call Number ADAS @ adas @ SaR2009 Serial 1232
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Author Carlo Gatta; Petia Radeva
Title Bilateral Enhancers Type Conference Article
Year 2009 Publication (up) 16th IEEE International Conference on Image Processing Abbreviated Journal
Volume Issue Pages 3161-3165
Keywords
Abstract Ten years ago the concept of bilateral filtering (BF) became popular in the image processing community. The core of the idea is to blend the effect of a spatial filter, as e.g. the Gaussian filter, with the effect of a filter that acts on image values. The two filters acts on orthogonal domains of a picture: the 2D lattice of the image support and the intensity (or color) domain. The BF approach is an intuitive way to blend these two filters giving rise to algorithms that perform difficult tasks requiring a relatively simple design. In this paper we extend the concept of BF, proposing the bilateral enhancers (BE). We show how to design proper functions to obtain an edge-preserving smoothing and a selective sharpening. Moreover, we show that the proposed algorithm can perform edge-preserving smoothing and selective sharpening simultaneously in a single filtering.
Address Cairo, Egypt
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1522-4880 ISBN 978-1-4244-5653-6 Medium
Area Expedition Conference ICIP
Notes MILAB Approved no
Call Number BCNPCL @ bcnpcl @ GaR2009b Serial 1243
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Author Patricia Suarez; Dario Carpio; Angel Sappa; Henry Velesaca
Title Transformer based Image Dehazing Type Conference Article
Year 2022 Publication (up) 16th IEEE International Conference on Signal Image Technology & Internet Based System Abbreviated Journal
Volume Issue Pages
Keywords atmospheric light; brightness component; computational cost; dehazing quality; haze-free image
Abstract This paper presents a novel approach to remove non homogeneous haze from real images. The proposed method consists mainly of image feature extraction, haze removal, and image reconstruction. To accomplish this challenging task, we propose an architecture based on transformers, which have been recently introduced and have shown great potential in different computer vision tasks. Our model is based on the SwinIR an image restoration architecture based on a transformer, but by modifying the deep feature extraction module, the depth level of the model, and by applying a combined loss function that improves styling and adapts the model for the non-homogeneous haze removal present in images. The obtained results prove to be superior to those obtained by state-of-the-art models.
Address Dijon; France; October 2022
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference SITIS
Notes MSIAU; no proj Approved no
Call Number Admin @ si @ SCS2022 Serial 3803
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Author Fernando Alonso; Xavier Baro; Sergio Escalera; Jordi Gonzalez; Martha Mackay; Anna Serrahima
Title CARE RESPITE: TAKING CARE OF THE CAREGIVERS, Theme 5 The Strategic use of Mobile and Digital Health and Care Solutions Type Conference Article
Year 2016 Publication (up) 16th International Conference for Integrated Care Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Poster
Address Barcelona; Spain; May 2016
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ICIC
Notes HuPBA; ISE;MV Approved no
Call Number Admin @ si @ ABE2016 Serial 2855
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Author Daniel Hernandez; Alejandro Chacon; Antonio Espinosa; David Vazquez; Juan Carlos Moure; Antonio Lopez
Title Embedded real-time stereo estimation via Semi-Global Matching on the GPU Type Conference Article
Year 2016 Publication (up) 16th International Conference on Computational Science Abbreviated Journal
Volume 80 Issue Pages 143-153
Keywords Autonomous Driving; Stereo; CUDA; 3d reconstruction
Abstract Dense, robust and real-time computation of depth information from stereo-camera systems is a computationally demanding requirement for robotics, advanced driver assistance systems (ADAS) and autonomous vehicles. Semi-Global Matching (SGM) is a widely used algorithm that propagates consistency constraints along several paths across the image. This work presents a real-time system producing reliable disparity estimation results on the new embedded energy-efficient GPU devices. Our design runs on a Tegra X1 at 41 frames per second for an image size of 640x480, 128 disparity levels, and using 4 path directions for the SGM method.
Address San Diego; CA; USA; June 2016
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ICCS
Notes ADAS; 600.085; 600.082; 600.076 Approved no
Call Number ADAS @ adas @ HCE2016a Serial 2740
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Author Victor Campmany; Sergio Silva; Antonio Espinosa; Juan Carlos Moure; David Vazquez; Antonio Lopez
Title GPU-based pedestrian detection for autonomous driving Type Conference Article
Year 2016 Publication (up) 16th International Conference on Computational Science Abbreviated Journal
Volume 80 Issue Pages 2377-2381
Keywords Pedestrian detection; Autonomous Driving; CUDA
Abstract We propose a real-time pedestrian detection system for the embedded Nvidia Tegra X1 GPU-CPU hybrid platform. The pipeline is composed by the following state-of-the-art algorithms: Histogram of Local Binary Patterns (LBP) and Histograms of Oriented Gradients (HOG) features extracted from the input image; Pyramidal Sliding Window technique for foreground segmentation; and Support Vector Machine (SVM) for classification. Results show a 8x speedup in the target Tegra X1 platform and a better performance/watt ratio than desktop CUDA platforms in study.
Address San Diego; CA; USA; June 2016
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ICCS
Notes ADAS; 600.085; 600.082; 600.076 Approved no
Call Number ADAS @ adas @ CSE2016 Serial 2741
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Author Angel Sappa; Patricia Suarez; Henry Velesaca; Dario Carpio
Title Domain Adaptation in Image Dehazing: Exploring the Usage of Images from Virtual Scenarios Type Conference Article
Year 2022 Publication (up) 16th International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing Abbreviated Journal
Volume Issue Pages 85-92
Keywords Domain adaptation; Synthetic hazed dataset; Dehazing
Abstract This work presents a novel domain adaptation strategy for deep learning-based approaches to solve the image dehazing
problem. Firstly, a large set of synthetic images is generated by using a realistic 3D graphic simulator; these synthetic
images contain different densities of haze, which are used for training the model that is later adapted to any real scenario.
The adaptation process requires just a few images to fine-tune the model parameters. The proposed strategy allows
overcoming the limitation of training a given model with few images. In other words, the proposed strategy implements
the adaptation of a haze removal model trained with synthetic images to real scenarios. It should be noticed that it is quite
difficult, if not impossible, to have large sets of pairs of real-world images (with and without haze) to train in a supervised
way dehazing algorithms. Experimental results are provided showing the validity of the proposed domain adaptation
strategy.
Address Lisboa; Portugal; July 2022
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference CGVCVIP
Notes MSIAU; no proj Approved no
Call Number Admin @ si @ SSV2022 Serial 3804
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Author Adria Molina; Pau Riba; Lluis Gomez; Oriol Ramos Terrades; Josep Llados
Title Date Estimation in the Wild of Scanned Historical Photos: An Image Retrieval Approach Type Conference Article
Year 2021 Publication (up) 16th International Conference on Document Analysis and Recognition Abbreviated Journal
Volume 12822 Issue Pages 306-320
Keywords
Abstract This paper presents a novel method for date estimation of historical photographs from archival sources. The main contribution is to formulate the date estimation as a retrieval task, where given a query, the retrieved images are ranked in terms of the estimated date similarity. The closer are their embedded representations the closer are their dates. Contrary to the traditional models that design a neural network that learns a classifier or a regressor, we propose a learning objective based on the nDCG ranking metric. We have experimentally evaluated the performance of the method in two different tasks: date estimation and date-sensitive image retrieval, using the DEW public database, overcoming the baseline methods.
Address Lausanne; Suissa; September 2021
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ICDAR
Notes DAG; 600.121; 600.140; 110.312 Approved no
Call Number Admin @ si @ MRG2021b Serial 3571
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Author Pau Riba; Adria Molina; Lluis Gomez; Oriol Ramos Terrades; Josep Llados
Title Learning to Rank Words: Optimizing Ranking Metrics for Word Spotting Type Conference Article
Year 2021 Publication (up) 16th International Conference on Document Analysis and Recognition Abbreviated Journal
Volume 12822 Issue Pages 381–395
Keywords
Abstract In this paper, we explore and evaluate the use of ranking-based objective functions for learning simultaneously a word string and a word image encoder. We consider retrieval frameworks in which the user expects a retrieval list ranked according to a defined relevance score. In the context of a word spotting problem, the relevance score has been set according to the string edit distance from the query string. We experimentally demonstrate the competitive performance of the proposed model on query-by-string word spotting for both, handwritten and real scene word images. We also provide the results for query-by-example word spotting, although it is not the main focus of this work.
Address Lausanne; Suissa; September 2021
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ICDAR
Notes DAG; 600.121; 600.140; 110.312 Approved no
Call Number Admin @ si @ RMG2021 Serial 3572
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Author Sanket Biswas; Pau Riba; Josep Llados; Umapada Pal
Title DocSynth: A Layout Guided Approach for Controllable Document Image Synthesis Type Conference Article
Year 2021 Publication (up) 16th International Conference on Document Analysis and Recognition Abbreviated Journal
Volume 12823 Issue Pages 555–568
Keywords
Abstract Despite significant progress on current state-of-the-art image generation models, synthesis of document images containing multiple and complex object layouts is a challenging task. This paper presents a novel approach, called DocSynth, to automatically synthesize document images based on a given layout. In this work, given a spatial layout (bounding boxes with object categories) as a reference by the user, our proposed DocSynth model learns to generate a set of realistic document images consistent with the defined layout. Also, this framework has been adapted to this work as a superior baseline model for creating synthetic document image datasets for augmenting real data during training for document layout analysis tasks. Different sets of learning objectives have been also used to improve the model performance. Quantitatively, we also compare the generated results of our model with real data using standard evaluation metrics. The results highlight that our model can successfully generate realistic and diverse document images with multiple objects. We also present a comprehensive qualitative analysis summary of the different scopes of synthetic image generation tasks. Lastly, to our knowledge this is the first work of its kind.
Address Lausanne; Suissa; September 2021
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes DAG; 600.121; 600.140; 110.312 Approved no
Call Number Admin @ si @ BRL2021a Serial 3573
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Author Ruben Tito; Dimosthenis Karatzas; Ernest Valveny
Title Document Collection Visual Question Answering Type Conference Article
Year 2021 Publication (up) 16th International Conference on Document Analysis and Recognition Abbreviated Journal
Volume 12822 Issue Pages 778-792
Keywords Document collection; Visual Question Answering
Abstract Current tasks and methods in Document Understanding aims to process documents as single elements. However, documents are usually organized in collections (historical records, purchase invoices), that provide context useful for their interpretation. To address this problem, we introduce Document Collection Visual Question Answering (DocCVQA) a new dataset and related task, where questions are posed over a whole collection of document images and the goal is not only to provide the answer to the given question, but also to retrieve the set of documents that contain the information needed to infer the answer. Along with the dataset we propose a new evaluation metric and baselines which provide further insights to the new dataset and task.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ICDAR
Notes DAG; 600.121 Approved no
Call Number Admin @ si @ TKV2021 Serial 3622
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Author Ruben Tito; Minesh Mathew; C.V. Jawahar; Ernest Valveny; Dimosthenis Karatzas
Title ICDAR 2021 Competition on Document Visual Question Answering Type Conference Article
Year 2021 Publication (up) 16th International Conference on Document Analysis and Recognition Abbreviated Journal
Volume Issue Pages 635-649
Keywords
Abstract In this report we present results of the ICDAR 2021 edition of the Document Visual Question Challenges. This edition complements the previous tasks on Single Document VQA and Document Collection VQA with a newly introduced on Infographics VQA. Infographics VQA is based on a new dataset of more than 5, 000 infographics images and 30, 000 question-answer pairs. The winner methods have scored 0.6120 ANLS in Infographics VQA task, 0.7743 ANLSL in Document Collection VQA task and 0.8705 ANLS in Single Document VQA. We present a summary of the datasets used for each task, description of each of the submitted methods and the results and analysis of their performance. A summary of the progress made on Single Document VQA since the first edition of the DocVQA 2020 challenge is also presented.
Address VIRTUAL; Lausanne; Suissa; September 2021
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ICDAR
Notes DAG; 600.121 Approved no
Call Number Admin @ si @ TMJ2021 Serial 3624
Permanent link to this record
 

 
Author Albert Suso; Pau Riba; Oriol Ramos Terrades; Josep Llados
Title A Self-supervised Inverse Graphics Approach for Sketch Parametrization Type Conference Article
Year 2021 Publication (up) 16th International Conference on Document Analysis and Recognition Abbreviated Journal
Volume 12916 Issue Pages 28-42
Keywords
Abstract The study of neural generative models of handwritten text and human sketches is a hot topic in the computer vision field. The landmark SketchRNN provided a breakthrough by sequentially generating sketches as a sequence of waypoints, and more recent articles have managed to generate fully vector sketches by coding the strokes as Bézier curves. However, the previous attempts with this approach need them all a ground truth consisting in the sequence of points that make up each stroke, which seriously limits the datasets the model is able to train in. In this work, we present a self-supervised end-to-end inverse graphics approach that learns to embed each image to its best fit of Bézier curves. The self-supervised nature of the training process allows us to train the model in a wider range of datasets, but also to perform better after-training predictions by applying an overfitting process on the input binary image. We report qualitative an quantitative evaluations on the MNIST and the Quick, Draw! datasets.
Address Lausanne; Suissa; September 2021
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ICDAR
Notes DAG; 600.121 Approved no
Call Number Admin @ si @ SRR2021 Serial 3675
Permanent link to this record
 

 
Author Sanket Biswas; Pau Riba; Josep Llados; Umapada Pal
Title Graph-Based Deep Generative Modelling for Document Layout Generation Type Conference Article
Year 2021 Publication (up) 16th International Conference on Document Analysis and Recognition Abbreviated Journal
Volume 12917 Issue Pages 525-537
Keywords
Abstract One of the major prerequisites for any deep learning approach is the availability of large-scale training data. When dealing with scanned document images in real world scenarios, the principal information of its content is stored in the layout itself. In this work, we have proposed an automated deep generative model using Graph Neural Networks (GNNs) to generate synthetic data with highly variable and plausible document layouts that can be used to train document interpretation systems, in this case, specially in digital mailroom applications. It is also the first graph-based approach for document layout generation task experimented on administrative document images, in this case, invoices.
Address Lausanne; Suissa; September 2021
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes DAG; 600.121; 600.140; 110.312 Approved no
Call Number Admin @ si @ BRL2021 Serial 3676
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