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Author Helena Muñoz; Fernando Vilariño; Dimosthenis Karatzas
Title Eye-Movements During Information Extraction from Administrative Documents Type Conference Article
Year 2019 Publication (up) International Conference on Document Analysis and Recognition Workshops Abbreviated Journal
Volume Issue Pages 6-9
Keywords
Abstract A key aspect of digital mailroom processes is the extraction of relevant information from administrative documents. More often than not, the extraction process cannot be fully automated, and there is instead an important amount of manual intervention. In this work we study the human process of information extraction from invoice document images. We explore whether the gaze of human annotators during an manual information extraction process could be exploited towards reducing the manual effort and automating the process. To this end, we perform an eye-tracking experiment replicating real-life interfaces for information extraction. Through this pilot study we demonstrate that relevant areas in the document can be identified reliably through automatic fixation classification, and the obtained models generalize well to new subjects. Our findings indicate that it is in principle possible to integrate the human in the document image analysis loop, making use of the scanpath to automate the extraction process or verify extracted information.
Address Sydney; Australia; September 2019
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Area Expedition Conference ICDARW
Notes DAG; 600.140; 600.121; 600.129;SIAI Approved no
Call Number Admin @ si @ MVK2019 Serial 3336
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Author Jose Antonio Rodriguez; Florent Perronnin
Title Local Gradient Histogram Features for Word Spotting in Unconstrained Handwritten Documents Type Conference Article
Year 2008 Publication (up) International Conference on Frontiers in Handwriting Recognition Abbreviated Journal
Volume Issue Pages 7–12
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Abstract
Address Montreal (Canada)
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Publisher Place of Publication Editor
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ISSN ISBN Medium
Area Expedition Conference ICFHR
Notes Approved no
Call Number Admin @ si @ RoP2008b Serial 1066
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Author Jose Antonio Rodriguez; Florent Perronnin
Title Score Normalization for Hmm-based Word Spotting Using Universal Background Model Type Conference Article
Year 2008 Publication (up) International Conference on Frontiers in Handwriting Recognition Abbreviated Journal
Volume Issue Pages 82–87
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Abstract
Address Montreal (Canada)
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Publisher Place of Publication Editor
Language Summary Language Original Title
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ISSN ISBN Medium
Area Expedition Conference ICFHR
Notes Approved no
Call Number Admin @ si @ RoP2008c Serial 1067
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Author Partha Pratim Roy; Umapada Pal; Josep Llados
Title Morphology Based Handwritten Line Segmentation using Foreground and Background Information Type Conference Article
Year 2008 Publication (up) International Conference on Frontiers in Handwriting Recognition, Abbreviated Journal
Volume Issue Pages 241–246
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Abstract
Address Montreal (Canada)
Corporate Author Thesis
Publisher Place of Publication Editor
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Series Editor Series Title Abbreviated Series Title
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ISSN ISBN Medium
Area Expedition Conference ICFHR
Notes DAG Approved no
Call Number DAG @ dag @ RPL2008a Serial 1050
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Author Giacomo Magnifico; Beata Megyesi; Mohamed Ali Souibgui; Jialuo Chen; Alicia Fornes
Title Lost in Transcription of Graphic Signs in Ciphers Type Conference Article
Year 2022 Publication (up) International Conference on Historical Cryptology (HistoCrypt 2022) Abbreviated Journal
Volume Issue Pages 153-158
Keywords transcription of ciphers; hand-written text recognition of symbols; graphic signs
Abstract Hand-written Text Recognition techniques with the aim to automatically identify and transcribe hand-written text have been applied to historical sources including ciphers. In this paper, we compare the performance of two machine learning architectures, an unsupervised method based on clustering and a deep learning method with few-shot learning. Both models are tested on seen and unseen data from historical ciphers with different symbol sets consisting of various types of graphic signs. We compare the models and highlight their differences in performance, with their advantages and shortcomings.
Address Amsterdam, Netherlands, June 20-22, 2022
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Area Expedition Conference HystoCrypt
Notes DAG; 600.121; 600.162; 602.230; 600.140 Approved no
Call Number Admin @ si @ MBS2022 Serial 3731
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Author Angel Sappa; David Geronimo; Fadi Dornaika; Antonio Lopez
Title Real Time Vehicle Pose Using On-Board Stereo Vision System Type Conference Article
Year 2006 Publication (up) International Conference on Image Analysis and Recognition Abbreviated Journal ICIAR
Volume Issue LNCS 4142 Pages 205–216
Keywords
Abstract This paper presents a robust technique for a real time estimation of both camera’s position and orientation—referred as pose. A commercial stereo vision system is used. Unlike previous approaches, it can be used either for urban or highway scenarios. The proposed technique consists of two stages. Initially, a compact 2D representation of the original 3D data points is computed. Then, a RANSAC based least squares approach is used for fitting a plane to the road. At the same time,
relative camera’s position and orientation are computed. The proposed technique is intended to be used on a driving assistance scheme for applications such as obstacle or pedestrian detection. Experimental results on urban environments with different road geometries are presented.
Address
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Notes ADAS Approved no
Call Number ADAS @ adas @ SGD2006b Serial 671
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Author Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez
Title An Iterative Multiresolution Scheme for SFM Type Conference Article
Year 2006 Publication (up) International Conference on Image Analysis and Recognition Abbreviated Journal ICIAR 2006
Volume LNCS 4141 Issue 1 Pages 804–815
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Address
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Notes ADAS Approved no
Call Number ADAS @ adas @ JSL2006c Serial 704
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Author Joaquin Salas; P. Martinez; Jordi Gonzalez
Title Background Updating with the Use of Intrinsic Curves Type Book Chapter
Year 2006 Publication (up) International Conference on Image Analysis and Recognition (ICIAR´06), LNCS 4141 (A. Campilho et al., eds.), 1: 731–742, ISBN 978–3–540–44891–4 Abbreviated Journal
Volume Issue Pages
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Address
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Notes Approved no
Call Number ISE @ ise @ SMG2006 Serial 768
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Author Bogdan Raducanu; Jordi Vitria
Title A Robust Particle Filter-Based Face Tracker Using Combination of Color and Geometric Information Type Book Chapter
Year 2006 Publication (up) International Conference on Image Analysis and Recognition (ICIAR´06), LNCS 4141 (A. Campilho et al., eds.), 1: 922–933 Abbreviated Journal
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Address
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Notes OR;MV Approved no
Call Number BCNPCL @ bcnpcl @ RaV2006c Serial 715
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Author Fadi Dornaika; Angel Sappa
Title SFM for Planar Scenes: a Direct and Robust Approach Type Miscellaneous
Year 2005 Publication (up) International Conference on Informatics in Control, Automation and Robotics (ICINCO 2005) Abbreviated Journal
Volume Issue Pages
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Address Barcelona (Spain)
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Notes ADAS Approved no
Call Number ADAS @ adas @ DoS2005a Serial 559
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Author Zhong Jin; Zhen Lou; Jing-Yu Yang; Quan-sen Sun
Title Face detection using template matching and skin color information Type Miscellaneous
Year 2005 Publication (up) International Conference on Intelligent Computing, 636–645 Abbreviated Journal
Volume Issue Pages
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Address Hefei (China)
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Notes Approved no
Call Number Admin @ si @ JLY2005 Serial 627
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Author R. de Nijs; Sebastian Ramos; Gemma Roig; Xavier Boix; Luc Van Gool; K. Kühnlenz.
Title On-line Semantic Perception Using Uncertainty Type Conference Article
Year 2012 Publication (up) International Conference on Intelligent Robots and Systems Abbreviated Journal IROS
Volume Issue Pages 4185-4191
Keywords Semantic Segmentation
Abstract Visual perception capabilities are still highly unreliable in unconstrained settings, and solutions might not beaccurate in all regions of an image. Awareness of the uncertainty of perception is a fundamental requirement for proper high level decision making in a robotic system. Yet, the uncertainty measure is often sacrificed to account for dependencies between object/region classifiers. This is the case of Conditional Random Fields (CRFs), the success of which stems from their ability to infer the most likely world configuration, but they do not directly allow to estimate the uncertainty of the solution. In this paper, we consider the setting of assigning semantic labels to the pixels of an image sequence. Instead of using a CRF, we employ a Perturb-and-MAP Random Field, a recently introduced probabilistic model that allows performing fast approximate sampling from its probability density function. This allows to effectively compute the uncertainty of the solution, indicating the reliability of the most likely labeling in each region of the image. We report results on the CamVid dataset, a standard benchmark for semantic labeling of urban image sequences. In our experiments, we show the benefits of exploiting the uncertainty by putting more computational effort on the regions of the image that are less reliable, and use more efficient techniques for other regions, showing little decrease of performance
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Notes ADAS Approved no
Call Number ADAS @ adas @ NRR2012 Serial 2378
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Author Miguel Oliveira; L. Seabra Lopes; G. Hyun Lim; S. Hamidreza Kasaei; Angel Sappa; A. Tom
Title Concurrent Learning of Visual Codebooks and Object Categories in Openended Domains Type Conference Article
Year 2015 Publication (up) International Conference on Intelligent Robots and Systems Abbreviated Journal
Volume Issue Pages 2488 - 2495
Keywords Visual Learning; Computer Vision; Autonomous Agents
Abstract In open-ended domains, robots must continuously learn new object categories. When the training sets are created offline, it is not possible to ensure their representativeness with respect to the object categories and features the system will find when operating online. In the Bag of Words model, visual codebooks are constructed from training sets created offline. This might lead to non-discriminative visual words and, as a consequence, to poor recognition performance. This paper proposes a visual object recognition system which concurrently learns in an incremental and online fashion both the visual object category representations as well as the codebook words used to encode them. The codebook is defined using Gaussian Mixture Models which are updated using new object views. The approach contains similarities with the human visual object recognition system: evidence suggests that the development of recognition capabilities occurs on multiple levels and is sustained over large periods of time. Results show that the proposed system with concurrent learning of object categories and codebooks is capable of learning more categories, requiring less examples, and with similar accuracies, when compared to the classical Bag of Words approach using offline constructed codebooks.
Address Hamburg; Germany; October 2015
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Notes ADAS; 600.076 Approved no
Call Number Admin @ si @ OSL2015 Serial 2664
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Author Yi Xiao; Felipe Codevilla; Diego Porres; Antonio Lopez
Title Scaling Vision-Based End-to-End Autonomous Driving with Multi-View Attention Learning Type Conference Article
Year 2023 Publication (up) International Conference on Intelligent Robots and Systems Abbreviated Journal
Volume Issue Pages
Keywords
Abstract On end-to-end driving, human driving demonstrations are used to train perception-based driving models by imitation learning. This process is supervised on vehicle signals (e.g., steering angle, acceleration) but does not require extra costly supervision (human labeling of sensor data). As a representative of such vision-based end-to-end driving models, CILRS is commonly used as a baseline to compare with new driving models. So far, some latest models achieve better performance than CILRS by using expensive sensor suites and/or by using large amounts of human-labeled data for training. Given the difference in performance, one may think that it is not worth pursuing vision-based pure end-to-end driving. However, we argue that this approach still has great value and potential considering cost and maintenance. In this paper, we present CIL++, which improves on CILRS by both processing higher-resolution images using a human-inspired HFOV as an inductive bias and incorporating a proper attention mechanism. CIL++ achieves competitive performance compared to models which are more costly to develop. We propose to replace CILRS with CIL++ as a strong vision-based pure end-to-end driving baseline supervised by only vehicle signals and trained by conditional imitation learning.
Address Detroit; USA; October 2023
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Notes ADAS Approved no
Call Number Admin @ si @ XCP2023 Serial 3930
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Author Marc Masana; Joost Van de Weijer; Andrew Bagdanov
Title On-the-fly Network pruning for object detection Type Conference Article
Year 2016 Publication (up) International conference on learning representations Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Object detection with deep neural networks is often performed by passing a few
thousand candidate bounding boxes through a deep neural network for each image.
These bounding boxes are highly correlated since they originate from the same
image. In this paper we investigate how to exploit feature occurrence at the image scale to prune the neural network which is subsequently applied to all bounding boxes. We show that removing units which have near-zero activation in the image allows us to significantly reduce the number of parameters in the network. Results on the PASCAL 2007 Object Detection Challenge demonstrate that up to 40% of units in some fully-connected layers can be entirely eliminated with little change in the detection result.
Address Puerto Rico; May 2016
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Area Expedition Conference ICLR
Notes LAMP; 600.068; 600.106; 600.079 Approved no
Call Number Admin @ si @MWB2016 Serial 2758
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