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Author Oriol Pujol; Debora Gil; Petia Radeva
Title Fundamentals of Stop and Go active models Type Journal Article
Year 2005 Publication Image and Vision Computing Abbreviated Journal
Volume 23 Issue 8 Pages 681-691
Keywords Deformable models; Geodesic snakes; Region-based segmentation
Abstract (down) An efficient snake formulation should conform to the idea of picking the smoothest curve among all the shapes approximating an object of interest. In current geodesic snakes, the regularizing curvature also affects the convergence stage, hindering the latter at concave regions. In the present work, we make use of characteristic functions to define a novel geodesic formulation that decouples regularity and convergence. This term decoupling endows the snake with higher adaptability to non-convex shapes. Convergence is ensured by splitting the definition of the external force into an attractive vector field and a repulsive one. In our paper, we propose to use likelihood maps as approximation of characteristic functions of object appearance. The better efficiency and accuracy of our decoupled scheme are illustrated in the particular case of feature space-based segmentation.
Address
Corporate Author Thesis
Publisher Butterworth-Heinemann Place of Publication Newton, MA, USA Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0262-8856 ISBN Medium
Area Expedition Conference
Notes IAM;MILAB;HuPBA Approved no
Call Number IAM @ iam @ PGR2005 Serial 1629
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Author Mikhail Mozerov; Ariel Amato; Xavier Roca; Jordi Gonzalez
Title Solving the Multi Object Occlusion Problem in a Multiple Camera Tracking System Type Journal
Year 2009 Publication Pattern Recognition and Image Analysis Abbreviated Journal
Volume 19 Issue 1 Pages 165-171
Keywords
Abstract (down) An efficient method to overcome adverse effects of occlusion upon object tracking is presented. The method is based on matching paths of objects in time and solves a complex occlusion-caused problem of merging separate segments of the same path.
Address
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 1054-6618 ISBN Medium
Area Expedition Conference
Notes ISE Approved no
Call Number ISE @ ise @ MAR2009a Serial 1160
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Author Katerine Diaz; Francesc J. Ferri; W. Diaz
Title Fast Approximated Discriminative Common Vectors using rank-one SVD updates Type Conference Article
Year 2013 Publication 20th International Conference On Neural Information Processing Abbreviated Journal
Volume 8228 Issue III Pages 368-375
Keywords
Abstract (down) An efficient incremental approach to the discriminative common vector (DCV) method for dimensionality reduction and classification is presented. The proposal consists of a rank-one update along with an adaptive restriction on the rank of the null space which leads to an approximate but convenient solution. The algorithm can be implemented very efficiently in terms of matrix operations and space complexity, which enables its use in large-scale dynamic application domains. Deep comparative experimentation using publicly available high dimensional image datasets has been carried out in order to properly assess the proposed algorithm against several recent incremental formulations.
K. Diaz-Chito, F.J. Ferri, W. Diaz
Address Daegu; Korea; November 2013
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-42050-4 Medium
Area Expedition Conference ICONIP
Notes ADAS Approved no
Call Number Admin @ si @ DFD2013 Serial 2439
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Author L.Tarazon; D. Perez; N. Serrano; V. Alabau; Oriol Ramos Terrades; A. Sanchis; A. Juan
Title Confidence Measures for Error Correction in Interactive Transcription of Handwritten Text Type Conference Article
Year 2009 Publication 15th International Conference on Image Analysis and Processing Abbreviated Journal
Volume 5716 Issue Pages 567-574
Keywords
Abstract (down) An effective approach to transcribe old text documents is to follow an interactive-predictive paradigm in which both, the system is guided by the human supervisor, and the supervisor is assisted by the system to complete the transcription task as efficiently as possible. In this paper, we focus on a particular system prototype called GIDOC, which can be seen as a first attempt to provide user-friendly, integrated support for interactive-predictive page layout analysis, text line detection and handwritten text transcription. More specifically, we focus on the handwriting recognition part of GIDOC, for which we propose the use of confidence measures to guide the human supervisor in locating possible system errors and deciding how to proceed. Empirical results are reported on two datasets showing that a word error rate not larger than a 10% can be achieved by only checking the 32% of words that are recognised with less confidence.
Address Vietri sul Mare, Italy
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-04145-7 Medium
Area Expedition Conference ICIAP
Notes DAG Approved no
Call Number Admin @ si @ TPS2009 Serial 1871
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Author David Aldavert; Ricardo Toledo; Arnau Ramisa; Ramon Lopez de Mantaras
Title Visual Registration Method For A Low Cost Robot: Computer Vision Systems Type Conference Article
Year 2009 Publication 7th International Conference on Computer Vision Systems Abbreviated Journal
Volume 5815 Issue Pages 204–214
Keywords
Abstract (down) An autonomous mobile robot must face the correspondence or data association problem in order to carry out tasks like place recognition or unknown environment mapping. In order to put into correspondence two maps, most methods estimate the transformation relating the maps from matches established between low level feature extracted from sensor data. However, finding explicit matches between features is a challenging and computationally expensive task. In this paper, we propose a new method to align obstacle maps without searching explicit matches between features. The maps are obtained from a stereo pair. Then, we use a vocabulary tree approach to identify putative corresponding maps followed by the Newton minimization algorithm to find the transformation that relates both maps. The proposed method is evaluated in a typical office environment showing good performance.
Address Belgica
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-04666-7 Medium
Area Expedition Conference ICVS
Notes ADAS Approved no
Call Number Admin @ si @ ATR2009b Serial 1247
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Author David Vazquez; Jiaolong Xu; Sebastian Ramos; Antonio Lopez; Daniel Ponsa
Title Weakly Supervised Automatic Annotation of Pedestrian Bounding Boxes Type Conference Article
Year 2013 Publication CVPR Workshop on Ground Truth – What is a good dataset? Abbreviated Journal
Volume Issue Pages 706 - 711
Keywords Pedestrian Detection; Domain Adaptation
Abstract (down) Among the components of a pedestrian detector, its trained pedestrian classifier is crucial for achieving the desired performance. The initial task of the training process consists in collecting samples of pedestrians and background, which involves tiresome manual annotation of pedestrian bounding boxes (BBs). Thus, recent works have assessed the use of automatically collected samples from photo-realistic virtual worlds. However, learning from virtual-world samples and testing in real-world images may suffer the dataset shift problem. Accordingly, in this paper we assess an strategy to collect samples from the real world and retrain with them, thus avoiding the dataset shift, but in such a way that no BBs of real-world pedestrians have to be provided. In particular, we train a pedestrian classifier based on virtual-world samples (no human annotation required). Then, using such a classifier we collect pedestrian samples from real-world images by detection. After, a human oracle rejects the false detections efficiently (weak annotation). Finally, a new classifier is trained with the accepted detections. We show that this classifier is competitive with respect to the counterpart trained with samples collected by manually annotating hundreds of pedestrian BBs.
Address Portland; Oregon; June 2013
Corporate Author Thesis
Publisher IEEE Place of Publication Editor
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference CVPRW
Notes ADAS; 600.054; 600.057; 601.217 Approved no
Call Number ADAS @ adas @ VXR2013a Serial 2219
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Author Debora Gil; Petia Radeva
Title A Regularized Curvature Flow Designed for a Selective Shape Restoration Type Journal Article
Year 2004 Publication IEEE Transactions on Image Processing Abbreviated Journal
Volume 13 Issue Pages 1444–1458
Keywords Geometric flows, nonlinear filtering, shape recovery.
Abstract (down) Among all filtering techniques, those based exclu- sively on image level sets (geometric flows) have proven to be the less sensitive to the nature of noise and the most contrast preserving. A common feature to existent curvature flows is that they penalize high curvature, regardless of the curve regularity. This constitutes a major drawback since curvature extreme values are standard descriptors of the contour geometry. We argue that an operator designed with shape recovery purposes should include a term penalizing irregularity in the curvature rather than its magnitude. To this purpose, we present a novel geometric flow that includes a function that measures the degree of local irregularity present in the curve. A main advantage is that it achieves non-trivial steady states representing a smooth model of level curves in a noisy image. Performance of our approach is compared to classical filtering techniques in terms of quality in the restored image/shape and asymptotic behavior. We empirically prove that our approach is the technique that achieves the best compromise between image quality and evolution stabilization.
Address
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
Notes IAM;MILAB Approved no
Call Number BCNPCL @ bcnpcl @ GiR2004b Serial 491
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Author Alvaro Peris; Marc Bolaños; Petia Radeva; Francisco Casacuberta
Title Video Description Using Bidirectional Recurrent Neural Networks Type Conference Article
Year 2016 Publication 25th International Conference on Artificial Neural Networks Abbreviated Journal
Volume 2 Issue Pages 3-11
Keywords Video description; Neural Machine Translation; Birectional Recurrent Neural Networks; LSTM; Convolutional Neural Networks
Abstract (down) Although traditionally used in the machine translation field, the encoder-decoder framework has been recently applied for the generation of video and image descriptions. The combination of Convolutional and Recurrent Neural Networks in these models has proven to outperform the previous state of the art, obtaining more accurate video descriptions. In this work we propose pushing further this model by introducing two contributions into the encoding stage. First, producing richer image representations by combining object and location information from Convolutional Neural Networks and second, introducing Bidirectional Recurrent Neural Networks for capturing both forward and backward temporal relationships in the input frames.
Address Barcelona; September 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 ICANN
Notes MILAB; Approved no
Call Number Admin @ si @ PBR2016 Serial 2833
Permanent link to this record
 
 
Author Michael Teutsch; Angel Sappa; Riad I. Hammoud
Title Cross-Spectral Image Processing Type Book Chapter
Year 2022 Publication Computer Vision in the Infrared Spectrum. Synthesis Lectures on Computer Vision Abbreviated Journal
Volume Issue Pages 23-34
Keywords
Abstract (down) Although this book is on IR computer vision and its main focus lies on IR image and video processing and analysis, a special attention is dedicated to cross-spectral image processing due to the increasing number of publications and applications in this domain. In these cross-spectral frameworks, IR information is used together with information from other spectral bands to tackle some specific problems by developing more robust solutions. Tasks considered for cross-spectral processing are for instance dehazing, segmentation, vegetation index estimation, or face recognition. This increasing number of applications is motivated by cross- and multi-spectral camera setups available already on the market like for example smartphones, remote sensing multispectral cameras, or multi-spectral cameras for automotive systems or drones. In this chapter, different cross-spectral image processing techniques will be reviewed together with possible applications. Initially, image registration approaches for the cross-spectral case are reviewed: the registration stage is the first image processing task, which is needed to align images acquired by different sensors within the same reference coordinate system. Then, recent cross-spectral image colorization approaches, which are intended to colorize infrared images for different applications are presented. Finally, the cross-spectral image enhancement problem is tackled by including guided super resolution techniques, image dehazing approaches, cross-spectral filtering and edge detection. Figure 3.1 illustrates cross-spectral image processing stages as well as their possible connections. Table 3.1 presents some of the available public cross-spectral datasets generally used as reference data to evaluate cross-spectral image registration, colorization, enhancement, or exploitation results.
Address
Corporate Author Thesis
Publisher Springer Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title SLCV
Series Volume Series Issue Edition
ISSN ISBN 978-3-031-00698-2 Medium
Area Expedition Conference
Notes MSIAU; MACO Approved no
Call Number Admin @ si @ TSH2022b Serial 3805
Permanent link to this record
 
 
Author Jaume Gibert; Ernest Valveny
Title Graph Embedding based on Nodes Attributes Representatives and a Graph of Words Representation. Type Conference Article
Year 2010 Publication 13th International worshop on structural and syntactic pattern recognition and 8th international worshop on statistical pattern recognition Abbreviated Journal
Volume 6218 Issue Pages 223–232
Keywords
Abstract (down) Although graph embedding has recently been used to extend statistical pattern recognition techniques to the graph domain, some existing embeddings are usually computationally expensive as they rely on classical graph-based operations. In this paper we present a new way to embed graphs into vector spaces by first encapsulating the information stored in the original graph under another graph representation by clustering the attributes of the graphs to be processed. This new representation makes the association of graphs to vectors an easy step by just arranging both node attributes and the adjacency matrix in the form of vectors. To test our method, we use two different databases of graphs whose nodes attributes are of different nature. A comparison with a reference method permits to show that this new embedding is better in terms of classification rates, while being much more faster.
Address
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor In E.R. Hancock, R.C. Wilson, T. Windeatt, I. Ulusoy and F. Escolano,
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-14979-5 Medium
Area Expedition Conference S+SSPR
Notes DAG Approved no
Call Number DAG @ dag @ GiV2010 Serial 1416
Permanent link to this record
 
 
Author Lei Kang; Pau Riba; Yaxing Wang; Marçal Rusiñol; Alicia Fornes; Mauricio Villegas
Title GANwriting: Content-Conditioned Generation of Styled Handwritten Word Images Type Conference Article
Year 2020 Publication 16th European Conference on Computer Vision Abbreviated Journal
Volume Issue Pages
Keywords
Abstract (down) Although current image generation methods have reached impressive quality levels, they are still unable to produce plausible yet diverse images of handwritten words. On the contrary, when writing by hand, a great variability is observed across different writers, and even when analyzing words scribbled by the same individual, involuntary variations are conspicuous. In this work, we take a step closer to producing realistic and varied artificially rendered handwritten words. We propose a novel method that is able to produce credible handwritten word images by conditioning the generative process with both calligraphic style features and textual content. Our generator is guided by three complementary learning objectives: to produce realistic images, to imitate a certain handwriting style and to convey a specific textual content. Our model is unconstrained to any predefined vocabulary, being able to render whatever input word. Given a sample writer, it is also able to mimic its calligraphic features in a few-shot setup. We significantly advance over prior art and demonstrate with qualitative, quantitative and human-based evaluations the realistic aspect of our synthetically produced images.
Address Virtual; August 2020
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 ECCV
Notes DAG; 600.140; 600.121; 600.129 Approved no
Call Number Admin @ si @ KPW2020 Serial 3426
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Author Thierry Brouard; Jordi Gonzalez; Caifeng Shan; Massimo Piccardi; Larry S. Davis
Title Special issue on background modeling for foreground detection in real-world dynamic scenes Type Journal Article
Year 2014 Publication Machine Vision and Applications Abbreviated Journal MVAP
Volume 25 Issue 5 Pages 1101-1103
Keywords
Abstract (down) Although background modeling and foreground detection are not mandatory steps for computer vision applications, they may prove useful as they separate the primal objects usually called “foreground” from the remaining part of the scene called “background”, and permits different algorithmic treatment in the video processing field such as video surveillance, optical motion capture, multimedia applications, teleconferencing and human–computer interfaces. Conventional background modeling methods exploit the temporal variation of each pixel to model the background, and the foreground detection is made using change detection. The last decade witnessed very significant publications on background modeling but recently new applications in which background is not static, such as recordings taken from mobile devices or Internet videos, need new developments to detect robustly moving objects in challenging environments. Thus, effective methods for robustness to deal both with dynamic backgrounds, i
Address
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0932-8092 ISBN Medium
Area Expedition Conference
Notes ISE; 600.078 Approved no
Call Number BGS2014a Serial 2411
Permanent link to this record
 
 
Author Mikhail Mozerov; Joost Van de Weijer
Title Improved Recursive Geodesic Distance Computation for Edge Preserving Filter Type Journal Article
Year 2017 Publication IEEE Transactions on Image Processing Abbreviated Journal TIP
Volume 26 Issue 8 Pages 3696 - 3706
Keywords Geodesic distance filter; color image filtering; image enhancement
Abstract (down) All known recursive filters based on the geodesic distance affinity are realized by two 1D recursions applied in two orthogonal directions of the image plane. The 2D extension of the filter is not valid and has theoretically drawbacks, which lead to known artifacts. In this paper, a maximum influence propagation method is proposed to approximate the 2D extension for the
geodesic distance-based recursive filter. The method allows to partially overcome the drawbacks of the 1D recursion approach. We show that our improved recursion better approximates the true geodesic distance filter, and the application of this improved filter for image denoising outperforms the existing recursive implementation of the geodesic distance. As an application,
we consider a geodesic distance-based filter for image denoising.
Experimental evaluation of our denoising method demonstrates comparable and for several test images better results, than stateof-the-art approaches, while our algorithm is considerably fasterwith computational complexity O(8P).
Address
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
Notes LAMP; ISE; 600.120; 600.098; 600.119 Approved no
Call Number Admin @ si @ Moz2017 Serial 2921
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Author Dimosthenis Karatzas; V. Poulain d'Andecy; Marçal Rusiñol
Title Human-Document Interaction – a new frontier for document image analysis Type Conference Article
Year 2016 Publication 12th IAPR Workshop on Document Analysis Systems Abbreviated Journal
Volume Issue Pages 369-374
Keywords
Abstract (down) All indications show that paper documents will not cede in favour of their digital counterparts, but will instead be used increasingly in conjunction with digital information. An open challenge is how to seamlessly link the physical with the digital – how to continue taking advantage of the important affordances of paper, without missing out on digital functionality. This paper
presents the authors’ experience with developing systems for Human-Document Interaction based on augmented document interfaces and examines new challenges and opportunities arising for the document image analysis field in this area. The system presented combines state of the art camera-based document
image analysis techniques with a range of complementary tech-nologies to offer fluid Human-Document Interaction. Both fixed and nomadic setups are discussed that have gone through user testing in real-life environments, and use cases are presented that span the spectrum from business to educational application
Address Santorini; Greece; April 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 DAS
Notes DAG; 600.084; 600.077 Approved no
Call Number KPR2016 Serial 2756
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Author Pierluigi Casale; Oriol Pujol; Petia Radeva
Title Classyfing Agitation in Sedated ICU Patients Type Conference Article
Year 2010 Publication Medical Image Computing in Catalunya: Graduate Student Workshop Abbreviated Journal
Volume Issue Pages 19–20
Keywords
Abstract (down) Agitation is a serious problem in sedated intensive care unit (ICU) patients. In this work, standard machine learning techniques working on wearable accelerometer data have been used to classifying agitation levels achieving very good classification performances.
Address Girona
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 MICCAT
Notes MILAB;HUPBA Approved no
Call Number BCNPCL @ bcnpcl @ COR2010 Serial 1467
Permanent link to this record