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| Author | Fernando Barrera; Felipe Lumbreras; Angel Sappa | ||||
| Title | Multispectral Piecewise Planar Stereo using Manhattan-World Assumption | Type | Journal Article | ||
| Year | 2013 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 34 | Issue | 1 | Pages | 52-61 |
Keywords  |
Multispectral stereo rig; Dense disparity maps from multispectral stereo; Color and infrared images | ||||
| Abstract | This paper proposes a new framework for extracting dense disparity maps from a multispectral stereo rig. The system is constructed with an infrared and a color camera. It is intended to explore novel multispectral stereo matching approaches that will allow further extraction of semantic information. The proposed framework consists of three stages. Firstly, an initial sparse disparity map is generated by using a cost function based on feature matching in a multiresolution scheme. Then, by looking at the color image, a set of planar hypotheses is defined to describe the surfaces on the scene. Finally, the previous stages are combined by reformulating the disparity computation as a global minimization problem. The paper has two main contributions. The first contribution combines mutual information with a shape descriptor based on gradient in a multiresolution scheme. The second contribution, which is based on the Manhattan-world assumption, extracts a dense disparity representation using the graph cut algorithm. Experimental results in outdoor scenarios are provided showing the validity of the proposed framework. | ||||
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| Area | Expedition | Conference | |||
| Notes | ADAS; 600.054; 600.055; 605.203 | Approved | no | ||
| Call Number | Admin @ si @ BLS2013 | Serial | 2245 | ||
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| Author | Rahat Khan; Joost Van de Weijer; Dimosthenis Karatzas; Damien Muselet | ||||
| Title | Towards multispectral data acquisition with hand-held devices | Type | Conference Article | ||
| Year | 2013 | Publication | 20th IEEE International Conference on Image Processing | Abbreviated Journal | |
| Volume | Issue | Pages | 2053 - 2057 | ||
| Keywords |
Multispectral; mobile devices; color measurements | ||||
| Abstract | We propose a method to acquire multispectral data with handheld devices with front-mounted RGB cameras. We propose to use the display of the device as an illuminant while the camera captures images illuminated by the red, green and
blue primaries of the display. Three illuminants and three response functions of the camera lead to nine response values which are used for reflectance estimation. Results are promising and show that the accuracy of the spectral reconstruction improves in the range from 30-40% over the spectral reconstruction based on a single illuminant. Furthermore, we propose to compute sensor-illuminant aware linear basis by discarding the part of the reflectances that falls in the sensorilluminant null-space. We show experimentally that optimizing reflectance estimation on these new basis functions decreases the RMSE significantly over basis functions that are independent to sensor-illuminant. We conclude that, multispectral data acquisition is potentially possible with consumer hand-held devices such as tablets, mobiles, and laptops, opening up applications which are currently considered to be unrealistic. |
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| Address | Melbourne; Australia; September 2013 | ||||
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| Area | Expedition | Conference | ICIP | ||
| Notes | CIC; DAG; 600.048 | Approved | no | ||
| Call Number | Admin @ si @ KWK2013b | Serial | 2265 | ||
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| Author | Alicia Fornes; Anjan Dutta; Albert Gordo; Josep Llados | ||||
| Title | CVC-MUSCIMA: A Ground-Truth of Handwritten Music Score Images for Writer Identification and Staff Removal | Type | Journal Article | ||
| Year | 2012 | Publication | International Journal on Document Analysis and Recognition | Abbreviated Journal | IJDAR |
| Volume | 15 | Issue | 3 | Pages | 243-251 |
| Keywords |
Music scores; Handwritten documents; Writer identification; Staff removal; Performance evaluation; Graphics recognition; Ground truths | ||||
| Abstract | 0,405JCR
The analysis of music scores has been an active research field in the last decades. However, there are no publicly available databases of handwritten music scores for the research community. In this paper we present the CVC-MUSCIMA database and ground-truth of handwritten music score images. The dataset consists of 1,000 music sheets written by 50 different musicians. It has been especially designed for writer identification and staff removal tasks. In addition to the description of the dataset, ground-truth, partitioning and evaluation metrics, we also provide some base-line results for easing the comparison between different approaches. |
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1433-2833 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ FDG2012 | Serial | 2129 | ||
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| Author | Giuseppe De Gregorio; Sanket Biswas; Mohamed Ali Souibgui; Asma Bensalah; Josep Llados; Alicia Fornes; Angelo Marcelli | ||||
| Title | A Few Shot Multi-representation Approach for N-Gram Spotting in Historical Manuscripts | Type | Conference Article | ||
| Year | 2022 | Publication | Frontiers in Handwriting Recognition. International Conference on Frontiers in Handwriting Recognition (ICFHR2022) | Abbreviated Journal | |
| Volume | 13639 | Issue | Pages | 3-12 | |
| Keywords |
N-gram spotting; Few-shot learning; Multimodal understanding; Historical handwritten collections | ||||
| Abstract | Despite recent advances in automatic text recognition, the performance remains moderate when it comes to historical manuscripts. This is mainly because of the scarcity of available labelled data to train the data-hungry Handwritten Text Recognition (HTR) models. The Keyword Spotting System (KWS) provides a valid alternative to HTR due to the reduction in error rate, but it is usually limited to a closed reference vocabulary. In this paper, we propose a few-shot learning paradigm for spotting sequences of a few characters (N-gram) that requires a small amount of labelled training data. We exhibit that recognition of important n-grams could reduce the system’s dependency on vocabulary. In this case, an out-of-vocabulary (OOV) word in an input handwritten line image could be a sequence of n-grams that belong to the lexicon. An extensive experimental evaluation of our proposed multi-representation approach was carried out on a subset of Bentham’s historical manuscript collections to obtain some really promising results in this direction. | ||||
| Address | December 04 – 07, 2022; Hyderabad, India | ||||
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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ICFHR | ||
| Notes | DAG; 600.121; 600.162; 602.230; 600.140 | Approved | no | ||
| Call Number | Admin @ si @ GBS2022 | Serial | 3733 | ||
| Permanent link to this record | |||||
| Author | Manuel Carbonell; Mauricio Villegas; Alicia Fornes; Josep Llados | ||||
| Title | Joint Recognition of Handwritten Text and Named Entities with a Neural End-to-end Model | Type | Conference Article | ||
| Year | 2018 | Publication | 13th IAPR International Workshop on Document Analysis Systems | Abbreviated Journal | |
| Volume | Issue | Pages | 399-404 | ||
| Keywords |
Named entity recognition; Handwritten Text Recognition; neural networks | ||||
| Abstract | When extracting information from handwritten documents, text transcription and named entity recognition are usually faced as separate subsequent tasks. This has the disadvantage that errors in the first module affect heavily the
performance of the second module. In this work we propose to do both tasks jointly, using a single neural network with a common architecture used for plain text recognition. Experimentally, the work has been tested on a collection of historical marriage records. Results of experiments are presented to show the effect on the performance for different configurations: different ways of encoding the information, doing or not transfer learning and processing at text line or multi-line region level. The results are comparable to state of the art reported in the ICDAR 2017 Information Extraction competition, even though the proposed technique does not use any dictionaries, language modeling or post processing. |
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| Address | Vienna; Austria; April 2018 | ||||
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| Area | Expedition | Conference | DAS | ||
| Notes | DAG; 600.097; 603.057; 601.311; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ CVF2018 | Serial | 3170 | ||
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| Author | J.S. Cope; P.Remagnino; S.Mannan; Katerine Diaz; Francesc J. Ferri; P.Wilkin | ||||
| Title | Reverse Engineering Expert Visual Observations: From Fixations To The Learning Of Spatial Filters With A Neural-Gas Algorithm | Type | Journal Article | ||
| Year | 2013 | Publication | Expert Systems with Applications | Abbreviated Journal | EXWA |
| Volume | 40 | Issue | 17 | Pages | 6707-6712 |
| Keywords |
Neural gas; Expert vision; Eye-tracking; Fixations | ||||
| Abstract | Human beings can become experts in performing specific vision tasks, for example, doctors analysing medical images, or botanists studying leaves. With sufficient knowledge and experience, people can become very efficient at such tasks. When attempting to perform these tasks with a machine vision system, it would be highly beneficial to be able to replicate the process which the expert undergoes. Advances in eye-tracking technology can provide data to allow us to discover the manner in which an expert studies an image. This paper presents a first step towards utilizing these data for computer vision purposes. A growing-neural-gas algorithm is used to learn a set of Gabor filters which give high responses to image regions which a human expert fixated on. These filters can then be used to identify regions in other images which are likely to be useful for a given vision task. The algorithm is evaluated by learning filters for locating specific areas of plant leaves. | ||||
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| ISSN | 0957-4174 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ CRM2013 | Serial | 2438 | ||
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| Author | Parichehr Behjati Ardakani; Diego Velazquez; Josep M. Gonfaus; Pau Rodriguez; Xavier Roca; Jordi Gonzalez | ||||
| Title | Catastrophic interference in Disguised Face Recognition | Type | Conference Article | ||
| Year | 2019 | Publication | 9th Iberian Conference on Pattern Recognition and Image Analysis | Abbreviated Journal | |
| Volume | 11868 | Issue | Pages | 64-75 | |
| Keywords |
Neural network forgetness; Face recognition; Disguised Faces | ||||
| Abstract | It is commonly known the natural tendency of artificial neural networks to completely and abruptly forget previously known information when learning new information. We explore this behaviour in the context of Face Verification on the recently proposed Disguised Faces in the Wild dataset (DFW). We empirically evaluate several commonly used DCNN architectures on Face Recognition and distill some insights about the effect of sequential learning on distinct identities from different datasets, showing that the catastrophic forgetness phenomenon is present even in feature embeddings fine-tuned on different tasks from the original domain. | ||||
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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | IbPRIA | ||
| Notes | ISE; 600.098; 600.119 | Approved | no | ||
| Call Number | Admin @ si @ AVG2019 | Serial | 3416 | ||
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| Author | C. Gratin; Jordi Vitria; F. Moreso; D. Seron | ||||
| Title | Texture Classification using Neural Networks and Local Granulometries | Type | Conference Article | ||
| Year | 1994 | Publication | EURASIP Workshop, Mathematical Morphology and Its Applications to image Processing, J.Serra and P.Soille, editors | Abbreviated Journal | |
| Volume | Issue | Pages | 309-316 | ||
| Keywords |
Neural Networks; Granulometry; Kidney; Texture; Classication | ||||
| Abstract | |||||
| Address | Fointanebleau, France | ||||
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| Notes | OR;MV | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ GVM1994 | Serial | 110 | ||
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| Author | Asma Bensalah; Alicia Fornes; Cristina Carmona_Duarte; Josep Llados | ||||
| Title | Easing Automatic Neurorehabilitation via Classification and Smoothness Analysis | Type | Conference Article | ||
| Year | 2022 | Publication | Intertwining Graphonomics with Human Movements. 20th International Conference of the International Graphonomics Society, IGS 2022 | Abbreviated Journal | |
| Volume | 13424 | Issue | Pages | 336-348 | |
| Keywords |
Neurorehabilitation; Upper-lim; Movement classification; Movement smoothness; Deep learning; Jerk | ||||
| Abstract | Assessing the quality of movements for post-stroke patients during the rehabilitation phase is vital given that there is no standard stroke rehabilitation plan for all the patients. In fact, it depends basically on the patient’s functional independence and its progress along the rehabilitation sessions. To tackle this challenge and make neurorehabilitation more agile, we propose an automatic assessment pipeline that starts by recognising patients’ movements by means of a shallow deep learning architecture, then measuring the movement quality using jerk measure and related measures. A particularity of this work is that the dataset used is clinically relevant, since it represents movements inspired from Fugl-Meyer a well common upper-limb clinical stroke assessment scale for stroke patients. We show that it is possible to detect the contrast between healthy and patients movements in terms of smoothness, besides achieving conclusions about the patients’ progress during the rehabilitation sessions that correspond to the clinicians’ findings about each case. | ||||
| Address | June 7-9, 2022, Las Palmas de Gran Canaria, Spain | ||||
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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | IGS | ||
| Notes | DAG; 600.121; 600.162; 602.230; 600.140 | Approved | no | ||
| Call Number | Admin @ si @ BFC2022 | Serial | 3738 | ||
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| Author | Bhalaji Nagarajan; Ricardo Marques; Marcos Mejia; Petia Radeva | ||||
| Title | Class-conditional Importance Weighting for Deep Learning with Noisy Labels | Type | Conference Article | ||
| Year | 2022 | Publication | 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications | Abbreviated Journal | |
| Volume | 5 | Issue | Pages | 679-686 | |
| Keywords |
Noisy Labeling; Loss Correction; Class-conditional Importance Weighting; Learning with Noisy Labels | ||||
| Abstract | Large-scale accurate labels are very important to the Deep Neural Networks to train them and assure high performance. However, it is very expensive to create a clean dataset since usually it relies on human interaction. To this purpose, the labelling process is made cheap with a trade-off of having noisy labels. Learning with Noisy Labels is an active area of research being at the same time very challenging. The recent advances in Self-supervised learning and robust loss functions have helped in advancing noisy label research. In this paper, we propose a loss correction method that relies on dynamic weights computed based on the model training. We extend the existing Contrast to Divide algorithm coupled with DivideMix using a new class-conditional weighted scheme. We validate the method using the standard noise experiments and achieved encouraging results. | ||||
| Address | Virtual; February 2022 | ||||
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| Area | Expedition | Conference | VISAPP | ||
| Notes | MILAB; no menciona | Approved | no | ||
| Call Number | Admin @ si @ NMM2022 | Serial | 3798 | ||
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| Author | G.Thorvaldsen; Joana Maria Pujadas-Mora; T.Andersen ; L.Eikvil; Josep Llados; Alicia Fornes; Anna Cabre | ||||
| Title | A Tale of two Transcriptions | Type | Journal | ||
| Year | 2015 | Publication | Historical Life Course Studies | Abbreviated Journal | |
| Volume | 2 | Issue | Pages | 1-19 | |
| Keywords |
Nominative Sources; Census; Vital Records; Computer Vision; Optical Character Recognition; Word Spotting | ||||
| Abstract | non-indexed
This article explains how two projects implement semi-automated transcription routines: for census sheets in Norway and marriage protocols from Barcelona. The Spanish system was created to transcribe the marriage license books from 1451 to 1905 for the Barcelona area; one of the world’s longest series of preserved vital records. Thus, in the Project “Five Centuries of Marriages” (5CofM) at the Autonomous University of Barcelona’s Center for Demographic Studies, the Barcelona Historical Marriage Database has been built. More than 600,000 records were transcribed by 150 transcribers working online. The Norwegian material is cross-sectional as it is the 1891 census, recorded on one sheet per person. This format and the underlining of keywords for several variables made it more feasible to semi-automate data entry than when many persons are listed on the same page. While Optical Character Recognition (OCR) for printed text is scientifically mature, computer vision research is now focused on more difficult problems such as handwriting recognition. In the marriage project, document analysis methods have been proposed to automatically recognize the marriage licenses. Fully automatic recognition is still a challenge, but some promising results have been obtained. In Spain, Norway and elsewhere the source material is available as scanned pictures on the Internet, opening up the possibility for further international cooperation concerning automating the transcription of historic source materials. Like what is being done in projects to digitize printed materials, the optimal solution is likely to be a combination of manual transcription and machine-assisted recognition also for hand-written sources. |
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| ISSN | 2352-6343 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | DAG; 600.077; 602.006 | Approved | no | ||
| Call Number | Admin @ si @ TPA2015 | Serial | 2582 | ||
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| Author | Debora Gil; Aura Hernandez-Sabate; Mireia Burnat; Steven Jansen; Jordi Martinez-Vilalta | ||||
| Title | Structure-Preserving Smoothing of Biomedical Images | Type | Conference Article | ||
| Year | 2009 | Publication | 13th International Conference on Computer Analysis of Images and Patterns | Abbreviated Journal | |
| Volume | 5702 | Issue | Pages | 427-434 | |
| Keywords |
non-linear smoothing; differential geometry; anatomical structures segmentation; cardiac magnetic resonance; computerized tomography. | ||||
| Abstract | Smoothing of biomedical images should preserve gray-level transitions between adjacent tissues, while restoring contours consistent with anatomical structures. Anisotropic diffusion operators are based on image appearance discontinuities (either local or contextual) and might fail at weak inter-tissue transitions. Meanwhile, the output of block-wise and morphological operations is prone to present a block structure due to the shape and size of the considered pixel neighborhood. In this contribution, we use differential geometry concepts to define a diffusion operator that restricts to image consistent level-sets. In this manner, the final state is a non-uniform intensity image presenting homogeneous inter-tissue transitions along anatomical structures, while smoothing intra-structure texture. Experiments on different types of medical images (magnetic resonance, computerized tomography) illustrate its benefit on a further process (such as segmentation) of images. | ||||
| Address | Münster, Germany | ||||
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| 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 | 978-3-642-03766-5 | Medium | |
| Area | Expedition | Conference | CAIP | ||
| Notes | IAM | Approved | no | ||
| Call Number | IAM @ iam @ GHB2009 | Serial | 1527 | ||
| Permanent link to this record | |||||
| Author | Debora Gil; Aura Hernandez-Sabate; Mireia Brunat;Steven Jansen; Jordi Martinez-Vilalta | ||||
| Title | Structure-preserving smoothing of biomedical images | Type | Journal Article | ||
| Year | 2011 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 44 | Issue | 9 | Pages | 1842-1851 |
| Keywords |
Non-linear smoothing; Differential geometry; Anatomical structures; segmentation; Cardiac magnetic resonance; Computerized tomography | ||||
| Abstract | Smoothing of biomedical images should preserve gray-level transitions between adjacent tissues, while restoring contours consistent with anatomical structures. Anisotropic diffusion operators are based on image appearance discontinuities (either local or contextual) and might fail at weak inter-tissue transitions. Meanwhile, the output of block-wise and morphological operations is prone to present a block structure due to the shape and size of the considered pixel neighborhood. In this contribution, we use differential geometry concepts to define a diffusion operator that restricts to image consistent level-sets. In this manner, the final state is a non-uniform intensity image presenting homogeneous inter-tissue transitions along anatomical structures, while smoothing intra-structure texture. Experiments on different types of medical images (magnetic resonance, computerized tomography) illustrate its benefit on a further process (such as segmentation) of images. | ||||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 0031-3203 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | IAM; ADAS | Approved | no | ||
| Call Number | IAM @ iam @ GHB2011 | Serial | 1526 | ||
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| Author | Idoia Ruiz; Joan Serrat | ||||
| Title | Hierarchical Novelty Detection for Traffic Sign Recognition | Type | Journal Article | ||
| Year | 2022 | Publication | Sensors | Abbreviated Journal | SENS |
| Volume | 22 | Issue | 12 | Pages | 4389 |
| Keywords |
Novelty detection; hierarchical classification; deep learning; traffic sign recognition; autonomous driving; computer vision | ||||
| Abstract | Recent works have made significant progress in novelty detection, i.e., the problem of detecting samples of novel classes, never seen during training, while classifying those that belong to known classes. However, the only information this task provides about novel samples is that they are unknown. In this work, we leverage hierarchical taxonomies of classes to provide informative outputs for samples of novel classes. We predict their closest class in the taxonomy, i.e., its parent class. We address this problem, known as hierarchical novelty detection, by proposing a novel loss, namely Hierarchical Cosine Loss that is designed to learn class prototypes along with an embedding of discriminative features consistent with the taxonomy. We apply it to traffic sign recognition, where we predict the parent class semantics for new types of traffic signs. Our model beats state-of-the art approaches on two large scale traffic sign benchmarks, Mapillary Traffic Sign Dataset (MTSD) and Tsinghua-Tencent 100K (TT100K), and performs similarly on natural images benchmarks (AWA2, CUB). For TT100K and MTSD, our approach is able to detect novel samples at the correct nodes of the hierarchy with 81% and 36% of accuracy, respectively, at 80% known class accuracy. | ||||
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| Notes | ADAS; 600.154 | Approved | no | ||
| Call Number | Admin @ si @ RuS2022 | Serial | 3684 | ||
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| Author | Lorenzo Seidenari; Giuseppe Serra; Andrew Bagdanov; Alberto del Bimbo | ||||
| Title | Local pyramidal descriptors for image recognition | Type | Journal Article | ||
| Year | 2014 | Publication | IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
| Volume | 36 | Issue | 5 | Pages | 1033 - 1040 |
| Keywords |
Object categorization; local features; kernel methods | ||||
| Abstract | In this paper we present a novel method to improve the flexibility of descriptor matching for image recognition by using local multiresolution
pyramids in feature space. We propose that image patches be represented at multiple levels of descriptor detail and that these levels be defined in terms of local spatial pooling resolution. Preserving multiple levels of detail in local descriptors is a way of hedging one’s bets on which levels will most relevant for matching during learning and recognition. We introduce the Pyramid SIFT (P-SIFT) descriptor and show that its use in four state-of-the-art image recognition pipelines improves accuracy and yields state-of-the-art results. Our technique is applicable independently of spatial pyramid matching and we show that spatial pyramids can be combined with local pyramids to obtain further improvement.We achieve state-of-the-art results on Caltech-101 (80.1%) and Caltech-256 (52.6%) when compared to other approaches based on SIFT features over intensity images. Our technique is efficient and is extremely easy to integrate into image recognition pipelines. |
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| Series Volume | Series Issue | Edition | |||
| ISSN | 0162-8828 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | LAMP; 600.079 | Approved | no | ||
| Call Number | Admin @ si @ SSB2014 | Serial | 2524 | ||
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