| Records |
| Author |
Robert Benavente; Maria Vanrell |
Title  |
Parametrizacion del Espacio de Categorias de Color |
Type |
Miscellaneous |
| Year |
2007 |
Publication |
Proceedings del VIII Congreso Nacional del Color |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
77–78 |
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| Abstract |
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| Address |
Madrid (Spain) |
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Conference |
CNC’07 |
| Notes |
CAT;CIC |
Approved |
no |
| Call Number |
CAT @ cat @ BeV2007 |
Serial |
905 |
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| Author |
Robert Benavente; Maria Vanrell; Ramon Baldrich |
| Title |
Parametric Fuzzy Sets for Automatic Color Naming |
Type |
Journal |
| Year |
2008 |
Publication |
Journal of the Optical Society of America A |
Abbreviated Journal |
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| Volume |
25 |
Issue |
10 |
Pages |
2582–2593 |
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CIC |
Approved |
no |
| Call Number |
CAT @ cat @ BVB2008 |
Serial |
1004 |
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| Author |
Fahad Shahbaz Khan; Shida Beigpour; Joost Van de Weijer; Michael Felsberg |
| Title |
Painting-91: A Large Scale Database for Computational Painting Categorization |
Type |
Journal Article |
| Year |
2014 |
Publication |
Machine Vision and Applications |
Abbreviated Journal |
MVAP |
| Volume |
25 |
Issue |
6 |
Pages |
1385-1397 |
| Keywords |
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| Abstract |
Computer analysis of visual art, especially paintings, is an interesting cross-disciplinary research domain. Most of the research in the analysis of paintings involve medium to small range datasets with own specific settings. Interestingly, significant progress has been made in the field of object and scene recognition lately. A key factor in this success is the introduction and availability of benchmark datasets for evaluation. Surprisingly, such a benchmark setup is still missing in the area of computational painting categorization. In this work, we propose a novel large scale dataset of digital paintings. The dataset consists of paintings from 91 different painters. We further show three applications of our dataset namely: artist categorization, style classification and saliency detection. We investigate how local and global features popular in image classification perform for the tasks of artist and style categorization. For both categorization tasks, our experimental results suggest that combining multiple features significantly improves the final performance. We show that state-of-the-art computer vision methods can correctly classify 50 % of unseen paintings to its painter in a large dataset and correctly attribute its artistic style in over 60 % of the cases. Additionally, we explore the task of saliency detection on paintings and show experimental findings using state-of-the-art saliency estimation algorithms. |
| Address |
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| Publisher |
Springer Berlin Heidelberg |
Place of Publication |
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Edition |
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| ISSN |
0932-8092 |
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Conference |
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| Notes |
CIC; LAMP; 600.074; 600.079 |
Approved |
no |
| Call Number |
Admin @ si @ KBW2014 |
Serial |
2510 |
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| Author |
Francisco Alvaro; Francisco Cruz; Joan Andreu Sanchez; Oriol Ramos Terrades; Jose Miguel Bemedi |
| Title |
Page Segmentation of Structured Documents Using 2D Stochastic Context-Free Grammars |
Type |
Conference Article |
| Year |
2013 |
Publication |
6th Iberian Conference on Pattern Recognition and Image Analysis |
Abbreviated Journal |
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| Volume |
7887 |
Issue |
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Pages |
133-140 |
| Keywords |
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| Abstract |
In this paper we define a bidimensional extension of Stochastic Context-Free Grammars for page segmentation of structured documents. Two sets of text classification features are used to perform an initial classification of each zone of the page. Then, the page segmentation is obtained as the most likely hypothesis according to a grammar. This approach is compared to Conditional Random Fields and results show significant improvements in several cases. Furthermore, grammars provide a detailed segmentation that allowed a semantic evaluation which also validates this model. |
| Address |
Madeira; Portugal; June 2013 |
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| Publisher |
Springer Berlin Heidelberg |
Place of Publication |
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LNCS |
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Series Issue |
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Edition |
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| ISSN |
0302-9743 |
ISBN |
978-3-642-38627-5 |
Medium |
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Expedition |
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Conference |
IbPRIA |
| Notes |
DAG; 605.203 |
Approved |
no |
| Call Number |
Admin @ si @ ACS2013 |
Serial |
2328 |
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| Author |
Joan Serrat; X. Varona; Antonio Lopez; Xavier Roca; Juan J. Villanueva |
| Title |
P3: a three-dimensional digitizer prototype. |
Type |
Miscellaneous |
| Year |
2001 |
Publication |
Proceedings of the IX Spanish Symposium on Pattern Recognition and Image Analysis, 1:315–322. |
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| Address |
Castellon. |
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| Notes |
ADAS;ISE |
Approved |
no |
| Call Number |
ADAS @ adas @ SVL2001 |
Serial |
213 |
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| Author |
Gabriela Ramirez; Esau Villatoro; Bogdan Ionescu; Hugo Jair Escalante; Sergio Escalera; Martha Larson; Henning Muller; Isabelle Guyon |
| Title |
Overview of the Multimedia Information Processing for Personality & Social Networks Analysis Contes |
Type |
Conference Article |
| Year |
2018 |
Publication |
Multimedia Information Processing for Personality and Social Networks Analysis (MIPPSNA 2018) |
Abbreviated Journal |
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Pages |
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| Address |
Beijing; China; August 2018 |
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Conference |
ICPRW |
| Notes |
HUPBA |
Approved |
no |
| Call Number |
Admin @ si @ RVI2018 |
Serial |
3211 |
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| Author |
Stepan Simsa; Michal Uricar; Milan Sulc; Yash Patel; Ahmed Hamdi; Matej Kocian; Matyas Skalicky; Jiri Matas; Antoine Doucet; Mickael Coustaty; Dimosthenis Karatzas |
| Title |
Overview of DocILE 2023: Document Information Localization and Extraction |
Type |
Conference Article |
| Year |
2023 |
Publication |
International Conference of the Cross-Language Evaluation Forum for European Languages |
Abbreviated Journal |
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| Volume |
14163 |
Issue |
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Pages |
276–293 |
| Keywords |
Information Extraction; Computer Vision; Natural Language Processing; Optical Character Recognition; Document Understanding |
| Abstract |
This paper provides an overview of the DocILE 2023 Competition, its tasks, participant submissions, the competition results and possible future research directions. This first edition of the competition focused on two Information Extraction tasks, Key Information Localization and Extraction (KILE) and Line Item Recognition (LIR). Both of these tasks require detection of pre-defined categories of information in business documents. The second task additionally requires correctly grouping the information into tuples, capturing the structure laid out in the document. The competition used the recently published DocILE dataset and benchmark that stays open to new submissions. The diversity of the participant solutions indicates the potential of the dataset as the submissions included pure Computer Vision, pure Natural Language Processing, as well as multi-modal solutions and utilized all of the parts of the dataset, including the annotated, synthetic and unlabeled subsets. |
| Address |
Thessaloniki; Greece; September 2023 |
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LNCS |
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Conference |
CLEF |
| Notes |
DAG |
Approved |
no |
| Call Number |
Admin @ si @ SUS2023a |
Serial |
3924 |
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| Author |
Parichehr Behjati Ardakani; Pau Rodriguez; Armin Mehri; Isabelle Hupont; Carles Fernandez; Jordi Gonzalez |
| Title |
OverNet: Lightweight Multi-Scale Super-Resolution with Overscaling Network |
Type |
Conference Article |
| Year |
2021 |
Publication |
IEEE Winter Conference on Applications of Computer Vision |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
2693-2702 |
| Keywords |
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| Abstract |
Super-resolution (SR) has achieved great success due to the development of deep convolutional neural networks (CNNs). However, as the depth and width of the networks increase, CNN-based SR methods have been faced with the challenge of computational complexity in practice. More- over, most SR methods train a dedicated model for each target resolution, losing generality and increasing memory requirements. To address these limitations we introduce OverNet, a deep but lightweight convolutional network to solve SISR at arbitrary scale factors with a single model. We make the following contributions: first, we introduce a lightweight feature extractor that enforces efficient reuse of information through a novel recursive structure of skip and dense connections. Second, to maximize the performance of the feature extractor, we propose a model agnostic reconstruction module that generates accurate high-resolution images from overscaled feature maps obtained from any SR architecture. Third, we introduce a multi-scale loss function to achieve generalization across scales. Experiments show that our proposal outperforms previous state-of-the-art approaches in standard benchmarks, while maintaining relatively low computation and memory requirements. |
| Address |
Virtual; January 2021 |
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Conference |
WACV |
| Notes |
ISE; 600.119; 600.098 |
Approved |
no |
| Call Number |
Admin @ si @ BRM2021 |
Serial |
3512 |
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| Author |
Baiyu Chen; Sergio Escalera; Isabelle Guyon; Victor Ponce; N. Shah; Marc Oliu |
| Title |
Overcoming Calibration Problems in Pattern Labeling with Pairwise Ratings: Application to Personality Traits |
Type |
Conference Article |
| Year |
2016 |
Publication |
14th European Conference on Computer Vision Workshops |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
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| Keywords |
Calibration of labels; Label bias; Ordinal labeling; Variance Models; Bradley-Terry-Luce model; Continuous labels; Regression; Personality traits; Crowd-sourced labels |
| Abstract |
We address the problem of calibration of workers whose task is to label patterns with continuous variables, which arises for instance in labeling images of videos of humans with continuous traits. Worker bias is particularly dicult to evaluate and correct when many workers contribute just a few labels, a situation arising typically when labeling is crowd-sourced. In the scenario of labeling short videos of people facing a camera with personality traits, we evaluate the feasibility of the pairwise ranking method to alleviate bias problems. Workers are exposed to pairs of videos at a time and must order by preference. The variable levels are reconstructed by fitting a Bradley-Terry-Luce model with maximum likelihood. This method may at first sight, seem prohibitively expensive because for N videos, p = N (N-1)/2 pairs must be potentially processed by workers rather that N videos. However, by performing extensive simulations, we determine an empirical law for the scaling of the number of pairs needed as a function of the number of videos in order to achieve a given accuracy of score reconstruction and show that the pairwise method is a ordable. We apply the method to the labeling of a large scale dataset of 10,000 videos used in the ChaLearn Apparent Personality Trait challenge. |
| Address |
Amsterdam; The Netherlands; October 2016 |
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Conference |
ECCVW |
| Notes |
HuPBA;MILAB; |
Approved |
no |
| Call Number |
Admin @ si @ CEG2016 |
Serial |
2829 |
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| Author |
Sergi Garcia Bordils; Andres Mafla; Ali Furkan Biten; Oren Nuriel; Aviad Aberdam; Shai Mazor; Ron Litman; Dimosthenis Karatzas |
| Title |
Out-of-Vocabulary Challenge Report |
Type |
Conference Article |
| Year |
2022 |
Publication |
Proceedings European Conference on Computer Vision Workshops |
Abbreviated Journal |
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| Volume |
13804 |
Issue |
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Pages |
359–375 |
| Keywords |
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| Abstract |
This paper presents final results of the Out-Of-Vocabulary 2022 (OOV) challenge. The OOV contest introduces an important aspect that is not commonly studied by Optical Character Recognition (OCR) models, namely, the recognition of unseen scene text instances at training time. The competition compiles a collection of public scene text datasets comprising of 326,385 images with 4,864,405 scene text instances, thus covering a wide range of data distributions. A new and independent validation and test set is formed with scene text instances that are out of vocabulary at training time. The competition was structured in two tasks, end-to-end and cropped scene text recognition respectively. A thorough analysis of results from baselines and different participants is presented. Interestingly, current state-of-the-art models show a significant performance gap under the newly studied setting. We conclude that the OOV dataset proposed in this challenge will be an essential area to be explored in order to develop scene text models that achieve more robust and generalized predictions. |
| Address |
Tel-Aviv; Israel; October 2022 |
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LNCS |
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Conference |
ECCVW |
| Notes |
DAG; 600.155; 302.105; 611.002 |
Approved |
no |
| Call Number |
Admin @ si @ GMB2022 |
Serial |
3771 |
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| Author |
Fadi Dornaika; Bogdan Raducanu |
| Title |
Out-of-Sample Embedding for Manifold Learning Applied to Face Recognition |
Type |
Conference Article |
| Year |
2013 |
Publication |
IEEE International Workshop on Analysis and Modeling of Faces and Gestures |
Abbreviated Journal |
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Issue |
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Pages |
862-868 |
| Keywords |
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| Abstract |
Manifold learning techniques are affected by two critical aspects: (i) the design of the adjacency graphs, and (ii) the embedding of new test data---the out-of-sample problem. For the first aspect, the proposed schemes were heuristically driven. For the second aspect, the difficulty resides in finding an accurate mapping that transfers unseen data samples into an existing manifold. Past works addressing these two aspects were heavily parametric in the sense that the optimal performance is only reached for a suitable parameter choice that should be known in advance. In this paper, we demonstrate that sparse coding theory not only serves for automatic graph reconstruction as shown in recent works, but also represents an accurate alternative for out-of-sample embedding. Considering for a case study the Laplacian Eigenmaps, we applied our method to the face recognition problem. To evaluate the effectiveness of the proposed out-of-sample embedding, experiments are conducted using the k-nearest neighbor (KNN) and Kernel Support Vector Machines (KSVM) classifiers on four public face databases. The experimental results show that the proposed model is able to achieve high categorization effectiveness as well as high consistency with non-linear embeddings/manifolds obtained in batch modes. |
| Address |
Portland; USA; June 2013 |
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Conference |
CVPRW |
| Notes |
OR; 600.046;MV |
Approved |
no |
| Call Number |
Admin @ si @ DoR2013 |
Serial |
2236 |
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| Author |
Bogdan Raducanu; Fadi Dornaika |
| Title |
Out-of-Sample Embedding by Sparse Representation |
Type |
Conference Article |
| Year |
2012 |
Publication |
Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop |
Abbreviated Journal |
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| Volume |
7626 |
Issue |
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Pages |
336-344 |
| Keywords |
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| Abstract |
A critical aspect of non-linear dimensionality reduction techniques is represented by the construction of the adjacency graph. The difficulty resides in finding the optimal parameters, a process which, in general, is heuristically driven. Recently, sparse representation has been proposed as a non-parametric solution to overcome this problem. In this paper, we demonstrate that this approach not only serves for the graph construction, but also represents an efficient and accurate alternative for out-of-sample embedding. Considering for a case study the Laplacian Eigenmaps, we applied our method to the face recognition problem. Experimental results conducted on some challenging datasets confirmed the robustness of our approach and its superiority when compared to existing techniques. |
| Address |
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Springer Berlin Heidelberg |
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0302-9743 |
ISBN |
978-3-642-34165-6 |
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Conference |
SSPR&SPR |
| Notes |
OR;MV |
Approved |
no |
| Call Number |
Admin @ si @ RaD2012c |
Serial |
2175 |
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| Author |
Zhong Jin; Franck Davoine |
| Title |
Orthogonal ICA Representation Of Images |
Type |
Miscellaneous |
| Year |
2004 |
Publication |
8th International Conference on Control, Automation, Robotics and Vision, 369–374 |
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Kunming (China) |
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no |
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Admin @ si @ JiD2004 |
Serial |
499 |
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| Author |
Michael Villamizar; A. Sanfeliu; Juan Andrade |
| Title |
Orientation Invariant Features for Multiclass Object Recognition |
Type |
Miscellaneous |
| Year |
2006 |
Publication |
11th Iberoamerican Congress on Pattern Recognition (CIARP´06), LNCS 4225: 655–664 |
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| Address |
Cancun (Mexico) |
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no |
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Admin @ si @ VSA2006b |
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664 |
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| Author |
Bart M. Ter Haar Romeny; W. Niessen; J. Weickert; P. Van Roermund; W. Van Enk; Antonio Lopez; R. Maas |
| Title |
Orientation detection of trabecular bone |
Type |
Miscellaneous |
| Year |
1996 |
Publication |
Biophysics and Molecular Biology, International Biophysics Congress. Volume 65, pgs. P–H5–43 |
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ADAS |
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no |
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ADAS @ adas @ HNW1996 |
Serial |
489 |
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