| Records |
| Author |
Veronica Romero; Alicia Fornes; Enrique Vidal; Joan Andreu Sanchez |
| Title |
Information Extraction in Handwritten Marriage Licenses Books Using the MGGI Methodology |
Type |
Conference Article |
| Year |
2017 |
Publication |
8th Iberian Conference on Pattern Recognition and Image Analysis |
Abbreviated Journal |
|
| Volume |
10255 |
Issue |
|
Pages |
287-294 |
| Keywords |
Handwritten Text Recognition; Information extraction; Language modeling; MGGI; Categories-based language model |
| Abstract |
Historical records of daily activities provide intriguing insights into the life of our ancestors, useful for demographic and genealogical research. For example, marriage license books have been used for centuries by ecclesiastical and secular institutions to register marriages. These books follow a simple structure of the text in the records with a evolutionary vocabulary, mainly composed of proper names that change along the time. This distinct vocabulary makes automatic transcription and semantic information extraction difficult tasks. In previous works we studied the use of category-based language models and how a Grammatical Inference technique known as MGGI could improve the accuracy of these tasks. In this work we analyze the main causes of the semantic errors observed in previous results and apply a better implementation of the MGGI technique to solve these problems. Using the resulting language model, transcription and information extraction experiments have been carried out, and the results support our proposed approach. |
| Address |
Faro; Portugal; June 2017 |
| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
|
Editor |
L.A. Alexandre; J.Salvador Sanchez; Joao M. F. Rodriguez |
| Language |
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Summary Language |
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Original Title |
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| Series Editor |
|
Series Title |
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Abbreviated Series Title |
LNCS |
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
|
ISBN |
978-3-319-58837-7 |
Medium |
|
| Area |
|
Expedition |
|
Conference  |
IbPRIA |
| Notes |
DAG; 602.006; 600.097; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ RFV2017 |
Serial |
2952 |
| Permanent link to this record |
| |
|
| |
| Author |
Marc Bolaños; Alvaro Peris; Francisco Casacuberta; Petia Radeva |
| Title |
VIBIKNet: Visual Bidirectional Kernelized Network for Visual Question Answering |
Type |
Conference Article |
| Year |
2017 |
Publication |
8th Iberian Conference on Pattern Recognition and Image Analysis |
Abbreviated Journal |
|
| Volume |
|
Issue |
|
Pages |
|
| Keywords |
Visual Qestion Aswering; Convolutional Neural Networks; Long short-term memory networks |
| Abstract |
In this paper, we address the problem of visual question answering by proposing a novel model, called VIBIKNet. Our model is based on integrating Kernelized Convolutional Neural Networks and Long-Short Term Memory units to generate an answer given a question about an image. We prove that VIBIKNet is an optimal trade-off between accuracy and computational load, in terms of memory and time consumption. We validate our method on the VQA challenge dataset and compare it to the top performing methods in order to illustrate its performance and speed. |
| Address |
Faro; Portugal; June 2017 |
| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
|
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
IbPRIA |
| Notes |
MILAB; no proj |
Approved |
no |
| Call Number |
Admin @ si @ BPC2017 |
Serial |
2939 |
| Permanent link to this record |
| |
|
| |
| Author |
Hana Jarraya; Oriol Ramos Terrades; Josep Llados |
| Title |
Graph Embedding through Probabilistic Graphical Model applied to Symbolic Graphs |
Type |
Conference Article |
| Year |
2017 |
Publication |
8th Iberian Conference on Pattern Recognition and Image Analysis |
Abbreviated Journal |
|
| Volume |
|
Issue |
|
Pages |
|
| Keywords |
Attributed Graph; Probabilistic Graphical Model; Graph Embedding; Structured Support Vector Machines |
| Abstract |
We propose a new Graph Embedding (GEM) method that takes advantages of structural pattern representation. It models an Attributed Graph (AG) as a Probabilistic Graphical Model (PGM). Then, it learns the parameters of this PGM presented by a vector. This vector is a signature of AG in a lower dimensional vectorial space. We apply Structured Support Vector Machines (SSVM) to process classification task. As first tentative, results on the GREC dataset are encouraging enough to go further on this direction. |
| Address |
Faro; Portugal; June 2017 |
| Corporate Author |
|
Thesis |
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| Publisher |
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Place of Publication |
|
Editor |
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| Language |
|
Summary Language |
|
Original Title |
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| Series Editor |
|
Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
|
Edition |
|
| ISSN |
|
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
IbPRIA |
| Notes |
DAG; 600.097; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ JRL2017a |
Serial |
2953 |
| Permanent link to this record |
| |
|
| |
| Author |
Eduardo Aguilar; Petia Radeva |
| Title |
Food Recognition by Integrating Local and Flat Classifiers |
Type |
Conference Article |
| Year |
2019 |
Publication |
9th Iberian Conference on Pattern Recognition and Image Analysis |
Abbreviated Journal |
|
| Volume |
11867 |
Issue |
|
Pages |
65-74 |
| Keywords |
|
| Abstract |
The recognition of food image is an interesting research topic, in which its applicability in the creation of nutritional diaries stands out with the aim of improving the quality of life of people with a chronic disease (e.g. diabetes, heart disease) or prone to acquire it (e.g. people with overweight or obese). For a food recognition system to be useful in real applications, it is necessary to recognize a huge number of different foods. We argue that for very large scale classification, a traditional flat classifier is not enough to acquire an acceptable result. To address this, we propose a method that performs prediction with local classifiers, based on a class hierarchy, or with flat classifier. We decide which approach to use, depending on the analysis of both the Epistemic Uncertainty obtained for the image in the children classifiers and the prediction of the parent classifier. When our criterion is met, the final prediction is obtained with the respective local classifier; otherwise, with the flat classifier. From the results, we can see that the proposed method improves the classification performance compared to the use of a single flat classifier. |
| Address |
Madrid; July 2019 |
| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
LNCS |
| Series Volume |
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Series Issue |
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Edition |
|
| ISSN |
|
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
IbPRIA |
| Notes |
MILAB; no proj |
Approved |
no |
| Call Number |
Admin @ si @ AgR2019b |
Serial |
3369 |
| Permanent link to this record |
| |
|
| |
| 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. |
| Address |
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| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
LNCS |
| Series Volume |
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Series Issue |
|
Edition |
|
| ISSN |
|
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
IbPRIA |
| Notes |
ISE; 600.098; 600.119 |
Approved |
no |
| Call Number |
Admin @ si @ AVG2019 |
Serial |
3416 |
| Permanent link to this record |
| |
|
| |
| Author |
Gemma Rotger; Francesc Moreno-Noguer; Felipe Lumbreras; Antonio Agudo |
| Title |
Single view facial hair 3D reconstruction |
Type |
Conference Article |
| Year |
2019 |
Publication |
9th Iberian Conference on Pattern Recognition and Image Analysis |
Abbreviated Journal |
|
| Volume |
11867 |
Issue |
|
Pages |
423-436 |
| Keywords |
3D Vision; Shape Reconstruction; Facial Hair Modeling |
| Abstract |
n this work, we introduce a novel energy-based framework that addresses the challenging problem of 3D reconstruction of facial hair from a single RGB image. To this end, we identify hair pixels over the image via texture analysis and then determine individual hair fibers that are modeled by means of a parametric hair model based on 3D helixes. We propose to minimize an energy composed of several terms, in order to adapt the hair parameters that better fit the image detections. The final hairs respond to the resulting fibers after a post-processing step where we encourage further realism. The resulting approach generates realistic facial hair fibers from solely an RGB image without assuming any training data nor user interaction. We provide an experimental evaluation on real-world pictures where several facial hair styles and image conditions are observed, showing consistent results and establishing a comparison with respect to competing approaches. |
| Address |
Madrid; July 2019 |
| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
LNCS |
| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
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ISBN |
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Medium |
|
| Area |
|
Expedition |
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Conference |
IbPRIA |
| Notes |
MSIAU; 600.086; 600.130; 600.122 |
Approved |
no |
| Call Number |
Admin @ si @ |
Serial |
3707 |
| Permanent link to this record |
| |
|
| |
| Author |
Nil Ballus; Bhalaji Nagarajan; Petia Radeva |
| Title |
Opt-SSL: An Enhanced Self-Supervised Framework for Food Recognition |
Type |
Conference Article |
| Year |
2022 |
Publication |
10th Iberian Conference on Pattern Recognition and Image Analysis |
Abbreviated Journal |
|
| Volume |
13256 |
Issue |
|
Pages |
|
| Keywords |
Self-supervised; Contrastive learning; Food recognition |
| Abstract |
Self-supervised Learning has been showing upbeat performance in several computer vision tasks. The popular contrastive methods make use of a Siamese architecture with different loss functions. In this work, we go deeper into two very recent state of the art frameworks, namely, SimSiam and Barlow Twins. Inspired by them, we propose a new self-supervised learning method we call Opt-SSL that combines both image and feature contrasting. We validate the proposed method on the food recognition task, showing that our proposed framework enables the self-learning networks to learn better visual representations. |
| Address |
Aveiro; Portugal; May 2022 |
| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
LNCS |
| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
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ISBN |
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Medium |
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| Area |
|
Expedition |
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Conference |
IbPRIA |
| Notes |
MILAB; no menciona |
Approved |
no |
| Call Number |
Admin @ si @ BNR2022 |
Serial |
3782 |
| Permanent link to this record |
| |
|
| |
| Author |
Albert Tatjer; Bhalaji Nagarajan; Ricardo Marques; Petia Radeva |
| Title |
CCLM: Class-Conditional Label Noise Modelling |
Type |
Conference Article |
| Year |
2023 |
Publication |
11th Iberian Conference on Pattern Recognition and Image Analysis |
Abbreviated Journal |
|
| Volume |
14062 |
Issue |
|
Pages |
3-14 |
| Keywords |
|
| Abstract |
The performance of deep neural networks highly depends on the quality and volume of the training data. However, cost-effective labelling processes such as crowdsourcing and web crawling often lead to data with noisy (i.e., wrong) labels. Making models robust to this label noise is thus of prime importance. A common approach is using loss distributions to model the label noise. However, the robustness of these methods highly depends on the accuracy of the division of training set into clean and noisy samples. In this work, we dive in this research direction highlighting the existing problem of treating this distribution globally and propose a class-conditional approach to split the clean and noisy samples. We apply our approach to the popular DivideMix algorithm and show how the local treatment fares better with respect to the global treatment of loss distribution. We validate our hypothesis on two popular benchmark datasets and show substantial improvements over the baseline experiments. We further analyze the effectiveness of the proposal using two different metrics – Noise Division Accuracy and Classiness. |
| Address |
Alicante; Spain; June 2023 |
| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
LNCS |
| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
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ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
IbPRIA |
| Notes |
MILAB |
Approved |
no |
| Call Number |
Admin @ si @ TNM2023 |
Serial |
3925 |
| Permanent link to this record |
| |
|
| |
| 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 |
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| Volume |
2 |
Issue |
|
Pages |
3-11 |
| Keywords |
Video description; Neural Machine Translation; Birectional Recurrent Neural Networks; LSTM; Convolutional Neural Networks |
| Abstract |
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 |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
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ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
ICANN |
| Notes |
MILAB; |
Approved |
no |
| Call Number |
Admin @ si @ PBR2016 |
Serial |
2833 |
| Permanent link to this record |
| |
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| |
| Author |
Oriol Ramos Terrades; Salvatore Tabbone; Ernest Valveny |
| Title |
Optimal Linear Combination for Two-class Classifiers |
Type |
Conference Article |
| Year |
2007 |
Publication |
Proceedings of the International Conference on Advances in Pattern Recognition |
Abbreviated Journal |
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Issue |
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Pages |
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| Abstract |
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| Address |
Kolkata (India) |
| Corporate Author |
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Thesis |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
ICAPR |
| Notes |
DAG |
Approved |
no |
| Call Number |
DAG @ dag @ RTV2007a |
Serial |
894 |
| Permanent link to this record |
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| Author |
Bogdan Raducanu; Jordi Vitria; D. Gatica-Perez |
| Title |
You are Fired! Nonverbal Role Analysis in Competitive Meetings |
Type |
Conference Article |
| Year |
2009 |
Publication |
IEEE International Conference on Audio, Speech and Signal Processing |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
1949–1952 |
| Keywords |
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| Abstract |
This paper addresses the problem of social interaction analysis in competitive meetings, using nonverbal cues. For our study, we made use of ldquoThe Apprenticerdquo reality TV show, which features a competition for a real, highly paid corporate job. Our analysis is centered around two tasks regarding a person's role in a meeting: predicting the person with the highest status and predicting the fired candidates. The current study was carried out using nonverbal audio cues. Results obtained from the analysis of a full season of the show, representing around 90 minutes of audio data, are very promising (up to 85.7% of accuracy in the first case and up to 92.8% in the second case). Our approach is based only on the nonverbal interaction dynamics during the meeting without relying on the spoken words. |
| Address |
Taipei, Taiwan |
| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Issue |
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Edition |
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| ISSN |
1520-6149 |
ISBN |
978-1-4244-2353-8 |
Medium |
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| Area |
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Expedition |
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Conference |
ICASSP |
| Notes |
OR;MV |
Approved |
no |
| Call Number |
BCNPCL @ bcnpcl @ RVG2009 |
Serial |
1154 |
| Permanent link to this record |
| |
|
| |
| Author |
Saiping Zhang; Luis Herranz; Marta Mrak; Marc Gorriz Blanch; Shuai Wan; Fuzheng Yang |
| Title |
DCNGAN: A Deformable Convolution-Based GAN with QP Adaptation for Perceptual Quality Enhancement of Compressed Video |
Type |
Conference Article |
| Year |
2022 |
Publication |
47th International Conference on Acoustics, Speech, and Signal Processing |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
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| Keywords |
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| Abstract |
In this paper, we propose a deformable convolution-based generative adversarial network (DCNGAN) for perceptual quality enhancement of compressed videos. DCNGAN is also adaptive to the quantization parameters (QPs). Compared with optical flows, deformable convolutions are more effective and efficient to align frames. Deformable convolutions can operate on multiple frames, thus leveraging more temporal information, which is beneficial for enhancing the perceptual quality of compressed videos. Instead of aligning frames in a pairwise manner, the deformable convolution can process multiple frames simultaneously, which leads to lower computational complexity. Experimental results demonstrate that the proposed DCNGAN outperforms other state-of-the-art compressed video quality enhancement algorithms. |
| Address |
Virtual; May 2022 |
| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
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ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
ICASSP |
| Notes |
MACO; 600.161; 601.379 |
Approved |
no |
| Call Number |
Admin @ si @ ZHM2022a |
Serial |
3765 |
| Permanent link to this record |
| |
|
| |
| Author |
Guillem Martinez; Maya Aghaei; Martin Dijkstra; Bhalaji Nagarajan; Femke Jaarsma; Jaap van de Loosdrecht; Petia Radeva; Klaas Dijkstra |
| Title |
Hyper-Spectral Imaging for Overlapping Plastic Flakes Segmentation |
Type |
Conference Article |
| Year |
2022 |
Publication |
47th International Conference on Acoustics, Speech, and Signal Processing |
Abbreviated Journal |
|
| Volume |
|
Issue |
|
Pages |
|
| Keywords |
Hyper-spectral imaging; plastic sorting; multi-label segmentation; bitfield encoding |
| Abstract |
In this paper, we propose a deformable convolution-based generative adversarial network (DCNGAN) for perceptual quality enhancement of compressed videos. DCNGAN is also adaptive to the quantization parameters (QPs). Compared with optical flows, deformable convolutions are more effective and efficient to align frames. Deformable convolutions can operate on multiple frames, thus leveraging more temporal information, which is beneficial for enhancing the perceptual quality of compressed videos. Instead of aligning frames in a pairwise manner, the deformable convolution can process multiple frames simultaneously, which leads to lower computational complexity. Experimental results demonstrate that the proposed DCNGAN outperforms other state-of-the-art compressed video quality enhancement algorithms. |
| Address |
Singapore; May 2022 |
| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
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ISBN |
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Medium |
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| Area |
|
Expedition |
|
Conference |
ICASSP |
| Notes |
MILAB; no proj |
Approved |
no |
| Call Number |
Admin @ si @ MAD2022 |
Serial |
3767 |
| Permanent link to this record |
| |
|
| |
| Author |
Danna Xue; Luis Herranz; Javier Vazquez; Yanning Zhang |
| Title |
Burst Perception-Distortion Tradeoff: Analysis and Evaluation |
Type |
Conference Article |
| Year |
2023 |
Publication |
IEEE International Conference on Acoustics, Speech and Signal Processing |
Abbreviated Journal |
|
| Volume |
|
Issue |
|
Pages |
|
| Keywords |
|
| Abstract |
Burst image restoration attempts to effectively utilize the complementary cues appearing in sequential images to produce a high-quality image. Most current methods use all the available images to obtain the reconstructed image. However, using more images for burst restoration is not always the best option regarding reconstruction quality and efficiency, as the images acquired by handheld imaging devices suffer from degradation and misalignment caused by the camera noise and shake. In this paper, we extend the perception-distortion tradeoff theory by introducing multiple-frame information. We propose the area of the unattainable region as a new metric for perception-distortion tradeoff evaluation and comparison. Based on this metric, we analyse the performance of burst restoration from the perspective of the perception-distortion tradeoff under both aligned bursts and misaligned bursts situations. Our analysis reveals the importance of inter-frame alignment for burst restoration and shows that the optimal burst length for the restoration model depends both on the degree of degradation and misalignment. |
| Address |
Rodhes Islands; Greece; June 2023 |
| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
|
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
ICASSP |
| Notes |
CIC; MACO |
Approved |
no |
| Call Number |
Admin @ si @ XHV2023 |
Serial |
3909 |
| Permanent link to this record |
| |
|
| |
| Author |
Yifan Wang; Luka Murn; Luis Herranz; Fei Yang; Marta Mrak; Wei Zhang; Shuai Wan; Marc Gorriz Blanch |
| Title |
Efficient Super-Resolution for Compression Of Gaming Videos |
Type |
Conference Article |
| Year |
2023 |
Publication |
IEEE International Conference on Acoustics, Speech and Signal Processing |
Abbreviated Journal |
|
| Volume |
|
Issue |
|
Pages |
|
| Keywords |
|
| Abstract |
Due to the increasing demand for game-streaming services, efficient compression of computer-generated video is more critical than ever, especially when the available bandwidth is low. This paper proposes a super-resolution framework that improves the coding efficiency of computer-generated gaming videos at low bitrates. Most state-of-the-art super-resolution networks generalize over a variety of RGB inputs and use a unified network architecture for frames of different levels of degradation, leading to high complexity and redundancy. Since games usually consist of a limited number of fixed scenarios, we specialize one model for each scenario and assign appropriate network capacities for different QPs to perform super-resolution under the guidance of reconstructed high-quality luma components. Experimental results show that our framework achieves a superior quality-complexity trade-off compared to the ESRnet baseline, saving at most 93.59% parameters while maintaining comparable performance. The compression efficiency compared to HEVC is also improved by more than 17% BD-rate gain. |
| Address |
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| Corporate Author |
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Thesis |
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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 |
ICASSP |
| Notes |
LAMP; MACO |
Approved |
no |
| Call Number |
Admin @ si @ WMH2023 |
Serial |
3911 |
| Permanent link to this record |
