| Records |
| Author |
Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera |
| Title |
Efficient pairwise classification using Local Cross Off strategy |
Type |
Conference Article |
| Year |
2012 |
Publication |
25th Canadian Conference on Artificial Intelligence |
Abbreviated Journal |
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| Volume |
7310 |
Issue |
|
Pages  |
25-36 |
| Keywords |
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| Abstract |
The pairwise classification approach tends to perform better than other well-known approaches when dealing with multiclass classification problems. In the pairwise approach, however, the nuisance votes of many irrelevant classifiers may result in a wrong prediction class. To overcome this problem, a novel method, Local Crossing Off (LCO), is presented and evaluated in this paper. The proposed LCO system takes advantage of nearest neighbor classification algorithm because of its simplicity and speed, as well as the strength of other two powerful binary classifiers to discriminate between two classes. This paper provides a set of experimental results on 20 datasets using two base learners: Neural Networks and Support Vector Machines. The results show that the proposed technique not only achieves better classification accuracy, but also is computationally more efficient for tackling classification problems which have a relatively large number of target classes. |
| Address |
Toronto, Ontario |
| 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 |
LNCS |
| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
0302-9743 |
ISBN |
978-3-642-30352-4 |
Medium |
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| Area |
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Expedition |
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Conference |
AI |
| Notes |
HuPBA;MILAB |
Approved |
no |
| Call Number |
Admin @ si @ BGE2012c |
Serial |
2044 |
| Permanent link to this record |
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| Author |
Angel Sappa; David Geronimo; Fadi Dornaika; Mohammad Rouhani; Antonio Lopez |
| Title |
Moving object detection from mobile platforms using stereo data registration |
Type |
Book Chapter |
| Year |
2012 |
Publication |
Computational Intelligence paradigms in advanced pattern classification |
Abbreviated Journal |
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| Volume |
386 |
Issue |
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Pages |
25-37 |
| Keywords |
pedestrian detection |
| Abstract |
This chapter describes a robust approach for detecting moving objects from on-board stereo vision systems. It relies on a feature point quaternion-based registration, which avoids common problems that appear when computationally expensive iterative-based algorithms are used on dynamic environments. The proposed approach consists of three main stages. Initially, feature points are extracted and tracked through consecutive 2D frames. Then, a RANSAC based approach is used for registering two point sets, with known correspondences in the 3D space. The computed 3D rigid displacement is used to map two consecutive 3D point clouds into the same coordinate system by means of the quaternion method. Finally, moving objects correspond to those areas with large 3D registration errors. Experimental results show the viability of the proposed approach to detect moving objects like vehicles or pedestrians in different urban scenarios. |
| Address |
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| Corporate Author |
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Thesis |
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| Publisher |
Springer Berlin Heidelberg |
Place of Publication |
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Editor |
Marek R. Ogiela; Lakhmi C. Jain |
| 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 |
1860-949X |
ISBN |
978-3-642-24048-5 |
Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
ADAS |
Approved |
no |
| Call Number |
Admin @ si @ SGD2012 |
Serial |
2061 |
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| Author |
Klaus Broelemann; Anjan Dutta; Xiaoyi Jiang; Josep Llados |
| Title |
Hierarchical Plausibility-Graphs for Symbol Spotting in Graphical Documents |
Type |
Book Chapter |
| Year |
2014 |
Publication |
Graphics Recognition. Current Trends and Challenges |
Abbreviated Journal |
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| Volume |
8746 |
Issue |
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Pages |
25-37 |
| Keywords |
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| Abstract |
Graph representation of graphical documents often suffers from noise such as spurious nodes and edges, and their discontinuity. In general these errors occur during the low-level image processing viz. binarization, skeletonization, vectorization etc. Hierarchical graph representation is a nice and efficient way to solve this kind of problem by hierarchically merging node-node and node-edge depending on the distance. But the creation of hierarchical graph representing the graphical information often uses hard thresholds on the distance to create the hierarchical nodes (next state) of the lower nodes (or states) of a graph. As a result, the representation often loses useful information. This paper introduces plausibilities to the nodes of hierarchical graph as a function of distance and proposes a modified algorithm for matching subgraphs of the hierarchical graphs. The plausibility-annotated nodes help to improve the performance of the matching algorithm on two hierarchical structures. To show the potential of this approach, we conduct an experiment with the SESYD dataset. |
| Address |
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| Corporate Author |
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Thesis |
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| Publisher |
Springer Berlin Heidelberg |
Place of Publication |
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Editor |
Bart Lamiroy; Jean-Marc Ogier |
| 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 |
0302-9743 |
ISBN |
978-3-662-44853-3 |
Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
DAG; 600.045; 600.056; 600.061; 600.077 |
Approved |
no |
| Call Number |
Admin @ si @ BDJ2014 |
Serial |
2699 |
| Permanent link to this record |
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| Author |
Arnau Baro; Pau Riba; Jorge Calvo-Zaragoza; Alicia Fornes |
| Title |
Optical Music Recognition by Recurrent Neural Networks |
Type |
Conference Article |
| Year |
2017 |
Publication |
14th IAPR International Workshop on Graphics Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
25-26 |
| Keywords |
Optical Music Recognition; Recurrent Neural Network; Long Short-Term Memory |
| Abstract |
Optical Music Recognition is the task of transcribing a music score into a machine readable format. Many music scores are written in a single staff, and therefore, they could be treated as a sequence. Therefore, this work explores the use of Long Short-Term Memory (LSTM) Recurrent Neural Networks for reading the music score sequentially, where the LSTM helps in keeping the context. For training, we have used a synthetic dataset of more than 40000 images, labeled at primitive level |
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Thesis |
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Place of Publication |
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Editor |
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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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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 |
ICDAR |
| Notes |
DAG; 600.097; 601.302; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ BRC2017 |
Serial |
3056 |
| Permanent link to this record |
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| Author |
Gisel Bastidas-Guacho; Patricio Moreno; Boris X. Vintimilla; Angel Sappa |
| Title |
Application on the Loop of Multimodal Image Fusion: Trends on Deep-Learning Based Approaches |
Type |
Conference Article |
| Year |
2023 |
Publication |
13th International Conference on Pattern Recognition Systems |
Abbreviated Journal |
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| Volume |
14234 |
Issue |
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Pages |
25–36 |
| Keywords |
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| Abstract |
Multimodal image fusion allows the combination of information from different modalities, which is useful for tasks such as object detection, edge detection, and tracking, to name a few. Using the fused representation for applications results in better task performance. There are several image fusion approaches, which have been summarized in surveys. However, the existing surveys focus on image fusion approaches where the application on the loop of multimodal image fusion is not considered. On the contrary, this study summarizes deep learning-based multimodal image fusion for computer vision (e.g., object detection) and image processing applications (e.g., semantic segmentation), that is, approaches where the application module leverages the multimodal fusion process to enhance the final result. Firstly, we introduce image fusion and the existing general frameworks for image fusion tasks such as multifocus, multiexposure and multimodal. Then, we describe the multimodal image fusion approaches. Next, we review the state-of-the-art deep learning multimodal image fusion approaches for vision applications. Finally, we conclude our survey with the trends of task-driven multimodal image fusion. |
| Address |
Guayaquil; Ecuador; July 2023 |
| Corporate Author |
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ISBN |
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Conference |
ICPRS |
| Notes |
MSIAU |
Approved |
no |
| Call Number |
Admin @ si @ BMV2023 |
Serial |
3932 |
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| Author |
Jelena Gorbova; Egils Avots; Iiris Lusi; Mark Fishel; Sergio Escalera; Gholamreza Anbarjafari |
| Title |
Integrating Vision and Language for First Impression Personality Analysis |
Type |
Journal Article |
| Year |
2018 |
Publication |
IEEE Multimedia |
Abbreviated Journal |
MULTIMEDIA |
| Volume |
25 |
Issue |
2 |
Pages |
24 - 33 |
| Keywords |
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| Abstract |
The authors present a novel methodology for analyzing integrated audiovisual signals and language to assess a persons personality. An evaluation of their proposed multimodal method using a job candidate screening system that predicted five personality traits from a short video demonstrates the methods effectiveness. |
| Address |
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Place of Publication |
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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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ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
HUPBA; 602.133 |
Approved |
no |
| Call Number |
Admin @ si @ GAL2018 |
Serial |
3124 |
| Permanent link to this record |
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| Author |
Ajian Liu; Xuan Li; Jun Wan; Yanyan Liang; Sergio Escalera; Hugo Jair Escalante; Meysam Madadi; Yi Jin; Zhuoyuan Wu; Xiaogang Yu; Zichang Tan; Qi Yuan; Ruikun Yang; Benjia Zhou; Guodong Guo; Stan Z. Li |
| Title |
Cross-ethnicity Face Anti-spoofing Recognition Challenge: A Review |
Type |
Journal Article |
| Year |
2020 |
Publication |
IET Biometrics |
Abbreviated Journal |
BIO |
| Volume |
10 |
Issue |
1 |
Pages |
24-43 |
| Keywords |
|
| Abstract |
Face anti-spoofing is critical to prevent face recognition systems from a security breach. The biometrics community has %possessed achieved impressive progress recently due the excellent performance of deep neural networks and the availability of large datasets. Although ethnic bias has been verified to severely affect the performance of face recognition systems, it still remains an open research problem in face anti-spoofing. Recently, a multi-ethnic face anti-spoofing dataset, CASIA-SURF CeFA, has been released with the goal of measuring the ethnic bias. It is the largest up to date cross-ethnicity face anti-spoofing dataset covering 3 ethnicities, 3 modalities, 1,607 subjects, 2D plus 3D attack types, and the first dataset including explicit ethnic labels among the recently released datasets for face anti-spoofing. We organized the Chalearn Face Anti-spoofing Attack Detection Challenge which consists of single-modal (e.g., RGB) and multi-modal (e.g., RGB, Depth, Infrared (IR)) tracks around this novel resource to boost research aiming to alleviate the ethnic bias. Both tracks have attracted 340 teams in the development stage, and finally 11 and 8 teams have submitted their codes in the single-modal and multi-modal face anti-spoofing recognition challenges, respectively. All the results were verified and re-ran by the organizing team, and the results were used for the final ranking. This paper presents an overview of the challenge, including its design, evaluation protocol and a summary of results. We analyze the top ranked solutions and draw conclusions derived from the competition. In addition we outline future work directions. |
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Expedition |
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Conference |
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| Notes |
HUPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ LLW2020b |
Serial |
3523 |
| Permanent link to this record |
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| Author |
Dustin Carrion Ojeda; Hong Chen; Adrian El Baz; Sergio Escalera; Chaoyu Guan; Isabelle Guyon; Ihsan Ullah; Xin Wang; Wenwu Zhu |
| Title |
NeurIPS’22 Cross-Domain MetaDL competition: Design and baseline results |
Type |
Conference Article |
| Year |
2022 |
Publication |
Understanding Social Behavior in Dyadic and Small Group Interactions |
Abbreviated Journal |
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| Volume |
191 |
Issue |
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Pages |
24-37 |
| Keywords |
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| Abstract |
We present the design and baseline results for a new challenge in the ChaLearn meta-learning series, accepted at NeurIPS'22, focusing on “cross-domain” meta-learning. Meta-learning aims to leverage experience gained from previous tasks to solve new tasks efficiently (i.e., with better performance, little training data, and/or modest computational resources). While previous challenges in the series focused on within-domain few-shot learning problems, with the aim of learning efficiently N-way k-shot tasks (i.e., N class classification problems with k training examples), this competition challenges the participants to solve “any-way” and “any-shot” problems drawn from various domains (healthcare, ecology, biology, manufacturing, and others), chosen for their humanitarian and societal impact. To that end, we created Meta-Album, a meta-dataset of 40 image classification datasets from 10 domains, from which we carve out tasks with any number of “ways” (within the range 2-20) and any number of “shots” (within the range 1-20). The competition is with code submission, fully blind-tested on the CodaLab challenge platform. The code of the winners will be open-sourced, enabling the deployment of automated machine learning solutions for few-shot image classification across several domains. |
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Conference |
PMLR |
| Notes |
HUPBA; no menciona |
Approved |
no |
| Call Number |
Admin @ si @ CCB2022 |
Serial |
3802 |
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| Author |
Partha Pratim Roy; Umapada Pal; Josep Llados |
| Title |
Seal Object Detection in Document Images using GHT of Local Component Shapes |
Type |
Conference Article |
| Year |
2010 |
Publication |
10th ACM Symposium On Applied Computing |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
23–27 |
| Keywords |
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| Abstract |
Due to noise, overlapped text/signature and multi-oriented nature, seal (stamp) object detection involves a difficult challenge. This paper deals with automatic detection of seal from documents with cluttered background. Here, a seal object is characterized by scale and rotation invariant spatial feature descriptors (distance and angular position) computed from recognition result of individual connected components (characters). Recognition of multi-scale and multi-oriented component is done using Support Vector Machine classifier. Generalized Hough Transform (GHT) is used to detect the seal and a voting is casted for finding possible location of the seal object in a document based on these spatial feature descriptor of components pairs. The peak of votes in GHT accumulator validates the hypothesis to locate the seal object in a document. Experimental results show that, the method is efficient to locate seal instance of arbitrary shape and orientation in documents. |
| Address |
Sierre, Switzerland |
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Conference |
SAC |
| Notes |
DAG |
Approved |
no |
| Call Number |
DAG @ dag @ RPL2010a |
Serial |
1291 |
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| Author |
S.Grau; Ana Puig; Sergio Escalera; Maria Salamo |
| Title |
Intelligent Interactive Volume Classification |
Type |
Conference Article |
| Year |
2013 |
Publication |
Pacific Graphics |
Abbreviated Journal |
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| Volume |
32 |
Issue |
7 |
Pages |
23-28 |
| Keywords |
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| Abstract |
This paper defines an intelligent and interactive framework to classify multiple regions of interest from the original data on demand, without requiring any preprocessing or previous segmentation. The proposed intelligent and interactive approach is divided in three stages: visualize, training and testing. First, users visualize and label some samples directly on slices of the volume. Training and testing are based on a framework of Error Correcting Output Codes and Adaboost classifiers that learn to classify each region the user has painted. Later, at the testing stage, each classifier is directly applied on the rest of samples and combined to perform multi-class labeling, being used in the final rendering. We also parallelized the training stage using a GPU-based implementation for
obtaining a rapid interaction and classification. |
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ISBN |
978-3-905674-50-7 |
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Conference |
PG |
| Notes |
HuPBA; 600.046;MILAB |
Approved |
no |
| Call Number |
Admin @ si @ GPE2013b |
Serial |
2355 |
| Permanent link to this record |
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| Author |
Xavier Perez Sala; Laura Igual; Sergio Escalera; Cecilio Angulo |
| Title |
Uniform Sampling of Rotations for Discrete and Continuous Learning of 2D Shape Models |
Type |
Book Chapter |
| Year |
2012 |
Publication |
Vision Robotics: Technologies for Machine Learning and Vision Applications |
Abbreviated Journal |
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| Volume |
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Issue |
2 |
Pages |
23-42 |
| Keywords |
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| Abstract |
Different methodologies of uniform sampling over the rotation group, SO(3), for building unbiased 2D shape models from 3D objects are introduced and reviewed in this chapter. State-of-the-art non uniform sampling approaches are discussed, and uniform sampling methods using Euler angles and quaternions are introduced. Moreover, since presented work is oriented to model building applications, it is not limited to general discrete methods to obtain uniform 3D rotations, but also from a continuous point of view in the case of Procrustes Analysis. |
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IGI-Global |
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| Notes |
MILAB;HuPBA |
Approved |
no |
| Call Number |
Admin @ si @ PIE2012 |
Serial |
2064 |
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| Author |
Ariel Amato; Ivan Huerta; Mikhail Mozerov; Xavier Roca; Jordi Gonzalez |
| Title |
Moving Cast Shadows Detection Methods for Video Surveillance Applications |
Type |
Book Chapter |
| Year |
2014 |
Publication |
Augmented Vision and Reality |
Abbreviated Journal |
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| Volume |
6 |
Issue |
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Pages |
23-47 |
| Keywords |
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| Abstract |
Moving cast shadows are a major concern in today’s performance from broad range of many vision-based surveillance applications because they highly difficult the object classification task. Several shadow detection methods have been reported in the literature during the last years. They are mainly divided into two domains. One usually works with static images, whereas the second one uses image sequences, namely video content. In spite of the fact that both cases can be analogously analyzed, there is a difference in the application field. The first case, shadow detection methods can be exploited in order to obtain additional geometric and semantic cues about shape and position of its casting object (‘shape from shadows’) as well as the localization of the light source. While in the second one, the main purpose is usually change detection, scene matching or surveillance (usually in a background subtraction context). Shadows can in fact modify in a negative way the shape and color of the target object and therefore affect the performance of scene analysis and interpretation in many applications. This chapter wills mainly reviews shadow detection methods as well as their taxonomies related with the second case, thus aiming at those shadows which are associated with moving objects (moving shadows). |
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Springer Berlin Heidelberg |
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| ISSN |
2190-5916 |
ISBN |
978-3-642-37840-9 |
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| Notes |
ISE; 605.203; 600.049; 302.018; 302.012; 600.078 |
Approved |
no |
| Call Number |
Admin @ si @ AHM2014 |
Serial |
2223 |
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| Author |
Jose M. Armingol; Jorge Alfonso; Nourdine Aliane; Miguel Clavijo; Sergio Campos-Cordobes; Arturo de la Escalera; Javier del Ser; Javier Fernandez; Fernando Garcia; Felipe Jimenez; Antonio Lopez; Mario Mata |
| Title |
Environmental Perception for Intelligent Vehicles |
Type |
Book Chapter |
| Year |
2018 |
Publication |
Intelligent Vehicles. Enabling Technologies and Future Developments |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
23–101 |
| Keywords |
Computer vision; laser techniques; data fusion; advanced driver assistance systems; traffic monitoring systems; intelligent vehicles |
| Abstract |
Environmental perception represents, because of its complexity, a challenge for Intelligent Transport Systems due to the great variety of situations and different elements that can happen in road environments and that must be faced by these systems. In connection with this, so far there are a variety of solutions as regards sensors and methods, so the results of precision, complexity, cost, or computational load obtained by these works are different. In this chapter some systems based on computer vision and laser techniques are presented. Fusion methods are also introduced in order to provide advanced and reliable perception systems. |
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Conference |
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| Notes |
ADAS; 600.118 |
Approved |
no |
| Call Number |
Admin @ si @AAA2018 |
Serial |
3046 |
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| Author |
Oscar Argudo; Marc Comino; Antonio Chica; Carlos Andujar; Felipe Lumbreras |
| Title |
Segmentation of aerial images for plausible detail synthesis |
Type |
Journal Article |
| Year |
2018 |
Publication |
Computers & Graphics |
Abbreviated Journal |
CG |
| Volume |
71 |
Issue |
|
Pages |
23-34 |
| Keywords |
Terrain editing; Detail synthesis; Vegetation synthesis; Terrain rendering; Image segmentation |
| Abstract |
The visual enrichment of digital terrain models with plausible synthetic detail requires the segmentation of aerial images into a suitable collection of categories. In this paper we present a complete pipeline for segmenting high-resolution aerial images into a user-defined set of categories distinguishing e.g. terrain, sand, snow, water, and different types of vegetation. This segmentation-for-synthesis problem implies that per-pixel categories must be established according to the algorithms chosen for rendering the synthetic detail. This precludes the definition of a universal set of labels and hinders the construction of large training sets. Since artists might choose to add new categories on the fly, the whole pipeline must be robust against unbalanced datasets, and fast on both training and inference. Under these constraints, we analyze the contribution of common per-pixel descriptors, and compare the performance of state-of-the-art supervised learning algorithms. We report the findings of two user studies. The first one was conducted to analyze human accuracy when manually labeling aerial images. The second user study compares detailed terrains built using different segmentation strategies, including official land cover maps. These studies demonstrate that our approach can be used to turn digital elevation models into fully-featured, detailed terrains with minimal authoring efforts. |
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| ISSN |
0097-8493 |
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| Notes |
MSIAU; 600.086; 600.118 |
Approved |
no |
| Call Number |
Admin @ si @ ACC2018 |
Serial |
3147 |
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| Author |
Sergio Escalera; Marti Soler; Stephane Ayache; Umut Guçlu; Jun Wan; Meysam Madadi; Xavier Baro; Hugo Jair Escalante; Isabelle Guyon |
| Title |
ChaLearn Looking at People: Inpainting and Denoising Challenges |
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Book Chapter |
| Year |
2019 |
Publication |
The Springer Series on Challenges in Machine Learning |
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23-44 |
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| Abstract |
Dealing with incomplete information is a well studied problem in the context of machine learning and computational intelligence. However, in the context of computer vision, the problem has only been studied in specific scenarios (e.g., certain types of occlusions in specific types of images), although it is common to have incomplete information in visual data. This chapter describes the design of an academic competition focusing on inpainting of images and video sequences that was part of the competition program of WCCI2018 and had a satellite event collocated with ECCV2018. The ChaLearn Looking at People Inpainting Challenge aimed at advancing the state of the art on visual inpainting by promoting the development of methods for recovering missing and occluded information from images and video. Three tracks were proposed in which visual inpainting might be helpful but still challenging: human body pose estimation, text overlays removal and fingerprint denoising. This chapter describes the design of the challenge, which includes the release of three novel datasets, and the description of evaluation metrics, baselines and evaluation protocol. The results of the challenge are analyzed and discussed in detail and conclusions derived from this event are outlined. |
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| Notes |
HuPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ ESA2019 |
Serial |
3327 |
| Permanent link to this record |
