| Records |
| Author |
Sergio Vera; Miguel Angel Gonzalez Ballester; Debora Gil |
| Title |
A medial map capturing the essential geometry of organs |
Type |
Conference Article |
| Year |
2012 |
Publication |
ISBI Workshop on Open Source Medical Image Analysis software |
Abbreviated Journal |
|
| Volume |
|
Issue |
|
Pages |
1691 - 1694 |
| Keywords |
Medial Surface Representation, Volume Reconstruction,Geometry , Image reconstruction , Liver , Manifolds , Shape , Surface morphology , Surface reconstruction |
| Abstract |
Medial representations are powerful tools for describing and parameterizing the volumetric shape of anatomical structures. Accurate computation of one pixel wide medial surfaces is mandatory. Those surfaces must represent faithfully the geometry of the volume. Although morphological methods produce excellent results in 2D, their complexity and quality drops across dimensions, due to a more complex description of pixel neighborhoods. This paper introduces a continuous operator for accurate and efficient computation of medial structures of arbitrary dimension. Our experiments show its higher performance for medical imaging applications in terms of simplicity of medial structures and capability for reconstructing the anatomical volume |
| Address |
Barcelona,Spain |
| Corporate Author |
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Thesis |
|
Publisher  |
IEEE |
Place of Publication |
|
Editor |
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| 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 |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
1945-7928 |
ISBN |
978-1-4577-1857-1 |
Medium |
|
| Area |
|
Expedition |
|
Conference |
ISBI |
| Notes |
IAM |
Approved |
no |
| Call Number |
IAM @ iam @ VGG2012a |
Serial |
1989 |
| Permanent link to this record |
| |
|
| |
| Author |
Jiaolong Xu; David Vazquez; Antonio Lopez; Javier Marin; Daniel Ponsa |
| Title |
Learning a Multiview Part-based Model in Virtual World for Pedestrian Detection |
Type |
Conference Article |
| Year |
2013 |
Publication |
IEEE Intelligent Vehicles Symposium |
Abbreviated Journal |
|
| Volume |
|
Issue |
|
Pages |
467 - 472 |
| Keywords |
Pedestrian Detection; Virtual World; Part based |
| Abstract |
State-of-the-art deformable part-based models based on latent SVM have shown excellent results on human detection. In this paper, we propose to train a multiview deformable part-based model with automatically generated part examples from virtual-world data. The method is efficient as: (i) the part detectors are trained with precisely extracted virtual examples, thus no latent learning is needed, (ii) the multiview pedestrian detector enhances the performance of the pedestrian root model, (iii) a top-down approach is used for part detection which reduces the searching space. We evaluate our model on Daimler and Karlsruhe Pedestrian Benchmarks with publicly available Caltech pedestrian detection evaluation framework and the result outperforms the state-of-the-art latent SVM V4.0, on both average miss rate and speed (our detector is ten times faster). |
| Address |
Gold Coast; Australia; June 2013 |
| Corporate Author |
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Thesis |
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| Publisher |
IEEE |
Place of Publication |
|
Editor |
|
| Language |
|
Summary Language |
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Original Title |
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| Series Editor |
|
Series Title |
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Abbreviated Series Title |
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| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
1931-0587 |
ISBN |
978-1-4673-2754-1 |
Medium |
|
| Area |
|
Expedition |
|
Conference |
IV |
| Notes |
ADAS; 600.054; 600.057 |
Approved |
no |
| Call Number |
XVL2013; ADAS @ adas @ xvl2013a |
Serial |
2214 |
| Permanent link to this record |
| |
|
| |
| Author |
David Vazquez; Jiaolong Xu; Sebastian Ramos; Antonio Lopez; Daniel Ponsa |
| Title |
Weakly Supervised Automatic Annotation of Pedestrian Bounding Boxes |
Type |
Conference Article |
| Year |
2013 |
Publication |
CVPR Workshop on Ground Truth – What is a good dataset? |
Abbreviated Journal |
|
| Volume |
|
Issue |
|
Pages |
706 - 711 |
| Keywords |
Pedestrian Detection; Domain Adaptation |
| Abstract |
Among the components of a pedestrian detector, its trained pedestrian classifier is crucial for achieving the desired performance. The initial task of the training process consists in collecting samples of pedestrians and background, which involves tiresome manual annotation of pedestrian bounding boxes (BBs). Thus, recent works have assessed the use of automatically collected samples from photo-realistic virtual worlds. However, learning from virtual-world samples and testing in real-world images may suffer the dataset shift problem. Accordingly, in this paper we assess an strategy to collect samples from the real world and retrain with them, thus avoiding the dataset shift, but in such a way that no BBs of real-world pedestrians have to be provided. In particular, we train a pedestrian classifier based on virtual-world samples (no human annotation required). Then, using such a classifier we collect pedestrian samples from real-world images by detection. After, a human oracle rejects the false detections efficiently (weak annotation). Finally, a new classifier is trained with the accepted detections. We show that this classifier is competitive with respect to the counterpart trained with samples collected by manually annotating hundreds of pedestrian BBs. |
| Address |
Portland; Oregon; June 2013 |
| Corporate Author |
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Thesis |
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| Publisher |
IEEE |
Place of Publication |
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Editor |
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| Language |
English |
Summary Language |
English |
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 |
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Edition |
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| ISSN |
|
ISBN |
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Medium |
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| Area |
|
Expedition |
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Conference |
CVPRW |
| Notes |
ADAS; 600.054; 600.057; 601.217 |
Approved |
no |
| Call Number |
ADAS @ adas @ VXR2013a |
Serial |
2219 |
| Permanent link to this record |
| |
|
| |
| Author |
Jiaolong Xu; Sebastian Ramos;David Vazquez; Antonio Lopez |
| Title |
Cost-sensitive Structured SVM for Multi-category Domain Adaptation |
Type |
Conference Article |
| Year |
2014 |
Publication |
22nd International Conference on Pattern Recognition |
Abbreviated Journal |
|
| Volume |
|
Issue |
|
Pages |
3886 - 3891 |
| Keywords |
Domain Adaptation; Pedestrian Detection |
| Abstract |
Domain adaptation addresses the problem of accuracy drop that a classifier may suffer when the training data (source domain) and the testing data (target domain) are drawn from different distributions. In this work, we focus on domain adaptation for structured SVM (SSVM). We propose a cost-sensitive domain adaptation method for SSVM, namely COSS-SSVM. In particular, during the re-training of an adapted classifier based on target and source data, the idea that we explore consists in introducing a non-zero cost even for correctly classified source domain samples. Eventually, we aim to learn a more targetoriented classifier by not rewarding (zero loss) properly classified source-domain training samples. We assess the effectiveness of COSS-SSVM on multi-category object recognition. |
| Address |
Stockholm; Sweden; August 2014 |
| Corporate Author |
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Thesis |
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| Publisher |
IEEE |
Place of Publication |
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Editor |
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| Language |
|
Summary Language |
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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 |
1051-4651 |
ISBN |
|
Medium |
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| Area |
|
Expedition |
|
Conference |
ICPR |
| Notes |
ADAS; 600.057; 600.054; 601.217; 600.076 |
Approved |
no |
| Call Number |
ADAS @ adas @ XRV2014a |
Serial |
2434 |
| Permanent link to this record |
| |
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| |
| Author |
Parichehr Behjati Ardakani; Pau Rodriguez; Carles Fernandez; Armin Mehri; Xavier Roca; Seiichi Ozawa; Jordi Gonzalez |
| Title |
Frequency-based Enhancement Network for Efficient Super-Resolution |
Type |
Journal Article |
| Year |
2022 |
Publication |
IEEE Access |
Abbreviated Journal |
ACCESS |
| Volume |
10 |
Issue |
|
Pages |
57383-57397 |
| Keywords |
Deep learning; Frequency-based methods; Lightweight architectures; Single image super-resolution |
| Abstract |
Recently, deep convolutional neural networks (CNNs) have provided outstanding performance in single image super-resolution (SISR). Despite their remarkable performance, the lack of high-frequency information in the recovered images remains a core problem. Moreover, as the networks increase in depth and width, deep CNN-based SR methods are faced with the challenge of computational complexity in practice. A promising and under-explored solution is to adapt the amount of compute based on the different frequency bands of the input. To this end, we present a novel Frequency-based Enhancement Block (FEB) which explicitly enhances the information of high frequencies while forwarding low-frequencies to the output. In particular, this block efficiently decomposes features into low- and high-frequency and assigns more computation to high-frequency ones. Thus, it can help the network generate more discriminative representations by explicitly recovering finer details. Our FEB design is simple and generic and can be used as a direct replacement of commonly used SR blocks with no need to change network architectures. We experimentally show that when replacing SR blocks with FEB we consistently improve the reconstruction error, while reducing the number of parameters in the model. Moreover, we propose a lightweight SR model — Frequency-based Enhancement Network (FENet) — based on FEB that matches the performance of larger models. Extensive experiments demonstrate that our proposal performs favorably against the state-of-the-art SR algorithms in terms of visual quality, memory footprint, and inference time. The code is available at https://github.com/pbehjatii/FENet |
| Address |
18 May 2022 |
| Corporate Author |
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Thesis |
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| Publisher |
IEEE |
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 |
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| Notes |
ISE |
Approved |
no |
| Call Number |
Admin @ si @ BRF2022a |
Serial |
3747 |
| Permanent link to this record |
| |
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| |
| Author |
Xavier Soria; Gonzalo Pomboza-Junez; Angel Sappa |
| Title |
LDC: Lightweight Dense CNN for Edge Detection |
Type |
Journal Article |
| Year |
2022 |
Publication |
IEEE Access |
Abbreviated Journal |
ACCESS |
| Volume |
10 |
Issue |
|
Pages |
68281-68290 |
| Keywords |
|
| Abstract |
This paper presents a Lightweight Dense Convolutional (LDC) neural network for edge detection. The proposed model is an adaptation of two state-of-the-art approaches, but it requires less than 4% of parameters in comparison with these approaches. The proposed architecture generates thin edge maps and reaches the highest score (i.e., ODS) when compared with lightweight models (models with less than 1 million parameters), and reaches a similar performance when compare with heavy architectures (models with about 35 million parameters). Both quantitative and qualitative results and comparisons with state-of-the-art models, using different edge detection datasets, are provided. The proposed LDC does not use pre-trained weights and requires straightforward hyper-parameter settings. The source code is released at https://github.com/xavysp/LDC |
| Address |
27 June 2022 |
| Corporate Author |
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Thesis |
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| Publisher |
IEEE |
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 |
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Medium |
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| Area |
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Expedition |
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Conference |
|
| Notes |
MSIAU; MACO; 600.160; 600.167 |
Approved |
no |
| Call Number |
Admin @ si @ SPS2022 |
Serial |
3751 |
| Permanent link to this record |
| |
|
| |
| Author |
David Castells; Vinh Ngo; Juan Borrego-Carazo; Marc Codina; Carles Sanchez; Debora Gil; Jordi Carrabina |
| Title |
A Survey of FPGA-Based Vision Systems for Autonomous Cars |
Type |
Journal Article |
| Year |
2022 |
Publication |
IEEE Access |
Abbreviated Journal |
ACESS |
| Volume |
10 |
Issue |
|
Pages |
132525-132563 |
| Keywords |
Autonomous automobile; Computer vision; field programmable gate arrays; reconfigurable architectures |
| Abstract |
On the road to making self-driving cars a reality, academic and industrial researchers are working hard to continue to increase safety while meeting technical and regulatory constraints Understanding the surrounding environment is a fundamental task in self-driving cars. It requires combining complex computer vision algorithms. Although state-of-the-art algorithms achieve good accuracy, their implementations often require powerful computing platforms with high power consumption. In some cases, the processing speed does not meet real-time constraints. FPGA platforms are often used to implement a category of latency-critical algorithms that demand maximum performance and energy efficiency. Since self-driving car computer vision functions fall into this category, one could expect to see a wide adoption of FPGAs in autonomous cars. In this paper, we survey the computer vision FPGA-based works from the literature targeting automotive applications over the last decade. Based on the survey, we identify the strengths and weaknesses of FPGAs in this domain and future research opportunities and challenges. |
| Address |
16 December 2022 |
| Corporate Author |
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Thesis |
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| Publisher |
IEEE |
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 |
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| Notes |
IAM; 600.166 |
Approved |
no |
| Call Number |
Admin @ si @ CNB2022 |
Serial |
3760 |
| Permanent link to this record |
| |
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| |
| Author |
Ajian Liu; Chenxu Zhao; Zitong Yu; Jun Wan; Anyang Su; Xing Liu; Zichang Tan; Sergio Escalera; Junliang Xing; Yanyan Liang; Guodong Guo; Zhen Lei; Stan Z. Li; Shenshen Du |
| Title |
Contrastive Context-Aware Learning for 3D High-Fidelity Mask Face Presentation Attack Detection |
Type |
Journal Article |
| Year |
2022 |
Publication |
IEEE Transactions on Information Forensics and Security |
Abbreviated Journal |
TIForensicSEC |
| Volume |
17 |
Issue |
|
Pages |
2497 - 2507 |
| Keywords |
|
| Abstract |
Face presentation attack detection (PAD) is essential to secure face recognition systems primarily from high-fidelity mask attacks. Most existing 3D mask PAD benchmarks suffer from several drawbacks: 1) a limited number of mask identities, types of sensors, and a total number of videos; 2) low-fidelity quality of facial masks. Basic deep models and remote photoplethysmography (rPPG) methods achieved acceptable performance on these benchmarks but still far from the needs of practical scenarios. To bridge the gap to real-world applications, we introduce a large-scale Hi gh- Fi delity Mask dataset, namely HiFiMask . Specifically, a total amount of 54,600 videos are recorded from 75 subjects with 225 realistic masks by 7 new kinds of sensors. Along with the dataset, we propose a novel C ontrastive C ontext-aware L earning (CCL) framework. CCL is a new training methodology for supervised PAD tasks, which is able to learn by leveraging rich contexts accurately (e.g., subjects, mask material and lighting) among pairs of live faces and high-fidelity mask attacks. Extensive experimental evaluations on HiFiMask and three additional 3D mask datasets demonstrate the effectiveness of our method. The codes and dataset will be released soon. |
| Address |
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| Corporate Author |
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Thesis |
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| Publisher |
IEEE |
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 |
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| Notes |
HuPBA |
Approved |
no |
| Call Number |
Admin @ si @ LZY2022 |
Serial |
3778 |
| Permanent link to this record |
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| |
| Author |
Angel Sappa; David Geronimo; Fadi Dornaika; Antonio Lopez |
| Title |
On-board camera extrinsic parameter estimation |
Type |
Journal Article |
| Year |
2006 |
Publication |
Electronics Letters |
Abbreviated Journal |
EL |
| Volume |
42 |
Issue |
13 |
Pages |
745–746 |
| Keywords |
|
| Abstract |
An efficient technique for real-time estimation of camera extrinsic parameters is presented. It is intended to be used on on-board vision systems for driving assistance applications. The proposed technique is based on the use of a commercial stereo vision system that does not need any visual feature extraction. |
| Address |
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| Corporate Author |
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Thesis |
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| Publisher |
IEE |
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 |
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| Notes |
ADAS |
Approved |
no |
| Call Number |
ADAS @ adas @ SGD2006a |
Serial |
655 |
| Permanent link to this record |
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| |
| Author |
Jose Manuel Alvarez; Antonio Lopez |
| Title |
Photometric Invariance by Machine Learning |
Type |
Book Chapter |
| Year |
2012 |
Publication |
Color in Computer Vision: Fundamentals and Applications |
Abbreviated Journal |
|
| Volume |
7 |
Issue |
|
Pages |
113-134 |
| Keywords |
road detection |
| Abstract |
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| Address |
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| Corporate Author |
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Thesis |
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| Publisher |
iConcept Press Ltd |
Place of Publication |
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Editor |
Theo Gevers, Arjan Gijsenij, Joost van de Weijer, Jan-Mark Geusebroek |
| Language |
|
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 |
978-0-470-89084-4 |
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 @ AlL2012 |
Serial |
2186 |
| Permanent link to this record |
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| Author |
Javier Marin; David Geronimo; David Vazquez; Antonio Lopez |
| Title |
Pedestrian Detection: Exploring Virtual Worlds |
Type |
Book Chapter |
| Year |
2012 |
Publication |
Handbook of Pattern Recognition: Methods and Application |
Abbreviated Journal |
|
| Volume |
5 |
Issue |
|
Pages |
145-162 |
| Keywords |
Virtual worlds; Pedestrian Detection; Domain Adaptation |
| Abstract |
Handbook of pattern recognition will include contributions from university educators and active research experts. This Handbook is intended to serve as a basic reference on methods and applications of pattern recognition. The primary aim of this handbook is providing the community of pattern recognition with a readable, easy to understand resource that covers introductory, intermediate and advanced topics with equal clarity. Therefore, the Handbook of pattern recognition can serve equally well as reference resource and as classroom textbook. Contributions cover all methods, techniques and applications of pattern recognition. A tentative list of relevant topics might include: 1- Statistical, structural, syntactic pattern recognition. 2- Neural networks, machine learning, data mining. 3- Discrete geometry, algebraic, graph-based techniques for pattern recognition. 4- Face recognition, Signal analysis, image coding and processing, shape and texture analysis. 5- Document processing, text and graphics recognition, digital libraries. 6- Speech recognition, music analysis, multimedia systems. 7- Natural language analysis, information retrieval. 8- Biometrics, biomedical pattern analysis and information systems. 9- Other scientific, engineering, social and economical applications of pattern recognition. 10- Special hardware architectures, software packages for pattern recognition. |
| Address |
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| Corporate Author |
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Thesis |
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| Publisher |
iConcept Press |
Place of Publication |
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Editor |
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| Language |
English |
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 |
978-1-477554-82-1 |
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 |
ADAS @ adas @ MGV2012 |
Serial |
1979 |
| Permanent link to this record |
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| Author |
Josep Llados; Ernest Valveny; Gemma Sanchez; Enric Marti |
| Title |
A Case Study of Pattern Recognition: Symbol Recognition in Graphic Documentsa |
Type |
Conference Article |
| Year |
2003 |
Publication |
Proceedings of Pattern Recognition in Information Systems |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
1-13 |
| Keywords |
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| Abstract |
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| Address |
Angers, France |
| Corporate Author |
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Thesis |
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| Publisher |
ICEIS Press |
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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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
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ISBN |
972-98816-3-4 |
Medium |
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| Area |
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Expedition |
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Conference |
PRIS'03 |
| Notes |
DAG;IAM; |
Approved |
no |
| Call Number |
IAM @ iam @ LVS2003 |
Serial |
1576 |
| Permanent link to this record |
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| Author |
Josep Llados; Dorothea Blostein |
| Title |
Special Issue on Graphics Recognition |
Type |
Journal |
| Year |
2007 |
Publication |
International Journal on Document Analysis and Recognition |
Abbreviated Journal |
IJDAR |
| Volume |
9 |
Issue |
1 |
Pages |
1–2 |
| Keywords |
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Guest Editors |
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| Notes |
DAG |
Approved |
no |
| Call Number |
DAG @ dag @ LlB2007 |
Serial |
781 |
| Permanent link to this record |
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| Author |
Josep Llados; J. Lopez-Krahe; D. Archambault |
| Title |
Special Issue on Information Technologies for Visually Impaired People |
Type |
Journal |
| Year |
2007 |
Publication |
Novatica |
Abbreviated Journal |
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| Volume |
186 |
Issue |
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Pages |
4-7 |
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Guest Editors |
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| Notes |
DAG |
Approved |
no |
| Call Number |
DAG @ dag @ LLA2007a |
Serial |
903 |
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| Author |
Ana Garcia Rodriguez; Yael Tudela; Henry Cordova; S. Carballal; I. Ordas; L. Moreira; E. Vaquero; O. Ortiz; L. Rivero; F. Javier Sanchez; Miriam Cuatrecasas; Maria Pellise; Jorge Bernal; Gloria Fernandez Esparrach |
| Title |
First in Vivo Computer-Aided Diagnosis of Colorectal Polyps using White Light Endoscopy |
Type |
Journal Article |
| Year |
2022 |
Publication |
Endoscopy |
Abbreviated Journal |
END |
| Volume |
54 |
Issue |
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Pages |
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| Keywords |
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| Abstract |
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| Address |
2022/04/14 |
| Corporate Author |
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Thesis |
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| Publisher |
Georg Thieme Verlag KG |
Place of Publication |
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Summary Language |
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ISE |
Approved |
no |
| Call Number |
Admin @ si @ GTC2022a |
Serial |
3746 |
| Permanent link to this record |
