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Author | Angel Sappa; Fadi Dornaika; Daniel Ponsa; David Geronimo; Antonio Lopez | ||||
Title | An Efficient Approach to Onboard Stereo Vision System Pose Estimation | Type | Journal Article | ||
Year | 2008 | Publication | IEEE Transactions on Intelligent Transportation Systems | Abbreviated Journal | TITS |
Volume | 9 | Issue | 3 | Pages | 476–490 |
Keywords | Camera extrinsic parameter estimation, ground plane estimation, onboard stereo vision system | ||||
Abstract | This paper presents an efficient technique for estimating the pose of an onboard stereo vision system relative to the environment’s dominant surface area, which is supposed to be the road surface. Unlike previous approaches, it can be used either for urban or highway scenarios since it is not based on a specific visual traffic feature extraction but on 3-D raw data points. The whole process is performed in the Euclidean space and consists of two stages. Initially, a compact 2-D representation of the original 3-D data points is computed. Then, a RANdom SAmple Consensus (RANSAC) based least-squares approach is used to fit a plane to the road. Fast RANSAC fitting is obtained by selecting points according to a probability function that takes into account the density of points at a given depth. Finally, stereo camera height and pitch angle are computed related to the fitted road plane. The proposed technique is intended to be used in driverassistance systems for applications such as vehicle or pedestrian detection. Experimental results on urban environments, which are the most challenging scenarios (i.e., flat/uphill/downhill driving, speed bumps, and car’s accelerations), are presented. These results are validated with manually annotated ground truth. Additionally, comparisons with previous works are presented to show the improvements in the central processing unit processing time, as well as in the accuracy of the obtained results. | ||||
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IEEE | Place of Publication | Editor | ||
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Notes | ADAS | Approved | no | ||
Call Number | ADAS @ adas @ SDP2008 | Serial | 1000 | ||
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Author | Aura Hernandez-Sabate; Monica Mitiko; Sergio Shiguemi; Debora Gil | ||||
Title | A validation protocol for assessing cardiac phase retrieval in IntraVascular UltraSound | Type | Conference Article | ||
Year | 2010 | Publication | Computing in Cardiology | Abbreviated Journal | |
Volume | 37 | Issue | Pages | 899-902 | |
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Abstract | A good reliable approach to cardiac triggering is of utmost importance in obtaining accurate quantitative results of atherosclerotic plaque burden from the analysis of IntraVascular UltraSound. Although, in the last years, there has been an increase in research of methods for retrospective gating, there is no general consensus in a validation protocol. Many methods are based on quality assessment of longitudinal cuts appearance and those reporting quantitative numbers do not follow a standard protocol. Such heterogeneity in validation protocols makes faithful comparison across methods a difficult task. We propose a validation protocol based on the variability of the retrieved cardiac phase and explore the capability of several quality measures for quantifying such variability. An ideal detector, suitable for its application in clinical practice, should produce stable phases. That is, it should always sample the same cardiac cycle fraction. In this context, one should measure the variability (variance) of a candidate sampling with respect a ground truth (reference) sampling, since the variance would indicate how spread we are aiming a target. In order to quantify the deviation between the sampling and the ground truth, we have considered two quality scores reported in the literature: signed distance to the closest reference sample and distance to the right of each reference sample. We have also considered the residuals of the regression line of reference against candidate sampling. The performance of the measures has been explored on a set of synthetic samplings covering different cardiac cycle fractions and variabilities. From our simulations, we conclude that the metrics related to distances are sensitive to the shift considered while the residuals are robust against fraction and variabilities as far as one can establish a pair-wise correspondence between candidate and reference. We will further investigate the impact of false positive and negative detections in experimental data. | ||||
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IEEE | Place of Publication | Editor | ||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | 0276-6547 | ISBN | 978-1-4244-7318-2 | Medium | |
Area | Expedition | Conference | CINC | ||
Notes | IAM; | Approved | no | ||
Call Number | IAM @ iam @ HSM2010 | Serial | 1551 | ||
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Author | Patricia Marquez; Debora Gil; Aura Hernandez-Sabate | ||||
Title | A Confidence Measure for Assessing Optical Flow Accuracy in the Absence of Ground Truth | Type | Conference Article | ||
Year | 2011 | Publication | IEEE International Conference on Computer Vision – Workshops | Abbreviated Journal | |
Volume | Issue | Pages | 2042-2049 | ||
Keywords | IEEE International Conference on Computer Vision – Workshops | ||||
Abstract | Optical flow is a valuable tool for motion analysis in autonomous navigation systems. A reliable application requires determining the accuracy of the computed optical flow. This is a main challenge given the absence of ground truth in real world sequences. This paper introduces a measure of optical flow accuracy for Lucas-Kanade based flows in terms of the numerical stability of the data-term. We call this measure optical flow condition number. A statistical analysis over ground-truth data show a good statistical correlation between the condition number and optical flow error. Experiments on driving sequences illustrate its potential for autonomous navigation systems. | ||||
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IEEE | Place of Publication | Barcelona (Spain) | Editor | |
Language | English | Summary Language | English | Original Title | |
Series Editor | Series Title | Abbreviated Series Title | |||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | ICCVW | ||
Notes | IAM; ADAS | Approved | no | ||
Call Number | IAM @ iam @ MGH2011 | Serial | 1682 | ||
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Author | Maria Salamo; Sergio Escalera | ||||
Title | Increasing Retrieval Quality in Conversational Recommenders | Type | Journal Article | ||
Year | 2011 | Publication | IEEE Transactions on Knowledge and Data Engineering | Abbreviated Journal | TKDE |
Volume | 99 | Issue | Pages | 1-1 | |
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Abstract | IF JCR CCIA 2.286 2009 24/103
JCR Impact Factor 2010: 1.851 A major task of research in conversational recommender systems is personalization. Critiquing is a common and powerful form of feedback, where a user can express her feature preferences by applying a series of directional critiques over the recommendations instead of providing specific preference values. Incremental Critiquing is a conversational recommender system that uses critiquing as a feedback to efficiently personalize products. The expectation is that in each cycle the system retrieves the products that best satisfy the user’s soft product preferences from a minimal information input. In this paper, we present a novel technique that increases retrieval quality based on a combination of compatibility and similarity scores. Under the hypothesis that a user learns Turing the recommendation process, we propose two novel exponential reinforcement learning approaches for compatibility that take into account both the instant at which the user makes a critique and the number of satisfied critiques. Moreover, we consider that the impact of features on the similarity differs according to the preferences manifested by the user. We propose a global weighting approach that uses a common weight for nearest cases in order to focus on groups of relevant products. We show that our methodology significantly improves recommendation efficiency in four data sets of different sizes in terms of session length in comparison with state-of-the-art approaches. Moreover, our recommender shows higher robustness against noisy user data when compared to classical approaches |
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IEEE | Place of Publication | Editor | ||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | 1041-4347 | ISBN | Medium | ||
Area | Expedition | Conference | |||
Notes | MILAB; HuPBA | Approved | no | ||
Call Number | Admin @ si @ SaE2011 | Serial | 1713 | ||
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Author | Albert Andaluz; Francesc Carreras; Cristina Santa Marta;Debora Gil | ||||
Title | Myocardial torsion estimation with Tagged-MRI in the OsiriX platform | Type | Conference Article | ||
Year | 2012 | Publication | ISBI Workshop on Open Source Medical Image Analysis software | Abbreviated Journal | |
Volume | Issue | Pages | |||
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Abstract | Myocardial torsion (MT) plays a crucial role in the assessment of the functionality of the
left ventricle. For this purpose, the IAM group at the CVC has developed the Harmonic Phase Flow (HPF) plugin for the Osirix DICOM platform . We have validated its funcionalty on sequences acquired using different protocols and including healthy and pathological cases. Results show similar torsion trends for SPAMM acquisitions, with pathological cases introducing expected deviations from the ground truth. Finally, we provide the plugin free of charge at http://iam.cvc.uab.es |
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Address | Barcelona, Spain | ||||
Corporate Author | Thesis | ||||
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IEEE | Place of Publication | Editor | Wiro Niessen (Erasmus MC) and Marc Modat (UCL) | |
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | ISBI | ||
Notes | IAM | Approved | no | ||
Call Number | IAM @ iam @ ACS2012 | Serial | 1900 | ||
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Author | Javier Marin; David Vazquez; Antonio Lopez; Jaume Amores; Bastian Leibe | ||||
Title | Random Forests of Local Experts for Pedestrian Detection | Type | Conference Article | ||
Year | 2013 | Publication | 15th IEEE International Conference on Computer Vision | Abbreviated Journal | |
Volume | Issue | Pages | 2592 - 2599 | ||
Keywords | ADAS; Random Forest; Pedestrian Detection | ||||
Abstract | Pedestrian detection is one of the most challenging tasks in computer vision, and has received a lot of attention in the last years. Recently, some authors have shown the advantages of using combinations of part/patch-based detectors in order to cope with the large variability of poses and the existence of partial occlusions. In this paper, we propose a pedestrian detection method that efficiently combines multiple local experts by means of a Random Forest ensemble. The proposed method works with rich block-based representations such as HOG and LBP, in such a way that the same features are reused by the multiple local experts, so that no extra computational cost is needed with respect to a holistic method. Furthermore, we demonstrate how to integrate the proposed approach with a cascaded architecture in order to achieve not only high accuracy but also an acceptable efficiency. In particular, the resulting detector operates at five frames per second using a laptop machine. We tested the proposed method with well-known challenging datasets such as Caltech, ETH, Daimler, and INRIA. The method proposed in this work consistently ranks among the top performers in all the datasets, being either the best method or having a small difference with the best one. | ||||
Address | Sydney; Australia; December 2013 | ||||
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IEEE | Place of Publication | Editor | ||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | 1550-5499 | ISBN | Medium | ||
Area | Expedition | Conference | ICCV | ||
Notes | ADAS; 600.057; 600.054 | Approved | no | ||
Call Number | ADAS @ adas @ MVL2013 | Serial | 2333 | ||
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Author | David Vazquez; Antonio Lopez; Daniel Ponsa | ||||
Title | Unsupervised Domain Adaptation of Virtual and Real Worlds for Pedestrian Detection | Type | Conference Article | ||
Year | 2012 | Publication | 21st International Conference on Pattern Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 3492 - 3495 | ||
Keywords | Pedestrian Detection; Domain Adaptation; Virtual worlds | ||||
Abstract | Vision-based object detectors are crucial for different applications. They rely on learnt object models. Ideally, we would like to deploy our vision system in the scenario where it must operate, and lead it to self-learn how to distinguish the objects of interest, i.e., without human intervention. However, the learning of each object model requires labelled samples collected through a tiresome manual process. For instance, we are interested in exploring the self-training of a pedestrian detector for driver assistance systems. Our first approach to avoid manual labelling consisted in the use of samples coming from realistic computer graphics, so that their labels are automatically available [12]. This would make possible the desired self-training of our pedestrian detector. However, as we showed in [14], between virtual and real worlds it may be a dataset shift. In order to overcome it, we propose the use of unsupervised domain adaptation techniques that avoid human intervention during the adaptation process. In particular, this paper explores the use of the transductive SVM (T-SVM) learning algorithm in order to adapt virtual and real worlds for pedestrian detection (Fig. 1). | ||||
Address | Tsukuba Science City, Japan | ||||
Corporate Author | Thesis | ||||
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IEEE | Place of Publication | Tsukuba Science City, JAPAN | Editor | |
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | 1051-4651 | ISBN | 978-1-4673-2216-4 | Medium | |
Area | Expedition | Conference | ICPR | ||
Notes | ADAS | Approved | no | ||
Call Number | ADAS @ adas @ VLP2012 | Serial | 1981 | ||
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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 | Thesis | ||||
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IEEE | Place of Publication | Editor | ||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | 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 | ||
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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 | Thesis | ||||
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IEEE | Place of Publication | Editor | ||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
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 | ||
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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 | Thesis | ||||
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IEEE | Place of Publication | Editor | ||
Language | English | Summary Language | English | Original Title | |
Series Editor | Series Title | Abbreviated Series Title | |||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | CVPRW | ||
Notes | ADAS; 600.054; 600.057; 601.217 | Approved | no | ||
Call Number | ADAS @ adas @ VXR2013a | Serial | 2219 | ||
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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 | Thesis | ||||
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IEEE | Place of Publication | Editor | ||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | 1051-4651 | ISBN | Medium | ||
Area | Expedition | Conference | ICPR | ||
Notes | ADAS; 600.057; 600.054; 601.217; 600.076 | Approved | no | ||
Call Number | ADAS @ adas @ XRV2014a | Serial | 2434 | ||
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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 | ||||
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IEEE | Place of Publication | Editor | ||
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Notes | ISE | Approved | no | ||
Call Number | Admin @ si @ BRF2022a | Serial | 3747 | ||
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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 | |
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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 | ||||
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IEEE | Place of Publication | Editor | ||
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Notes | MSIAU; MACO; 600.160; 600.167 | Approved | no | ||
Call Number | Admin @ si @ SPS2022 | Serial | 3751 | ||
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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 | ||||
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IEEE | Place of Publication | Editor | ||
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Notes | IAM; 600.166 | Approved | no | ||
Call Number | Admin @ si @ CNB2022 | Serial | 3760 | ||
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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 | |
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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. | ||||
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IEEE | Place of Publication | Editor | ||
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Notes | HuPBA | Approved | no | ||
Call Number | Admin @ si @ LZY2022 | Serial | 3778 | ||
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