| Records |
| Author |
Idoia Ruiz; Joan Serrat |
| Title |
Hierarchical Novelty Detection for Traffic Sign Recognition |
Type |
Journal Article |
| Year |
2022 |
Publication |
Sensors |
Abbreviated Journal |
SENS |
Volume  |
22 |
Issue |
12 |
Pages |
4389 |
| Keywords |
Novelty detection; hierarchical classification; deep learning; traffic sign recognition; autonomous driving; computer vision |
| Abstract |
Recent works have made significant progress in novelty detection, i.e., the problem of detecting samples of novel classes, never seen during training, while classifying those that belong to known classes. However, the only information this task provides about novel samples is that they are unknown. In this work, we leverage hierarchical taxonomies of classes to provide informative outputs for samples of novel classes. We predict their closest class in the taxonomy, i.e., its parent class. We address this problem, known as hierarchical novelty detection, by proposing a novel loss, namely Hierarchical Cosine Loss that is designed to learn class prototypes along with an embedding of discriminative features consistent with the taxonomy. We apply it to traffic sign recognition, where we predict the parent class semantics for new types of traffic signs. Our model beats state-of-the art approaches on two large scale traffic sign benchmarks, Mapillary Traffic Sign Dataset (MTSD) and Tsinghua-Tencent 100K (TT100K), and performs similarly on natural images benchmarks (AWA2, CUB). For TT100K and MTSD, our approach is able to detect novel samples at the correct nodes of the hierarchy with 81% and 36% of accuracy, respectively, at 80% known class accuracy. |
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ADAS; 600.154 |
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no |
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Admin @ si @ RuS2022 |
Serial |
3684 |
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| Author |
Xavier Otazu; Xim Cerda-Company |
| Title |
The contribution of luminance and chromatic channels to color assimilation |
Type |
Journal Article |
| Year |
2022 |
Publication |
Journal of Vision |
Abbreviated Journal |
JOV |
| Volume |
22(6) |
Issue |
10 |
Pages |
1-15 |
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| Abstract |
Color induction is the phenomenon where the physical and the perceived colors of an object differ owing to the color distribution and the spatial configuration of the surrounding objects. Previous works studying this phenomenon on the lsY MacLeod–Boynton color space, show that color assimilation is present only when the magnocellular pathway (i.e., the Y axis) is activated (i.e., when there are luminance differences). Concretely, the authors showed that the effect is mainly induced by the koniocellular pathway (s axis), but not by the parvocellular pathway (l axis), suggesting that when magnocellular pathway is activated it inhibits the koniocellular pathway. In the present work, we study whether parvo-, konio-, and magnocellular pathways may influence on each other through the color induction effect. Our results show that color assimilation does not depend on a chromatic–chromatic interaction, and that chromatic assimilation is driven by the interaction between luminance and chromatic channels (mainly the magno- and the koniocellular pathways). Our results also show that chromatic induction is greatly decreased when all three visual pathways are simultaneously activated, and that chromatic pathways could influence each other through the magnocellular (luminance) pathway. In addition, we observe that chromatic channels can influence the luminance channel, hence inducing a small brightness induction. All these results show that color induction is a highly complex process where interactions between the several visual pathways are yet unknown and should be studied in greater detail. |
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Neurobit; 600.128; 600.120; 600.158 |
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no |
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Admin @ si @ OtC2022 |
Serial |
3685 |
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| Author |
Rafael E. Rivadeneira; Angel Sappa; Boris X. Vintimilla; Riad I. Hammoud |
| Title |
A Novel Domain Transfer-Based Approach for Unsupervised Thermal Image Super-Resolution |
Type |
Journal Article |
| Year |
2022 |
Publication |
Sensors |
Abbreviated Journal |
SENS |
| Volume |
22 |
Issue |
6 |
Pages |
2254 |
| Keywords |
Thermal image super-resolution; unsupervised super-resolution; thermal images; attention module; semiregistered thermal images |
| Abstract |
This paper presents a transfer domain strategy to tackle the limitations of low-resolution thermal sensors and generate higher-resolution images of reasonable quality. The proposed technique employs a CycleGAN architecture and uses a ResNet as an encoder in the generator along with an attention module and a novel loss function. The network is trained on a multi-resolution thermal image dataset acquired with three different thermal sensors. Results report better performance benchmarking results on the 2nd CVPR-PBVS-2021 thermal image super-resolution challenge than state-of-the-art methods. The code of this work is available online. |
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MSIAU; |
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no |
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Admin @ si @ RSV2022b |
Serial |
3688 |
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| Author |
Saad Minhas; Zeba Khanam; Shoaib Ehsan; Klaus McDonald Maier; Aura Hernandez-Sabate |
| Title |
Weather Classification by Utilizing Synthetic Data |
Type |
Journal Article |
| Year |
2022 |
Publication |
Sensors |
Abbreviated Journal |
SENS |
| Volume |
22 |
Issue |
9 |
Pages |
3193 |
| Keywords |
Weather classification; synthetic data; dataset; autonomous car; computer vision; advanced driver assistance systems; deep learning; intelligent transportation systems |
| Abstract |
Weather prediction from real-world images can be termed a complex task when targeting classification using neural networks. Moreover, the number of images throughout the available datasets can contain a huge amount of variance when comparing locations with the weather those images are representing. In this article, the capabilities of a custom built driver simulator are explored specifically to simulate a wide range of weather conditions. Moreover, the performance of a new synthetic dataset generated by the above simulator is also assessed. The results indicate that the use of synthetic datasets in conjunction with real-world datasets can increase the training efficiency of the CNNs by as much as 74%. The article paves a way forward to tackle the persistent problem of bias in vision-based datasets. |
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21 April 2022 |
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MDPI |
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IAM; 600.139; 600.159; 600.166; 600.145; |
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no |
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Admin @ si @ MKE2022 |
Serial |
3761 |
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| Author |
Penny Tarling; Mauricio Cantor; Albert Clapes; Sergio Escalera |
| Title |
Deep learning with self-supervision and uncertainty regularization to count fish in underwater images |
Type |
Journal Article |
| Year |
2022 |
Publication |
PloS One |
Abbreviated Journal |
Plos |
| Volume |
17 |
Issue |
5 |
Pages |
e0267759 |
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| Abstract |
Effective conservation actions require effective population monitoring. However, accurately counting animals in the wild to inform conservation decision-making is difficult. Monitoring populations through image sampling has made data collection cheaper, wide-reaching and less intrusive but created a need to process and analyse this data efficiently. Counting animals from such data is challenging, particularly when densely packed in noisy images. Attempting this manually is slow and expensive, while traditional computer vision methods are limited in their generalisability. Deep learning is the state-of-the-art method for many computer vision tasks, but it has yet to be properly explored to count animals. To this end, we employ deep learning, with a density-based regression approach, to count fish in low-resolution sonar images. We introduce a large dataset of sonar videos, deployed to record wild Lebranche mullet schools (Mugil liza), with a subset of 500 labelled images. We utilise abundant unlabelled data in a self-supervised task to improve the supervised counting task. For the first time in this context, by introducing uncertainty quantification, we improve model training and provide an accompanying measure of prediction uncertainty for more informed biological decision-making. Finally, we demonstrate the generalisability of our proposed counting framework through testing it on a recent benchmark dataset of high-resolution annotated underwater images from varying habitats (DeepFish). From experiments on both contrasting datasets, we demonstrate our network outperforms the few other deep learning models implemented for solving this task. By providing an open-source framework along with training data, our study puts forth an efficient deep learning template for crowd counting aquatic animals thereby contributing effective methods to assess natural populations from the ever-increasing visual data. |
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Public Library of Science |
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HuPBA |
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no |
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Admin @ si @ TCC2022 |
Serial |
3743 |
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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 |
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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 |
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HuPBA |
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no |
| Call Number |
Admin @ si @ LZY2022 |
Serial |
3778 |
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| Author |
Juan Borrego-Carazo; Carles Sanchez; David Castells; Jordi Carrabina; Debora Gil |
| Title |
A benchmark for the evaluation of computational methods for bronchoscopic navigation |
Type |
Journal Article |
| Year |
2022 |
Publication |
International Journal of Computer Assisted Radiology and Surgery |
Abbreviated Journal |
IJCARS |
| Volume |
17 |
Issue |
1 |
Pages |
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IAM |
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no |
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Admin @ si @ BSC2022 |
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3832 |
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| Author |
Antoni Rosell; Sonia Baeza; S. Garcia-Reina; JL. Mate; Ignasi Guasch; I. Nogueira; I. Garcia-Olive; Guillermo Torres; Carles Sanchez; Debora Gil |
| Title |
EP01.05-001 Radiomics to Increase the Effectiveness of Lung Cancer Screening Programs. Radiolung Preliminary Results |
Type |
Journal Article |
| Year |
2022 |
Publication |
Journal of Thoracic Oncology |
Abbreviated Journal |
JTO |
| Volume |
17 |
Issue |
9 |
Pages |
S182 |
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IAM |
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no |
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Admin @ si @ RBG2022b |
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3834 |
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| Author |
Meysam Madadi; Sergio Escalera; Xavier Baro; Jordi Gonzalez |
| Title |
End-to-end Global to Local CNN Learning for Hand Pose Recovery in Depth data |
Type |
Journal Article |
| Year |
2022 |
Publication |
IET Computer Vision |
Abbreviated Journal |
IETCV |
| Volume |
16 |
Issue |
1 |
Pages |
50-66 |
| Keywords |
Computer vision; data acquisition; human computer interaction; learning (artificial intelligence); pose estimation |
| Abstract |
Despite recent advances in 3D pose estimation of human hands, especially thanks to the advent of CNNs and depth cameras, this task is still far from being solved. This is mainly due to the highly non-linear dynamics of fingers, which make hand model training a challenging task. In this paper, we exploit a novel hierarchical tree-like structured CNN, in which branches are trained to become specialized in predefined subsets of hand joints, called local poses. We further fuse local pose features, extracted from hierarchical CNN branches, to learn higher order dependencies among joints in the final pose by end-to-end training. Lastly, the loss function used is also defined to incorporate appearance and physical constraints about doable hand motion and deformation. Finally, we introduce a non-rigid data augmentation approach to increase the amount of training depth data. Experimental results suggest that feeding a tree-shaped CNN, specialized in local poses, into a fusion network for modeling joints correlations and dependencies, helps to increase the precision of final estimations, outperforming state-of-the-art results on NYU and SyntheticHand datasets. |
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HUPBA; ISE; 600.098; 600.119 |
Approved |
no |
| Call Number |
Admin @ si @ MEB2022 |
Serial |
3652 |
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Hugo Jair Escalante; Heysem Kaya; Albert Ali Salah; Sergio Escalera; Yagmur Gucluturk; Umut Guçlu; Xavier Baro; Isabelle Guyon; Julio C. S. Jacques Junior; Meysam Madadi; Stephane Ayache; Evelyne Viegas; Furkan Gurpinar; Achmadnoer Sukma Wicaksana; Cynthia Liem; Marcel A. J. Van Gerven; Rob Van Lier |
| Title |
Modeling, Recognizing, and Explaining Apparent Personality from Videos |
Type |
Journal Article |
| Year |
2022 |
Publication |
IEEE Transactions on Affective Computing |
Abbreviated Journal |
TAC |
| Volume |
13 |
Issue |
2 |
Pages |
894-911 |
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| Abstract |
Explainability and interpretability are two critical aspects of decision support systems. Despite their importance, it is only recently that researchers are starting to explore these aspects. This paper provides an introduction to explainability and interpretability in the context of apparent personality recognition. To the best of our knowledge, this is the first effort in this direction. We describe a challenge we organized on explainability in first impressions analysis from video. We analyze in detail the newly introduced data set, evaluation protocol, proposed solutions and summarize the results of the challenge. We investigate the issue of bias in detail. Finally, derived from our study, we outline research opportunities that we foresee will be relevant in this area in the near future. |
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1 April-June 2022 |
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HuPBA; no menciona |
Approved |
no |
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Admin @ si @ EKS2022 |
Serial |
3406 |
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| Author |
Razieh Rastgoo; Kourosh Kiani; Sergio Escalera |
| Title |
Real-time Isolated Hand Sign Language RecognitioN Using Deep Networks and SVD |
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Journal |
| Year |
2022 |
Publication |
Journal of Ambient Intelligence and Humanized Computing |
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13 |
Issue |
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Pages |
591–611 |
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One of the challenges in computer vision models, especially sign language, is real-time recognition. In this work, we present a simple yet low-complex and efficient model, comprising single shot detector, 2D convolutional neural network, singular value decomposition (SVD), and long short term memory, to real-time isolated hand sign language recognition (IHSLR) from RGB video. We employ the SVD method as an efficient, compact, and discriminative feature extractor from the estimated 3D hand keypoints coordinators. Despite the previous works that employ the estimated 3D hand keypoints coordinates as raw features, we propose a novel and revolutionary way to apply the SVD to the estimated 3D hand keypoints coordinates to get more discriminative features. SVD method is also applied to the geometric relations between the consecutive segments of each finger in each hand and also the angles between these sections. We perform a detailed analysis of recognition time and accuracy. One of our contributions is that this is the first time that the SVD method is applied to the hand pose parameters. Results on four datasets, RKS-PERSIANSIGN (99.5±0.04), First-Person (91±0.06), ASVID (93±0.05), and isoGD (86.1±0.04), confirm the efficiency of our method in both accuracy (mean+std) and time recognition. Furthermore, our model outperforms or gets competitive results with the state-of-the-art alternatives in IHSLR and hand action recognition. |
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HUPBA; no proj |
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no |
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Admin @ si @ RKE2022a |
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3660 |
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Julio C. S. Jacques Junior; Yagmur Gucluturk; Marc Perez; Umut Guçlu; Carlos Andujar; Xavier Baro; Hugo Jair Escalante; Isabelle Guyon; Marcel A. J. van Gerven; Rob van Lier; Sergio Escalera |
| Title |
First Impressions: A Survey on Vision-Based Apparent Personality Trait Analysis |
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Journal Article |
| Year |
2022 |
Publication |
IEEE Transactions on Affective Computing |
Abbreviated Journal |
TAC |
| Volume |
13 |
Issue |
1 |
Pages |
75-95 |
| Keywords |
Personality computing; first impressions; person perception; big-five; subjective bias; computer vision; machine learning; nonverbal signals; facial expression; gesture; speech analysis; multi-modal recognition |
| Abstract |
Personality analysis has been widely studied in psychology, neuropsychology, and signal processing fields, among others. From the past few years, it also became an attractive research area in visual computing. From the computational point of view, by far speech and text have been the most considered cues of information for analyzing personality. However, recently there has been an increasing interest from the computer vision community in analyzing personality from visual data. Recent computer vision approaches are able to accurately analyze human faces, body postures and behaviors, and use these information to infer apparent personality traits. Because of the overwhelming research interest in this topic, and of the potential impact that this sort of methods could have in society, we present in this paper an up-to-date review of existing vision-based approaches for apparent personality trait recognition. We describe seminal and cutting edge works on the subject, discussing and comparing their distinctive features and limitations. Future venues of research in the field are identified and discussed. Furthermore, aspects on the subjectivity in data labeling/evaluation, as well as current datasets and challenges organized to push the research on the field are reviewed. |
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1 Jan.-March 2022 |
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HuPBA |
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no |
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Admin @ si @ JGP2022 |
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3724 |
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| Author |
Guillermo Torres; Sonia Baeza; Carles Sanchez; Ignasi Guasch; Antoni Rosell; Debora Gil |
| Title |
An Intelligent Radiomic Approach for Lung Cancer Screening |
Type |
Journal Article |
| Year |
2022 |
Publication |
Applied Sciences |
Abbreviated Journal |
APPLSCI |
| Volume |
12 |
Issue |
3 |
Pages |
1568 |
| Keywords |
Lung cancer; Early diagnosis; Screening; Neural networks; Image embedding; Architecture optimization |
| Abstract |
The efficiency of lung cancer screening for reducing mortality is hindered by the high rate of false positives. Artificial intelligence applied to radiomics could help to early discard benign cases from the analysis of CT scans. The available amount of data and the fact that benign cases are a minority, constitutes a main challenge for the successful use of state of the art methods (like deep learning), which can be biased, over-fitted and lack of clinical reproducibility. We present an hybrid approach combining the potential of radiomic features to characterize nodules in CT scans and the generalization of the feed forward networks. In order to obtain maximal reproducibility with minimal training data, we propose an embedding of nodules based on the statistical significance of radiomic features for malignancy detection. This representation space of lesions is the input to a feed
forward network, which architecture and hyperparameters are optimized using own-defined metrics of the diagnostic power of the whole system. Results of the best model on an independent set of patients achieve 100% of sensitivity and 83% of specificity (AUC = 0.94) for malignancy detection. |
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Jan 2022 |
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IAM; 600.139; 600.145 |
Approved |
no |
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Admin @ si @ TBS2022 |
Serial |
3699 |
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Aura Hernandez-Sabate; Jose Elias Yauri; Pau Folch; Miquel Angel Piera; Debora Gil |
| Title |
Recognition of the Mental Workloads of Pilots in the Cockpit Using EEG Signals |
Type |
Journal Article |
| Year |
2022 |
Publication |
Applied Sciences |
Abbreviated Journal |
APPLSCI |
| Volume |
12 |
Issue |
5 |
Pages |
2298 |
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Cognitive states; Mental workload; EEG analysis; Neural networks; Multimodal data fusion |
| Abstract |
The commercial flightdeck is a naturally multi-tasking work environment, one in which interruptions are frequent come in various forms, contributing in many cases to aviation incident reports. Automatic characterization of pilots’ workloads is essential to preventing these kind of incidents. In addition, minimizing the physiological sensor network as much as possible remains both a challenge and a requirement. Electroencephalogram (EEG) signals have shown high correlations with specific cognitive and mental states, such as workload. However, there is not enough evidence in the literature to validate how well models generalize in cases of new subjects performing tasks with workloads similar to the ones included during the model’s training. In this paper, we propose a convolutional neural network to classify EEG features across different mental workloads in a continuous performance task test that partly measures working memory and working memory capacity. Our model is valid at the general population level and it is able to transfer task learning to pilot mental workload recognition in a simulated operational environment. |
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February 2022 |
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IAM; ADAS; 600.139; 600.145; 600.118 |
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Admin @ si @ HYF2022 |
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3720 |
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Joana Maria Pujadas-Mora; Alicia Fornes; Oriol Ramos Terrades; Josep Llados; Jialuo Chen; Miquel Valls-Figols; Anna Cabre |
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The Barcelona Historical Marriage Database and the Baix Llobregat Demographic Database. From Algorithms for Handwriting Recognition to Individual-Level Demographic and Socioeconomic Data |
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2022 |
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Historical Life Course Studies |
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HLCS |
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12 |
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99-132 |
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Individual demographic databases; Computer vision, Record linkage; Social mobility; Inequality; Migration; Word spotting; Handwriting recognition; Local censuses; Marriage Licences |
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The Barcelona Historical Marriage Database (BHMD) gathers records of the more than 600,000 marriages celebrated in the Diocese of Barcelona and their taxation registered in Barcelona Cathedral's so-called Marriage Licenses Books for the long period 1451–1905 and the BALL Demographic Database brings together the individual information recorded in the population registers, censuses and fiscal censuses of the main municipalities of the county of Baix Llobregat (Barcelona). In this ongoing collection 263,786 individual observations have been assembled, dating from the period between 1828 and 1965 by December 2020. The two databases started as part of different interdisciplinary research projects at the crossroads of Historical Demography and Computer Vision. Their construction uses artificial intelligence and computer vision methods as Handwriting Recognition to reduce the time of execution. However, its current state still requires some human intervention which explains the implemented crowdsourcing and game sourcing experiences. Moreover, knowledge graph techniques have allowed the application of advanced record linkage to link the same individuals and families across time and space. Moreover, we will discuss the main research lines using both databases developed so far in historical demography. |
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June 23, 2022 |
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DAG; 600.121; 600.162; 602.230; 600.140 |
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Admin @ si @ PFR2022 |
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3737 |
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