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Author Utkarsh Porwal; Alicia Fornes; Faisal Shafait (eds)
Title Frontiers in Handwriting Recognition. International Conference on Frontiers in Handwriting Recognition. 18th International Conference, ICFHR 2022 Type Book Whole
Year 2022 Publication Frontiers in Handwriting Recognition. Abbreviated Journal
Volume 13639 Issue (up) Pages
Keywords
Abstract
Address ICFHR 2022, Hyderabad, India, December 4–7, 2022
Corporate Author Thesis
Publisher Springer Place of Publication Editor Utkarsh Porwal; Alicia Fornes; Faisal Shafait
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN 978-3-031-21648-0 Medium
Area Expedition Conference ICFHR
Notes DAG Approved no
Call Number Admin @ si @ PFS2022 Serial 3809
Permanent link to this record
 
 
Author Jorge Charco; Angel Sappa; Boris X. Vintimilla; Henry Velesaca
Title Human Body Pose Estimation in Multi-view Environments Type Book Chapter
Year 2022 Publication ICT Applications for Smart Cities. Intelligent Systems Reference Library Abbreviated Journal
Volume 224 Issue (up) Pages 79-99
Keywords
Abstract This chapter tackles the challenging problem of human pose estimation in multi-view environments to handle scenes with self-occlusions. The proposed approach starts by first estimating the camera pose—extrinsic parameters—in multi-view scenarios; due to few real image datasets, different virtual scenes are generated by using a special simulator, for training and testing the proposed convolutional neural network based approaches. Then, these extrinsic parameters are used to establish the relation between different cameras into the multi-view scheme, which captures the pose of the person from different points of view at the same time. The proposed multi-view scheme allows to robustly estimate human body joints’ position even in situations where they are occluded. This would help to avoid possible false alarms in behavioral analysis systems of smart cities, as well as applications for physical therapy, safe moving assistance for the elderly among other. The chapter concludes by presenting experimental results in real scenes by using state-of-the-art and the proposed multi-view approaches.
Address September 2022
Corporate Author Thesis
Publisher Springer Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title ISRL
Series Volume Series Issue Edition
ISSN ISBN 978-3-031-06306-0 Medium
Area Expedition Conference
Notes MSIAU; MACO Approved no
Call Number Admin @ si @ CSV2022b Serial 3810
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Author Henry Velesaca; Patricia Suarez; Dario Carpio; Rafael E. Rivadeneira; Angel Sanchez; Angel Morera
Title Video Analytics in Urban Environments: Challenges and Approaches Type Book Chapter
Year 2022 Publication ICT Applications for Smart Cities Abbreviated Journal
Volume 224 Issue (up) Pages 101-121
Keywords
Abstract This chapter reviews state-of-the-art approaches generally present in the pipeline of video analytics on urban scenarios. A typical pipeline is used to cluster approaches in the literature, including image preprocessing, object detection, object classification, and object tracking modules. Then, a review of recent approaches for each module is given. Additionally, applications and datasets generally used for training and evaluating the performance of these approaches are included. This chapter does not pretend to be an exhaustive review of state-of-the-art video analytics in urban environments but rather an illustration of some of the different recent contributions. The chapter concludes by presenting current trends in video analytics in the urban scenario field.
Address September 2022
Corporate Author Thesis
Publisher Springer Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title ISRL
Series Volume Series Issue Edition
ISSN ISBN 978-3-031-06306-0 Medium
Area Expedition Conference
Notes MSIAU; MACO Approved no
Call Number Admin @ si @ VSC2022 Serial 3811
Permanent link to this record
 
 
Author Guillermo Torres; Debora Gil; Antoni Rosell; S. Mena; Carles Sanchez
Title Virtual Radiomics Biopsy for the Histological Diagnosis of Pulmonary Nodules – Intermediate Results of the RadioLung Project Type Journal Article
Year 2023 Publication International Journal of Computer Assisted Radiology and Surgery Abbreviated Journal IJCARS
Volume Issue (up) Pages
Keywords
Abstract
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes IAM Approved no
Call Number Admin @ si @ TGM2023 Serial 3830
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Author Angel Sappa (ed)
Title ICT Applications for Smart Cities Type Book Whole
Year 2022 Publication ICT Applications for Smart Cities Abbreviated Journal
Volume 224 Issue (up) Pages
Keywords Computational Intelligence; Intelligent Systems; Smart Cities; ICT Applications; Machine Learning; Pattern Recognition; Computer Vision; Image Processing
Abstract Part of the book series: Intelligent Systems Reference Library (ISRL)

This book is the result of four-year work in the framework of the Ibero-American Research Network TICs4CI funded by the CYTED program. In the following decades, 85% of the world's population is expected to live in cities; hence, urban centers should be prepared to provide smart solutions for problems ranging from video surveillance and intelligent mobility to the solid waste recycling processes, just to mention a few. More specifically, the book describes underlying technologies and practical implementations of several successful case studies of ICTs developed in the following smart city areas:

• Urban environment monitoring
• Intelligent mobility
• Waste recycling processes
• Video surveillance
• Computer-aided diagnose in healthcare systems
• Computer vision-based approaches for efficiency in production processes

The book is intended for researchers and engineers in the field of ICTs for smart cities, as well as to anyone who wants to know about state-of-the-art approaches and challenges on this field.
Address September 2022
Corporate Author Thesis
Publisher Springer Place of Publication Editor Angel Sappa
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title ISRL
Series Volume Series Issue Edition
ISSN ISBN 978-3-031-06306-0 Medium
Area Expedition Conference
Notes MSIAU; MACO Approved no
Call Number Admin @ si @ Sap2022 Serial 3812
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Author Victoria Ruiz; Angel Sanchez; Jose F. Velez; Bogdan Raducanu
Title Waste Classification with Small Datasets and Limited Resources Type Book Chapter
Year 2022 Publication ICT Applications for Smart Cities. Intelligent Systems Reference Library Abbreviated Journal
Volume 224 Issue (up) Pages 185-203
Keywords
Abstract Automatic waste recycling has become a very important societal challenge nowadays, raising people’s awareness for a cleaner environment and a more sustainable lifestyle. With the transition to Smart Cities, and thanks to advanced ICT solutions, this problem has received a new impulse. The waste recycling focus has shifted from general waste treating facilities to an individual responsibility, where each person should become aware of selective waste separation. The surge of the mobile devices, accompanied by a significant increase in computation power, has potentiated and facilitated this individual role. An automated image-based waste classification mechanism can help with a more efficient recycling and a reduction of contamination from residuals. Despite the good results achieved with the deep learning methodologies for this task, the Achille’s heel is that they require large neural networks which need significant computational resources for training and therefore are not suitable for mobile devices. To circumvent this apparently intractable problem, we will rely on knowledge distillation in order to transfer the network’s knowledge from a larger network (called ‘teacher’) to a smaller, more compact one, (referred as ‘student’) and thus making it possible the task of image classification on a device with limited resources. For evaluation, we considered as ‘teachers’ large architectures such as InceptionResNet or DenseNet and as ‘students’, several configurations of the MobileNets. We used the publicly available TrashNet dataset to demonstrate that the distillation process does not significantly affect system’s performance (e.g. classification accuracy) of the student network.
Address September 2022
Corporate Author Thesis
Publisher Springer Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title ISRL
Series Volume Series Issue Edition
ISSN ISBN 978-3-031-06306-0 Medium
Area Expedition Conference
Notes LAMP Approved no
Call Number Admin @ si @ Serial 3813
Permanent link to this record
 
 
Author Shiqi Yang; Yaxing Wang; Kai Wang; Shangling Jui; Joost Van de Weijer
Title Local Prediction Aggregation: A Frustratingly Easy Source-free Domain Adaptation Method Type Miscellaneous
Year 2022 Publication Arxiv Abbreviated Journal
Volume Issue (up) Pages
Keywords
Abstract We propose a simple but effective source-free domain adaptation (SFDA) method. Treating SFDA as an unsupervised clustering problem and following the intuition that local neighbors in feature space should have more similar predictions than other features, we propose to optimize an objective of prediction consistency. This objective encourages local neighborhood features in feature space to have similar predictions while features farther away in feature space have dissimilar predictions, leading to efficient feature clustering and cluster assignment simultaneously. For efficient training, we seek to optimize an upper-bound of the objective resulting in two simple terms. Furthermore, we relate popular existing methods in domain adaptation, source-free domain adaptation and contrastive learning via the perspective of discriminability and diversity. The experimental results prove the superiority of our method, and our method can be adopted as a simple but strong baseline for future research in SFDA. Our method can be also adapted to source-free open-set and partial-set DA which further shows the generalization ability of our method. Code is available in this https URL.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes LAMP; 600.147 Approved no
Call Number Admin @ si @ YWW2022b Serial 3815
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Author Asma Bensalah; Antonio Parziale; Giuseppe De Gregorio; Angelo Marcelli; Alicia Fornes; Josep Llados
Title I Can’t Believe It’s Not Better: In-air Movement for Alzheimer Handwriting Synthetic Generation Type Conference Article
Year 2023 Publication 21st International Graphonomics Conference Abbreviated Journal
Volume Issue (up) Pages 136–148
Keywords
Abstract During recent years, there here has been a boom in terms of deep learning use for handwriting analysis and recognition. One main application for handwriting analysis is early detection and diagnosis in the health field. Unfortunately, most real case problems still suffer a scarcity of data, which makes difficult the use of deep learning-based models. To alleviate this problem, some works resort to synthetic data generation. Lately, more works are directed towards guided data synthetic generation, a generation that uses the domain and data knowledge to generate realistic data that can be useful to train deep learning models. In this work, we combine the domain knowledge about the Alzheimer’s disease for handwriting and use it for a more guided data generation. Concretely, we have explored the use of in-air movements for synthetic data generation.
Address Evora; Portugal; October 2023
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference IGS
Notes DAG Approved no
Call Number Admin @ si @ BPG2023 Serial 3838
Permanent link to this record
 
 
Author Ali Furkan Biten; Ruben Tito; Lluis Gomez; Ernest Valveny; Dimosthenis Karatzas
Title OCR-IDL: OCR Annotations for Industry Document Library Dataset Type Conference Article
Year 2022 Publication ECCV Workshop on Text in Everything Abbreviated Journal
Volume Issue (up) Pages
Keywords
Abstract Pretraining has proven successful in Document Intelligence tasks where deluge of documents are used to pretrain the models only later to be finetuned on downstream tasks. One of the problems of the pretraining approaches is the inconsistent usage of pretraining data with different OCR engines leading to incomparable results between models. In other words, it is not obvious whether the performance gain is coming from diverse usage of amount of data and distinct OCR engines or from the proposed models. To remedy the problem, we make public the OCR annotations for IDL documents using commercial OCR engine given their superior performance over open source OCR models. The contributed dataset (OCR-IDL) has an estimated monetary value over 20K US$. It is our hope that OCR-IDL can be a starting point for future works on Document Intelligence. All of our data and its collection process with the annotations can be found in this https URL.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ECCV
Notes DAG; no proj Approved no
Call Number Admin @ si @ BTG2022 Serial 3817
Permanent link to this record
 
 
Author Shiqi Yang; Yaxing Wang; Kai Wang; Shangling Jui; Joost Van de Weijer
Title One Ring to Bring Them All: Towards Open-Set Recognition under Domain Shift Type Miscellaneous
Year 2022 Publication Arxiv Abbreviated Journal
Volume Issue (up) Pages
Keywords
Abstract In this paper, we investigate model adaptation under domain and category shift, where the final goal is to achieve
(SF-UNDA), which addresses the situation where there exist both domain and category shifts between source and target domains. Under the SF-UNDA setting, the model cannot access source data anymore during target adaptation, which aims to address data privacy concerns. We propose a novel training scheme to learn a (
+1)-way classifier to predict the
source classes and the unknown class, where samples of only known source categories are available for training. Furthermore, for target adaptation, we simply adopt a weighted entropy minimization to adapt the source pretrained model to the unlabeled target domain without source data. In experiments, we show:
After source training, the resulting source model can get excellent performance for
;
After target adaptation, our method surpasses current UNDA approaches which demand source data during adaptation. The versatility to several different tasks strongly proves the efficacy and generalization ability of our method.
When augmented with a closed-set domain adaptation approach during target adaptation, our source-free method further outperforms the current state-of-the-art UNDA method by 2.5%, 7.2% and 13% on Office-31, Office-Home and VisDA respectively.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes LAMP; no proj Approved no
Call Number Admin @ si @ YWW2022c Serial 3818
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Author Saiping Zhang, Luis Herranz, Marta Mrak, Marc Gorriz Blanch, Shuai Wan, Fuzheng Yang
Title PeQuENet: Perceptual Quality Enhancement of Compressed Video with Adaptation-and Attention-based Network Type Miscellaneous
Year 2022 Publication Arxiv Abbreviated Journal
Volume Issue (up) Pages
Keywords
Abstract In this paper we propose a generative adversarial network (GAN) framework to enhance the perceptual quality of compressed videos. Our framework includes attention and adaptation to different quantization parameters (QPs) in a single model. The attention module exploits global receptive fields that can capture and align long-range correlations between consecutive frames, which can be beneficial for enhancing perceptual quality of videos. The frame to be enhanced is fed into the deep network together with its neighboring frames, and in the first stage features at different depths are extracted. Then extracted features are fed into attention blocks to explore global temporal correlations, followed by a series of upsampling and convolution layers. Finally, the resulting features are processed by the QP-conditional adaptation module which leverages the corresponding QP information. In this way, a single model can be used to enhance adaptively to various QPs without requiring multiple models specific for every QP value, while having similar performance. Experimental results demonstrate the superior performance of the proposed PeQuENet compared with the state-of-the-art compressed video quality enhancement algorithms.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes MACO; no proj Approved no
Call Number Admin @ si @ ZHM2022b Serial 3819
Permanent link to this record
 
 
Author Simone Zini; Alex Gomez-Villa; Marco Buzzelli; Bartlomiej Twardowski; Andrew D. Bagdanov; Joost Van de Weijer
Title Planckian Jitter: countering the color-crippling effects of color jitter on self-supervised training Type Conference Article
Year 2023 Publication 11th International Conference on Learning Representations Abbreviated Journal
Volume Issue (up) Pages
Keywords
Abstract Several recent works on self-supervised learning are trained by mapping different augmentations of the same image to the same feature representation. The data augmentations used are of crucial importance to the quality of learned feature representations. In this paper, we analyze how the color jitter traditionally used in data augmentation negatively impacts the quality of the color features in learned feature representations. To address this problem, we propose a more realistic, physics-based color data augmentation – which we call Planckian Jitter – that creates realistic variations in chromaticity and produces a model robust to illumination changes that can be commonly observed in real life, while maintaining the ability to discriminate image content based on color information. Experiments confirm that such a representation is complementary to the representations learned with the currently-used color jitter augmentation and that a simple concatenation leads to significant performance gains on a wide range of downstream datasets. In addition, we present a color sensitivity analysis that documents the impact of different training methods on model neurons and shows that the performance of the learned features is robust with respect to illuminant variations.
Address 1 -5 May 2023, Kigali, Ruanda
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ICLR
Notes LAMP; 600.147; 611.008; 5300006 Approved no
Call Number Admin @ si @ ZGB2023 Serial 3820
Permanent link to this record
 
 
Author Ruben Ballester; Xavier Arnal Clemente; Carles Casacuberta; Meysam Madadi; Ciprian Corneanu
Title Towards explaining the generalization gap in neural networks using topological data analysis Type Miscellaneous
Year 2022 Publication Arxiv Abbreviated Journal
Volume Issue (up) Pages
Keywords
Abstract Understanding how neural networks generalize on unseen data is crucial for designing more robust and reliable models. In this paper, we study the generalization gap of neural networks using methods from topological data analysis. For this purpose, we compute homological persistence diagrams of weighted graphs constructed from neuron activation correlations after a training phase, aiming to capture patterns that are linked to the generalization capacity of the network. We compare the usefulness of different numerical summaries from persistence diagrams and show that a combination of some of them can accurately predict and partially explain the generalization gap without the need of a test set. Evaluation on two computer vision recognition tasks (CIFAR10 and SVHN) shows competitive generalization gap prediction when compared against state-of-the-art methods.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes HUPBA; no menciona Approved no
Call Number Admin @ si @ BAC2022 Serial 3821
Permanent link to this record
 
 
Author Arya Farkhondeh; Cristina Palmero; Simone Scardapane; Sergio Escalera
Title Towards Self-Supervised Gaze Estimation Type Miscellaneous
Year 2022 Publication Arxiv Abbreviated Journal
Volume Issue (up) Pages
Keywords
Abstract Recent joint embedding-based self-supervised methods have surpassed standard supervised approaches on various image recognition tasks such as image classification. These self-supervised methods aim at maximizing agreement between features extracted from two differently transformed views of the same image, which results in learning an invariant representation with respect to appearance and geometric image transformations. However, the effectiveness of these approaches remains unclear in the context of gaze estimation, a structured regression task that requires equivariance under geometric transformations (e.g., rotations, horizontal flip). In this work, we propose SwAT, an equivariant version of the online clustering-based self-supervised approach SwAV, to learn more informative representations for gaze estimation. We demonstrate that SwAT, with ResNet-50 and supported with uncurated unlabeled face images, outperforms state-of-the-art gaze estimation methods and supervised baselines in various experiments. In particular, we achieve up to 57% and 25% improvements in cross-dataset and within-dataset evaluation tasks on existing benchmarks (ETH-XGaze, Gaze360, and MPIIFaceGaze).
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes HUPBA; no menciona Approved no
Call Number Admin @ si @ FPS2022 Serial 3822
Permanent link to this record
 
 
Author Razieh Rastgoo; Kourosh Kiani; Sergio Escalera
Title Word separation in continuous sign language using isolated signs and post-processing Type Miscellaneous
Year 2022 Publication Arxiv Abbreviated Journal
Volume Issue (up) Pages
Keywords
Abstract Continuous Sign Language Recognition (CSLR) is a long challenging task in Computer Vision due to the difficulties in detecting the explicit boundaries between the words in a sign sentence. To deal with this challenge, we propose a two-stage model. In the first stage, the predictor model, which includes a combination of CNN, SVD, and LSTM, is trained with the isolated signs. In the second stage, we apply a post-processing algorithm to the Softmax outputs obtained from the first part of the model in order to separate the isolated signs in the continuous signs. Due to the lack of a large dataset, including both the sign sequences and the corresponding isolated signs, two public datasets in Isolated Sign Language Recognition (ISLR), RKS-PERSIANSIGN and ASLVID, are used for evaluation. Results of the continuous sign videos confirm the efficiency of the proposed model to deal with isolated sign boundaries detection.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes HUPBA; no menciona Approved no
Call Number Admin @ si @ RKE2022b Serial 3824
Permanent link to this record