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| Author | Lei Kang; Marçal Rusiñol; Alicia Fornes; Pau Riba; Mauricio Villegas | ||||
Title  |
Unsupervised Adaptation for Synthetic-to-Real Handwritten Word Recognition | Type | Conference Article | ||
| Year | 2020 | Publication | IEEE Winter Conference on Applications of Computer Vision | Abbreviated Journal | |
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| Abstract | Handwritten Text Recognition (HTR) is still a challenging problem because it must deal with two important difficulties: the variability among writing styles, and the scarcity of labelled data. To alleviate such problems, synthetic data generation and data augmentation are typically used to train HTR systems. However, training with such data produces encouraging but still inaccurate transcriptions in real words. In this paper, we propose an unsupervised writer adaptation approach that is able to automatically adjust a generic handwritten word recognizer, fully trained with synthetic fonts, towards a new incoming writer. We have experimentally validated our proposal using five different datasets, covering several challenges (i) the document source: modern and historic samples, which may involve paper degradation problems; (ii) different handwriting styles: single and multiple writer collections; and (iii) language, which involves different character combinations. Across these challenging collections, we show that our system is able to maintain its performance, thus, it provides a practical and generic approach to deal with new document collections without requiring any expensive and tedious manual annotation step. | ||||
| Address | Aspen; Colorado; USA; March 2020 | ||||
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| Area | Expedition | Conference | WACV | ||
| Notes | DAG; 600.129; 600.140; 601.302; 601.312; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ KRF2020 | Serial | 3446 | ||
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| Author | Carlo Gatta; Adriana Romero; Joost Van de Weijer | ||||
| Title |
Unrolling loopy top-down semantic feedback in convolutional deep networks | Type | Conference Article | ||
| Year | 2014 | Publication | Workshop on Deep Vision: Deep Learning for Computer Vision | Abbreviated Journal | |
| Volume | Issue | Pages | 498-505 | ||
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| Abstract | In this paper, we propose a novel way to perform top-down semantic feedback in convolutional deep networks for efficient and accurate image parsing. We also show how to add global appearance/semantic features, which have shown to improve image parsing performance in state-of-the-art methods, and was not present in previous convolutional approaches. The proposed method is characterised by an efficient training and a sufficiently fast testing. We use the well known SIFTflow dataset to numerically show the advantages provided by our contributions, and to compare with state-of-the-art image parsing convolutional based approaches. | ||||
| Address | Columbus; Ohio; June 2014 | ||||
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| Area | Expedition | Conference | CVPRW | ||
| Notes | LAMP; MILAB; 601.160; 600.079 | Approved | no | ||
| Call Number | Admin @ si @ GRW2014 | Serial | 2490 | ||
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| Author | Mireia Sole; Joan Blanco; Debora Gil; G. Fonseka; Richard Frodsham; Oliver Valero; Francesca Vidal; Zaida Sarrate | ||||
| Title |
Unraveling the enigmas of chromosome territoriality during spermatogenesis | Type | Conference Article | ||
| Year | 2017 | Publication | IX Jornada del Departament de Biologia Cel•lular, Fisiologia i Immunologia | Abbreviated Journal | |
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| Address | UAB; Barcelona; June 2017 | ||||
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| Notes | IAM; 600.145 | Approved | no | ||
| Call Number | Admin @ si @ SBG2017b | Serial | 2959 | ||
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| Author | Soumya Jahagirdar; Minesh Mathew; Dimosthenis Karatzas; CV Jawahar | ||||
| Title |
Understanding Video Scenes Through Text: Insights from Text-Based Video Question Answering | Type | Conference Article | ||
| Year | 2023 | Publication | Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops | Abbreviated Journal | |
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| Abstract | Researchers have extensively studied the field of vision and language, discovering that both visual and textual content is crucial for understanding scenes effectively. Particularly, comprehending text in videos holds great significance, requiring both scene text understanding and temporal reasoning. This paper focuses on exploring two recently introduced datasets, NewsVideoQA and M4-ViteVQA, which aim to address video question answering based on textual content. The NewsVideoQA dataset contains question-answer pairs related to the text in news videos, while M4- ViteVQA comprises question-answer pairs from diverse categories like vlogging, traveling, and shopping. We provide an analysis of the formulation of these datasets on various levels, exploring the degree of visual understanding and multi-frame comprehension required for answering the questions. Additionally, the study includes experimentation with BERT-QA, a text-only model, which demonstrates comparable performance to the original methods on both datasets, indicating the shortcomings in the formulation of these datasets. Furthermore, we also look into the domain adaptation aspect by examining the effectiveness of training on M4-ViteVQA and evaluating on NewsVideoQA and vice-versa, thereby shedding light on the challenges and potential benefits of out-of-domain training. | ||||
| Address | Paris; France; October 2023 | ||||
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| Area | Expedition | Conference | ICCVW | ||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ JMK2023 | Serial | 3946 | ||
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| Author | Olivier Penacchio; Xavier Otazu; A. wilkins; J. Harris | ||||
| Title |
Uncomfortable images prevent lateral interactions in the cortex from providing a sparse code | Type | Conference Article | ||
| Year | 2015 | Publication | European Conference on Visual Perception ECVP2015 | Abbreviated Journal | |
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| Address | Liverpool; uk; August 2015 | ||||
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| Area | Expedition | Conference | ECVP | ||
| Notes | NEUROBIT; | Approved | no | ||
| Call Number | Admin @ si @ POW2015 | Serial | 2633 | ||
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| Author | Petia Radeva | ||||
| Title |
Uncertainty Modeling within an End-to-end Framework for Food Image Analysis | Type | Conference Article | ||
| Year | 2020 | Publication | 1st DELTA | Abbreviated Journal | |
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| Area | Expedition | Conference | DELTA | ||
| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @ Rad2020 | Serial | 3527 | ||
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| Author | Eduardo Aguilar; Bhalaji Nagarajan; Rupali Khatun; Marc Bolaños; Petia Radeva | ||||
| Title |
Uncertainty Modeling and Deep Learning Applied to Food Image Analysis | Type | Conference Article | ||
| Year | 2020 | Publication | 13th International Joint Conference on Biomedical Engineering Systems and Technologies | Abbreviated Journal | |
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| Abstract | Recently, computer vision approaches specially assisted by deep learning techniques have shown unexpected advancements that practically solve problems that never have been imagined to be automatized like face recognition or automated driving. However, food image recognition has received a little effort in the Computer Vision community. In this project, we review the field of food image analysis and focus on how to combine with two challenging research lines: deep learning and uncertainty modeling. After discussing our methodology to advance in this direction, we comment potential research, social and economic impact of the research on food image analysis. | ||||
| Address | Villetta; Malta; February 2020 | ||||
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| Area | Expedition | Conference | BIODEVICES | ||
| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @ ANK2020 | Serial | 3526 | ||
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| Author | Jaume Garcia; Debora Gil; Francesc Carreras ; Sandra Pujades; R.Leta; Xavier Alomar; Guillem Pons-LLados | ||||
| Title |
Un Model 3D del Ventricle Esquerre Integrant Anatomia i Funcionalitat | Type | Conference Article | ||
| Year | 2008 | Publication | XX Congrés de la Societat Catalana de Cardiologia, Actes del Congres | Abbreviated Journal | |
| Volume | Issue | Pages | 122 | ||
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| Abstract | Els canvis en la dinàmica del Ventricle Esquerre (VE) reflecteixen la majoria de malalties cardiovasculars . Els avenços en imatge mèdica han impulsat la recerca en models i simulacions de la dinàmica 3D del VE . La majoria dels models existents sols consideren l’anatomia externa del VE i no permeten una avaluació de l’acoblament electromecànic . Donat que la mecànica d’un muscle depèn de la orientació de les seves fibres, un model realista hauria d’incloure la disposició espacial de la banda ventricular helicoidal (BVH) .
Proposem desenvolupar un model del VE adaptat a cada pacient que integri, per primer cop, l’anatomia de la banda ventricular, l’anatomia externa del VE i la seva funcionalitat, per a una millor determinació del patró d’activació electromecànica |
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| Publisher | Place of Publication | Barcelona | Editor | ||
| Language | catalan | Summary Language | catalan | Original Title | |
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| Notes | IAM | Approved | no | ||
| Call Number | IAM @ iam @ GGC2008c | Serial | 1504 | ||
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| Author | Francisco Jose Perales; Yuhua Luo; Juan J. Villanueva | ||||
| Title |
Un metodo Automatico de Rotoscopia Sin Marcas para el Estudio del Movimiento Humano Basado en un modelo Biomecanico. | Type | Conference Article | ||
| Year | 1991 | Publication | Primer Congreso Español de Informatica Grafica | Abbreviated Journal | |
| Volume | Issue | Pages | 53-65 | ||
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| Address | Madrid | ||||
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| Notes | Approved | no | |||
| Call Number | ISE @ ise @ PLV1991 | Serial | 266 | ||
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| Author | David Lloret; Joan Serrat; Antonio Lopez; Juan J. Villanueva | ||||
| Title |
Ultrasound to MR Volume Registration for Brain Sinking Measurement | Type | Conference Article | ||
| Year | 2003 | Publication | 1rst. Iberian Conference on Pattern Recognition and Image Analysis IbPRIA 2003 | Abbreviated Journal | |
| Volume | 2652 | Issue | Pages | 420-427 | |
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| Address | Springer-Verlag | ||||
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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | IbPRIA | ||
| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ LSL2003a | Serial | 384 | ||
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| Author | Jose Seabra; F. Javier Sanchez; Francesco Ciompi; Petia Radeva | ||||
| Title |
Ultrasonographic Plaque Characterization using a Rayleigh Mixture Model | Type | Conference Article | ||
| Year | 2010 | Publication | 7th IEEE International Symposium on Biomedical Imaging | Abbreviated Journal | |
| Volume | Issue | Pages | 1–4 | ||
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| Abstract | From Nano to Macro
A correct modelling of tissue morphology is determinant for the identification of vulnerable plaques. This paper aims at describing the plaque composition by means of a Rayleigh Mixture Model applied to ultrasonic data. The effectiveness of using a mixture of distributions is established through synthetic and real ultrasonic data samples. Furthermore, the proposed mixture model is used in a plaque classification problem in Intravascular Ultrasound (IVUS) images of coronary plaques. A classifier tested on a set of 67 in-vitro plaques, yields an overall accuracy of 86% and sensitivity of 92%, 94% and 82%, for fibrotic, calcified and lipidic tissues, respectively. These results strongly suggest that different plaques types can be distinguished by means of the coefficients and Rayleigh parameters of the mixture distribution. |
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| Address | Rotterdam (Netherlands) | ||||
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| ISSN | 1945-7928 | ISBN | 978-1-4244-4125-9 | Medium | |
| Area | Expedition | Conference | ISBI | ||
| Notes | MILAB | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ SSC2010 | Serial | 1366 | ||
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| Author | Ayan Banerjee; Palaiahnakote Shivakumara; Parikshit Acharya; Umapada Pal; Josep Llados | ||||
| Title |
TWD: A New Deep E2E Model for Text Watermark Detection in Video Images | Type | Conference Article | ||
| Year | 2022 | Publication | 26th International Conference on Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Deep learning; U-Net; FCENet; Scene text detection; Video text detection; Watermark text detection | ||||
| Abstract | Text watermark detection in video images is challenging because text watermark characteristics are different from caption and scene texts in the video images. Developing a successful model for detecting text watermark, caption, and scene texts is an open challenge. This study aims at developing a new Deep End-to-End model for Text Watermark Detection (TWD), caption and scene text in video images. To standardize non-uniform contrast, quality, and resolution, we explore the U-Net3+ model for enhancing poor quality text without affecting high-quality text. Similarly, to address the challenges of arbitrary orientation, text shapes and complex background, we explore Stacked Hourglass Encoded Fourier Contour Embedding Network (SFCENet) by feeding the output of the U-Net3+ model as input. Furthermore, the proposed work integrates enhancement and detection models as an end-to-end model for detecting multi-type text in video images. To validate the proposed model, we create our own dataset (named TW-866), which provides video images containing text watermark, caption (subtitles), as well as scene text. The proposed model is also evaluated on standard natural scene text detection datasets, namely, ICDAR 2019 MLT, CTW1500, Total-Text, and DAST1500. The results show that the proposed method outperforms the existing methods. This is the first work on text watermark detection in video images to the best of our knowledge | ||||
| Address | Montreal; Quebec; Canada; August 2022 | ||||
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| Area | Expedition | Conference | ICPR | ||
| Notes | DAG; | Approved | no | ||
| Call Number | Admin @ si @ BSA2022 | Serial | 3788 | ||
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| Author | Judit Martinez; Eva Costa; P. Herreros; Antonio Lopez; Juan J. Villanueva | ||||
| Title |
TV-Screen Quality Inspection by Artificial Vision | Type | Conference Article | ||
| Year | 2003 | Publication | Proceedings SPIE 5132, Sixth International Conference on Quality Control by Artificial Vision (QCAV 2003) | Abbreviated Journal | |
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| Abstract | A real-time vision system for TV screen quality inspection is introduced. The whole system consists of eight cameras and one processor per camera. It acquires and processes 112 images in 6 seconds. The defects to be inspected can be grouped into four main categories (bubble, line-out, line reduction and landing) although there exists a large variability among each particular type of defect. The complexity of the whole inspection process has been reduced by dividing images into smaller ones and grouping the defects into frequency and intensity relevant ones. Tools such as mathematical morphology, Fourier transform, profile analysis and classification have been used. The performance of the system has been successfully proved against human operators in normal production conditions. | ||||
| Address | Gatlinburg, (EEUU) | ||||
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| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ MCH2003a | Serial | 393 | ||
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| Author | A. Auge; Javier Varona; Juan J. Villanueva | ||||
| Title |
Tumour Segmentation in Mammographies with Neural Networks. Application to Tumoural Volume Approximation. | Type | Conference Article | ||
| Year | 1997 | Publication | (SNRFAI’97) 7th Spanish National Symposium on Pattern Recognition and Image Analysis. | Abbreviated Journal | |
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| Notes | Approved | no | |||
| Call Number | ISE @ ise @ AVV1997 | Serial | 208 | ||
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| Author | Andreas Møgelmose; Chris Bahnsen; Thomas B. Moeslund; Albert Clapes; Sergio Escalera | ||||
| Title |
Tri-modal Person Re-identification with RGB, Depth and Thermal Features | Type | Conference Article | ||
| Year | 2013 | Publication | 9th IEEE Workshop on Perception beyond the visible Spectrum, Computer Vision and Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 301-307 | ||
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| Abstract | Person re-identification is about recognizing people who have passed by a sensor earlier. Previous work is mainly based on RGB data, but in this work we for the first time present a system where we combine RGB, depth, and thermal data for re-identification purposes. First, from each of the three modalities, we obtain some particular features: from RGB data, we model color information from different regions of the body, from depth data, we compute different soft body biometrics, and from thermal data, we extract local structural information. Then, the three information types are combined in a joined classifier. The tri-modal system is evaluated on a new RGB-D-T dataset, showing successful results in re-identification scenarios. | ||||
| Address | Portland; oregon; June 2013 | ||||
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| ISSN | ISBN | 978-0-7695-4990-3 | Medium | ||
| Area | Expedition | Conference | CVPRW | ||
| Notes | HUPBA;MILAB | Approved | no | ||
| Call Number | Admin @ si @ MBM2013 | Serial | 2253 | ||
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